(CLP) Programs

Appleton Greene corporate training programs are all process-driven. They are used as vehicles to implement tangible business processes within clients’ organizations, together with training, support and facilitation during the use of these processes. Corporate training programs are therefore implemented over a sustainable period of time, that is to say, between 1 year (incorporating 12 monthly workshops), and 4 years (incorporating 48 monthly workshops). Your program information guide will specify how long each program takes to complete. Each monthly workshop takes 6 hours to implement and can be undertaken either on the client’s premises, an Appleton Greene serviced office, or online via the internet. This enables clients to implement each part of their business process, before moving onto the next stage of the program and enables employees to plan their study time around their current work commitments. The result is far greater program benefit, over a more sustainable period of time and a significantly improved return on investment.

Appleton Greene uses standard and bespoke corporate training programs as vessels to transfer business process improvement knowledge into the heart of our clients’ organizations. Each individual program focuses upon the implementation of a specific business process, which enables clients to easily quantify their return on investment. There are hundreds of established Appleton Greene corporate training products now available to clients within customer services, e-business, finance, globalization, human resources, information technology, legal, management, marketing and production. It does not matter whether a client’s employees are located within one office, or an unlimited number of international offices, we can still bring them together to learn and implement specific business processes collectively. Our approach to global localization enables us to provide clients with a truly international service with that all important personal touch. Appleton Greene corporate training programs can be provided virtually or locally and they are all unique in that they individually focus upon a specific business function. All (CLP) programs are implemented over a sustainable period of time, usually between 1-4 years, incorporating 12-48 monthly workshops and professional support is consistently provided during this time by qualified learning providers and where appropriate, by Accredited Consultants.

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Executive summary

Process Optimization

History of Process Excellence

The desire for organizations to improve operational efficiency, reduce waste, and enhance customer satisfaction has driven the search for process excellence which has a rich history that spans several decades. Though it is difficult to determine a definite start date for the management discipline described as Business Process Management, related methodologies such as Total Quality Management (TQM), Lean manufacturing and Six Sigma have been developed over the decades, all of which are concerned with the design, execution, measurement, control and optimization of business processes. These methodologies focus on eliminating defects, reducing process variation, and continuously improving processes. For example, the Toyota Production System (TPS), one of the best-known examples of a Lean manufacturing system, identified seven types of waste (transportation, inventory, motion, waiting, overprocessing, overproduction and defects) and relentlessly focused on eliminating or reducing these. Over time, organizations began to realize the importance of process optimization, leading to the development of specialized tools and techniques to analyze and streamline business processes. Utilizing these approaches, many organizations have established sustainable competitive advantages. The Toyota Production System mentioned above enabled the company to produce vehicles of excellent quality efficiently and quickly (see case study below).

Figure 1: Process Optimization

However, there were some fundamental problems with these methodologies. For example, the de-facto approach for implementing one of the most popular methodologies – Lean Six Sigma (LSS) – is DMAIC (Define – Measure – Analyse – Improve – Control) outlining the five stages of a typical LSS initiative. In the Define phase, the discovery of the ‘As-Is’ process was often done manually through workshops and interviews with Subject Matter Experts, among others. However, in addition to being time and resource-intensive, the process models produced often do not accurately reflect the actual process reality. As these process models are the foundation on which improvement efforts are built, these inaccurate models comprised the integrity of the improvement efforts. Additionally, in the Measure phase, the traditional data collection techniques were typically manual, which introduced issues with data quality (e.g. measurement system errors), cost of collection and inadequate or biased sample sizes. Similar issues were encountered in the Control phase which aims to ensure that any improvements to the process are sustained over time.

A couple of complementary developments helped address the issues described above. The first was the widespread adoption of Process-Aware Information Systems (PAIS), which are designed to record accurate information about business processes in event logs in a cost-effective manner. This has provided a means to support, control and monitor operational business processes. The availability of event log data, amongst others, has enabled the development of new and novel approaches to discover, measure and optimize processes.

The second development has been the advances in the research field known as process mining. Process mining is a data-driven approach to process improvement that leverages event logs to extract valuable insights about existing processes. It originated in the early 2000s at the Eindhoven University of Technology (TuE) in the Netherlands. Process mining techniques enable organizations to visualize, analyze, and improve their processes based on real data, rather than relying solely on subjective opinions or assumptions. By uncovering bottlenecks, inefficiencies, and variations in processes, process mining helps organizations make data-driven decisions to enhance operational performance and achieve higher levels of process excellence.

Figure 2: Lean Six Sigma

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The Toyota Production System: A Case Study

The Toyota Production System (TPS) is a management philosophy developed by Toyota Motor Corporation over the course of several decades. It is a systematic approach to the elimination of waste and the continuous improvement of processes. TPS is built on the philosophy of “kaizen,” which means continuous improvement. The goal of TPS is to create a lean manufacturing system that is efficient, flexible, and responsive to customer demand.

History of the Toyota Production System:

The roots of the Toyota Production System can be traced back to the 1890s, when Sakichi Toyoda, the founder of Toyota (then known as Toyoda), invented a revolutionary automatic loom. This loom was able to stop automatically if a thread broke, preventing the production of defective fabric. This was a major breakthrough in the textile industry, and it laid the foundation for the development of TPS.

In the 1940s, Toyota began to apply the principles of the automatic loom to its automotive manufacturing operations. The company developed a system of manufacturing that was characterized by its focus on eliminating waste, improving quality, and responding quickly to customer demand. This system was later named the Toyota Production System.

The Toyota Production System was refined and developed over the years by Toyota engineers and managers, including Taiichi Ohno, who is considered to be the father of TPS. Ohno was a brilliant engineer who had a deep understanding of the manufacturing process. He was also a master of kaizen, and he constantly looked for ways to improve the TPS.

The Toyota Production System has been widely adopted by other manufacturers around the world. It is considered to be one of the most successful manufacturing methodologies ever developed. TPS has helped companies to improve quality, reduce costs, and become more competitive.

Why was the Toyota Production System required?:

The Toyota Production System was formally developed in response to the challenges that Toyota faced in the aftermath of World War II. At the time, US productivity in automobile manufacturing was eight times higher than that of their Japanese counterparts, and Toyota was short of equipment and capital. Toyota needed to find a way to raise the value-added productivity of individual workers in order to produce high-quality cars at a low cost.

The Toyota Production System was the answer. It helped Toyota to achieve its goals of high quality, low cost, and flexibility. TPS has been a major factor in Toyota’s success. The company has been able to produce high-quality cars at a low cost, and it has been able to respond quickly to changes in customer demand. TPS has also helped Toyota to build a strong reputation for quality and reliability.

What does the Toyota Production System entail?

The Toyota Production System is based on a set of principles that are designed to eliminate waste and improve efficiency. These principles include:

• Jidoka (automation with a human touch): This principle ensures that any defect in the production process should be detected immediately and corrected. This is done to prevent defective products from being produced and shipped to customers.

• Just-in-time (JIT): This principle means that only the amount of material that is needed is produced at the time it is needed. This helps to reduce inventory and waste.

• Heijunka (leveling production): This principle implies that production is leveled out so that there are no peaks and valleys in demand. This helps to improve efficiency and reduce waste.
• Kanban (visual control system): This system uses visual cues to signal when material needs to be replenished. This helps to prevent overproduction and waste.

• Kaizen (continuous improvement): This principle ensures that everyone in the organization is constantly looking for ways to improve the process. This helps to create a culture of continuous improvement.

The benefits of the Toyota Production System:

The Toyota Production System has consistently delivered several benefits, including:

• Improved quality: TPS helps to improve quality by eliminating defects and preventing them from being produced in the first place.
• Reduced costs: TPS helps to reduce costs by reducing waste and improving efficiency.
• Increased flexibility: TPS helps to increase flexibility by allowing companies to respond quickly to changes in customer demand.
• Improved employee morale: TPS helps to improve employee morale by creating a sense of ownership and responsibility for the process.
• Increased customer satisfaction: TPS helps to increase customer satisfaction by providing high-quality products that are delivered on time.

All these benefits have resulted in a significant increase in shareholder value as demonstrated by the chart below:

Figure 3: Changes in Toyota Motor Corporation’s shareholders’ equity from 1937 to 2011
(Source: toyota-global.com)

Lean Six Sigma and Toyota Production System:

Lean Six Sigma was developed in the 1980s by combining the principles of TPS with the statistical tools and problem-solving techniques of Six Sigma. Six Sigma is a quality management methodology that focuses on reducing defects to a level of no more than 3.4 defects per million opportunities (DPMO).

Lean Six Sigma combines the best of both worlds, bringing together the focus on waste elimination of TPS with the statistical rigor of Six Sigma. Below are some examples of how Lean Six Sigma has been used by some financial services and healthcare organizations to achieve significant improvements in quality, productivity, and profitability.

Financial services

Banking:

Merrill Lynch:
Merrill Lynch’s Partnering Team, which was entrusted with increasing equipment efficiency, is made up of Merrill Lynch and five key suppliers. Its goals were to lower costs, decrease rework, and increase the throughput of equipment processing. The group achieved all of its objectives as a result of a Lean Six Sigma project. Merrill Lynch strengthened its supplier relationships while achieving an annualised cost savings of $1,088,000. Another project focused on reducing the length of customer statements without affecting the customer data. The number of pages was cut by 15%, and there were considerable postage savings and increases in customer satisfaction.
(Source: isixsigma.com)

Standard Bank Group:
According to the case study provided by European independent IT research and analysis firm Bloor, the bank realised substantial aggregate savings of R438 million ($64.84 million) over a four-year period because of changes associated with Lean Six Sigma initiatives.

The Personal and Business Banking (PBB) section of Standard Bank, which offers financial services to both individuals and small- to medium-sized businesses, participated in the Lean Six Sigma programme. Approximately 34% of the Standard Bank Group’s yearly revenue comes from the PBB business.

(Source: isixsigma.com)

Insurance:

CIGNA

Four completed Lean Six Sigma initiatives brought in annualized savings of $3 million as opposed to the initial aim of $2.4 million.

(Source: isixsigma.com)

Healthcare

Hospitals:

Mayo Clinic Transplant Center
A four-and-half quality improvement program had a total cumulative return on investment (ROI) of $28.8M. Of this, $11.2 million came from operational cost reduction and efficiency benefits (net present value [NPV] approach utilized) and $17.6 million came from revenue growth models, which were ascribed to one project (Living Kidney Donor Transplant Improvement)
(Source: isixsigma.com)

Memorial Hermann Southwest Hospital:

A Lean Six Sigma project with the aim of reducing the turnover time between operations brought the rotation time down to 20 minutes (from 24 minutes). More significantly, the percentage of faults (turnovers longer than 25 minutes) has decreased from 40% to 21%. The Central Processing Department (CPD)department kept up a 40% improvement in instrument availability eleven months after the improvement. Implementing and maintaining these changes required the cooperation of the orthopaedic team in the operating room and CPD. A graph visualizing the reduction of Operation Room turnover faults is shown below.

Figure 4: Changes in Operating Room Turnover Defects After Lean Six Sigma Project
(Source: isixsigma.com)

Pharmaceutical companies:

Covance

In six months, Six Sigma, which is used by more than two thirds of the organization, generated an additional $5 million in profitability. However, the most significant benefit of Six Sigma is the reduction of process variance, which enhanced project execution, raised customer satisfaction, and promoted repeat business.
(Source: isixsigma.com)

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Current Outlook for Process Excellence and Process Mining

In today’s highly competitive business landscape, process excellence has become a strategic imperative for organizations across various industries. The relentless pursuit of operational efficiency and continuous improvement is essential for staying ahead in the market. Organizations increasingly adopt process mining as a critical tool in their process improvement initiatives. The growing availability of digital data, along with advancements in data analytics and machine learning, has made process mining more accessible and powerful than ever before.

Process mining offers several benefits in the current business environment. It provides organizations with a comprehensive view of their end-to-end processes, allowing them to identify process variations, bottlenecks, and compliance issues. By visualizing process flows and performance metrics, organizations can pinpoint areas for improvement and optimize their operations for better outcomes. Moreover, process mining can be integrated with other process improvement methodologies, such as Lean Six Sigma or Business Process Management (BPM), to create a holistic approach that combines data-driven insights with proven methodologies for process excellence.

Several factors are converging to accelerate the development and adoption of process mining. One of these is the development of commercial process mining tools such as Celonis, Apromore, Signavio, etc. All the key technology vendors have invested heavily in the development of Process Mining tools and in March 2023, Gartner (a leading provider of IT research and consulting services) launched a Process Mining Magic Quadrant).

Another factor is the close symbiotic relationship between academia and industry. This has resulted in a continuous flow of advances in process mining and monitoring research, several of which are implemented in commercial tools to deliver value to businesses.

Descriptive process mining, which aims to discover the true nature of business processes, is currently the most mature sub-discipline of process mining. Numerous organizations have utilized its process discovery algorithms to automatically model their processes, creating process analytics dashboards which have provided insight that has driven waste reduction, cost saving and defect reduction (see case studies below). Audit firms and functions have used conformance testing to reduce the time taken to check the degree of compliance of processes to standards whilst improving the quality of the outputs. Recently, a breakthrough in descriptive process mining was the development of object-centric process mining, which sheds light on the various business objects involved in a process and enables a three-dimensional view of the process.

Diagnostic process mining utilize causal inference techniques to uncover the true root causes of the problems unearthed by descriptive approaches described above. This ensures that any improvement made to the process addresses the true root cause, ensuring that these improvements are sustained.

Figure 5: Root Cause Analysis

Predictive process monitoring uses the rich history embedded in completed process instances to predict the remaining time, cost, outcome, or next activity of in-inflight process. This provides early warning of the likelihood of undesirable outcomes (e.g., late completion) and enables action to be taken to avert these outcomes.

Figure 6: Prescriptive Process Monitoring Flow

Prescriptive monitoring complements the prescriptive approaches described above and recommends the most appropriate intervention to avoid the undesired outcome of interest.

These approaches have been utilized to deliver benefits in many organizations, albeit not as widely as descriptive process mining approaches. The leading commercial vendors have implemented some of these features in their product. In addition, they are continually optimizing their products to democratize process insights, integrate this with third-party applications and make it easier for business users to derive value from process mining and monitoring tools.

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Process Mining Case Studies

Education:

University of Melbourne

The Problem:
The university identified the need to improve its student admissions process with the aim of reducing turnaround time, improve conversion rate and resource allocation.

How Process Mining Helped:
The university used process mining to analyze the student admissions process. The process mining analysis identified several bottlenecks, inefficiencies in the process and areas where the process could be improved. Process mining provided transparency by quantifying and visualizing the process in a manner that was digestible which enabled further focused process improvement.

Results Delivered:
The number of applications received has increased by 56%, and the overall turnaround time in Stage 2 of the process has decreased by 65%. The overall turnaround time has reduced by 28%
(Source:tf-pm.org)

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Public Services:

WoonFriesland

The Problem:
The Dutch housing association decided to optimize its housing allocation process to reduce the time its properties were vacant as this had the potential to ensure tenants were promptly provided accommodation as well as resulting in cost savings for the organization.

How Process Mining Helped:
The process mining analysis enabled the organization to identify the parts of the process where the most time was wasted e.g., relisting the vacant property, tenant refusal, etc. These enable the organization to effectively re-design the process to reduce or eliminate these bottlenecks. Additionally, the organization was able to obtain valuable perspectives on how to further optimize the process.

Results Delivered:
Of the 583 properties that became vacant in the six-month period, the turnaround, was, on average, 7 days shorter. This saved the organization a void period of c. 4000 days.
(Source: tf-pm.org)

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Information Technology:

Autodesk

The Problem:
As part of a critical systems migration project, Autodesk had a requirement for complete visibility of both their ERP systems which work together to process millions of orders annually. throughout the move. Financial statements would be impacted by any failure to preserve the data integrity of sales orders across the two systems throughout the migration, and regulators and auditors could think the company was losing or changing the data. In addition to the potential sanctions, this outcome would likely harm the organization’s reputation.

How Process Mining Helped:
Process Mining supported Autodesk’s system transition firstly by creating comprehensive visibility to make sure no procedure was overlooked. Second, it quickly reconciled 120,000 sales order line items every month using live data verification across the two systems. Finally, after the migration, the Autodesk team uses process mining to track performance and keep improving its processes.

Results Delivered:
The team was able to save four to six weeks throughout their system migration. In addition, the organization was able to identify fifteen critical defects in the interfaces between the old and new ERP systems because of using process mining for reconciliation, which eliminated significant customer experience impacts.
(Source: celonis.com)

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Healthcare:

IQVIA

The Problem:
The organization, a major international provider of clinical research services, realised that its Order-to-Cash and Procure-to-Pay processes would significantly benefit from optimization. For instance, several invoice’s payment terms were misclassified, resulting in a loss of savings that would have accrued from settling them early.

How Process Mining Helped:
Process Mining enabled the organisation to discover inefficiencies in their billing process and adjust it to deliver invoices to their customers quicker and get them acknowledged faster. Additionally, Process Mining enabled the organization to identify invoices with a total value of approximately $30 million with misclassified payment terms.
Finally, Process Mining facilitated granular insight into how long it took to receive payment for each of its customers, enabling it to optimize the process to receive payment for its services quicker.

Results Delivered:
The organization rapidly freed up $600,000 in working capital and saved millions of dollars across its Order-to-Cash and Procure-to-Pay processes, helping reduce the cost of its Shared Service Centre by 40% in only two years.
(Source: celonis.ocm)

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Financial Services:

Suncorp

The Problem:
The largest insurance firm in Australia and the second largest in New Zealand, Suncorp, recognised the need to better understand how claims were handled internally. The organization was particularly interested in process optimization insights to shorten the time it takes to process lengthy claims and reduce the proportion of claims completed late.

How Process Mining Helped:
Within a six-month period, the organization was able to quickly identify variations in process behaviours between on-time and late claims using automated process discovery and animation. This enabled them to implement a one-touch programme based on learnings from this initiative, which significantly sped up the time it took to process claims.

Results Delivered:
The results from the analysis enabled the organization optimize processes based on data-driven insight rather than “gut feeling”. As a result, claims processing time was reduced from a range of between 30 to 60 days to 1 to 5 days.
(Source: tf-pm.org)

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Future Outlook for Process Excellence and Process Mining

The future of process excellence and process mining holds tremendous potential for organizations seeking to thrive in a rapidly changing business landscape. As technology continues to advance, process mining is expected to become more sophisticated and capable of handling larger volumes of data from various sources. Continuous advancements in artificial intelligence and machine learning algorithms also will play a crucial role in automating the process discovery and improvement steps, enabling organizations to achieve process excellence at scale. For example, advancement in Generative AI models such as Large Language Models (LLMs) will unlock the possibility for business users to query processes using natural language e.g., “show me all high-risk cases which have bypassed four-eye check activity” or “list all cases where Activity X was directly followed by Activity Y”.

Another nascent area where future process mining development is likely to focus is object-centric process mining. Business domains where insight from relationships are critical e.g., Anti-fraud and money laundering, cybersecurity operations, etc will particularly benefit from the insights unlocked by this innovation. The focus on objects will make insights easier to digest and act o. Developments in diagnostic, predictive and prescriptive object-centric process mining and monitoring are also likely to continue at pace.

Additionally, the integration of process mining with emerging technologies such as robotic process automation (RPA), Internet of Things (IoT), and blockchain will further enhance process efficiency and transparency. For example, by combining process mining with RPA, organizations can identify repetitive and rule-based tasks that can be automated, leading to increased productivity and cost savings. Furthermore, the use of process mining in conjunction with IoT devices can enable real-time monitoring of processes, facilitating proactive decision-making and continuous process improvement.

Other factors such as reducing cost of computing power and data storage, availability of training data and competitive interests among process mining vendors is likely to lead to a breakthrough in ground-breaking research in augmented process management. This is an emerging approach to process management that combines process automation with artificial intelligence and machine learning technologies with the aim to continuously adapt and improve a business process with respect to one or more performance metrics. This will result in self-healing and adaptive processes which detect problems automatically and resolve these with little or no human intervention. This is considered the nirvana of business process management.

The future direction of process mining tools is also likely to focus on making the development of process mining artefacts, extraction of insights and action to close execution gaps easier. This is likely to increase the adoption of these tools and make them more mainstream.

In conclusion, efforts to achieve process excellence has evolved over the years, with process mining emerging as a powerful tool for achieving operational efficiency and continuous improvement. The current business landscape demands data-driven insights and a holistic approach to process optimization, and process mining fulfils these requirements. Process Mining is poised to become even more advanced and integrated with other emerging technologies, enabling organizations to unlock new levels of process excellence and maintain a competitive edge in the future.

Figure 7: Adaptive self-healing processes

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Key Corporate Objectives:

1. Enhance Process Efficiency: The primary objective of the training program will be to equip employees with the skills and knowledge to identify inefficiencies in existing processes and recommend improvements using process mining techniques.

2. Optimize Resource Utilization: Train employees to use process mining tools to analyze resource allocation and utilization, leading to better decision-making and cost optimization.

3. Improve Process Visibility: Enable participants to gain insights into end-to-end processes by visualizing and understanding the flow of activities, bottlenecks, and variations through process mining.

4. Boost Process Compliance: Train employees to identify compliance gaps and ensure adherence to regulatory requirements and internal policies through process mining insights.

5. Enhance Data-Driven Decision-Making: Foster a culture of data-driven decision-making by empowering employees to use process mining data to make informed choices and prioritize process improvements.

6. Accelerate Process Innovation: Encourage innovative thinking by using process mining as a tool to discover new process opportunities and streamline workflows.

7. Promote Continuous Improvement: Cultivate a mindset of continuous improvement among employees, using process mining insights to drive iterative enhancements to processes over time.

8. Empower Cross-Functional Collaboration: Facilitate collaboration between different departments by providing them with a common language and understanding of processes through process mining training.

9. Increase Customer Satisfaction: Use process mining insights to identify pain points in customer-facing processes and implement changes that lead to improved customer experiences.

10. Measure and Track Key Performance Indicators (KPIs): Educate participants on selecting and monitoring relevant KPIs, leveraging process mining data to track performance and measure the success of process improvements.

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Curriculum

Process Optimization – Part 1 – Year 1

1. Part 1 Month 1 Competitive Advantage
2. Part 1 Month 2 Process-Oriented Thinking
3. Part 1 Month 3 Process Architecture
4. Part 1 Month 4 Process Mining
5. Part 1 Month 5 Process-Aware Data
6. Part 1 Month 6 Descriptive Process Mining
7. Part 1 Month 7 Diagnostic Process Mining
8. Part 1 Month 8 Solution Implementation
9. Part 1 Month 9 Predictive Process Monitoring
10. Part 1 Month 10 Prescriptive Process Monitoring
11. Part 1 Month 11 Augmented Process Management
12. Part 1 Month 12 Sustainable Process Optimisation

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Program Objectives

The following list represents the Key Program Objectives (KPO) for the Appleton Greene Process Optimization corporate training program.

Process Optimization – Part 1- Year 1

1. Part 1 Month 1 Competitive Advantage – For example, organizations can streamline customer-facing activities by optimizing processes, reducing waiting times, and improving responsiveness, resulting in a smoother and more enjoyable customer experience and increasing satisfaction and loyalty. Efficient processes enable organizations to respond quickly to customer needs and preferences, delivering products or services on time and providing tailored solutions. A superior customer experience differentiates an organization from its competitors and attracts new customers through positive word-of-mouth, expanding the customer base and driving revenue growth. Employees, too, are more fulfilled due to the innovative and collaborative culture that drives continuous improvements. Employees can focus on value-adding activities by eliminating inefficiencies, streamlining workflows, and delivering better results. This increased efficiency reduces workloads, stress, and burnout, promoting a healthier work environment. Well-documented and standardized processes enable organizations to demonstrate their adherence to regulatory requirements. As a result, regulators view the organization as a trusted partner that requires light-touch oversight. Organizations can minimize errors, fraud, and non-compliance by implementing robust controls and quality assurance measures. This not only satisfies regulatory obligations but also enhances the credibility and trustworthiness of the organization. Shareholders, are delighted as improved revenue growth, operating margin, and capital efficiency increase shareholder value. The organization can reinvest cost savings achieved through process excellence in strategic initiatives, research and development, or return to shareholders as dividends. In this, the first workshop of the series, the scene is set for the data-driven process excellence journey, including why it is essential and providing an overview of the journey. The objective is to obtain the full buy-in and support of the senior management team.
2. Part 1 Month 2 Process-Oriented Thinking – Process-oriented thinking is a crucial mindset for organizations aiming to enhance efficiency, productivity, and customer satisfaction. It involves viewing operations as interconnected, interdependent processes rather than isolated tasks or business functions. The process lens also needs to span the end-to-end process, examining the possible outcomes, the expected value, and the various actors – customers, employees, suppliers, etc – who impact the process outcome. Processes are ubiquitous in every aspect of an organization, regardless of industry or function. Processes exist at all levels, from sales and marketing to operations, finance, and human resources. Process-oriented thinking recognizes the pervasive nature of business processes and emphasizes the need to analyze, optimize, and continuously improve them. It encourages organizations to break down silos and collaborate across departments, ensuring a holistic and integrated approach to achieving organizational goals. Organizations can enhance coordination, communication, and overall performance by understanding and improving cross-functional processes. Process-oriented thinking is essential for fostering a culture of continuous improvement within organizations. By adopting this mindset, organizations are better equipped to adapt to changing market dynamics, customer preferences, and technological advancements. It encourages employees to proactively identify process improvement opportunities and contribute to ongoing optimization efforts. Process-oriented thinking also supports agility and flexibility, allowing organizations to respond swiftly to evolving business needs and capitalize on emerging opportunities. In this workshop, participants will explore the ubiquitous nature of business processes, how to differentiate between core and support processes, and the role of key enablers, e.g., technology, in optimizing business processes.
3. Part 1 Month 3 Process Architecture – Process architecture refers to the systematic design and organization of processes. It facilitates insight into the organization’s various processes, including their interdependencies, inputs, outputs, and the sequence of activities involved in achieving specific outcomes. Process architecture provides a holistic view of the organization’s processes, aligning them with business objectives and facilitating improvement initiatives. Process architecture provides the foundation for enterprise process modelling by visually representing how different organisational processes interconnect and operate across multiple divisions and tiers. Organizations can understand how various processes work together to achieve their goals by developing enterprise process models based on their process architecture. Enterprise process modelling enables businesses to better coordinate their operations and workflows by visualizing process flows, identifying potential inefficiencies or gaps, and simulating process changes or improvements. It facilitates communication and collaboration among stakeholders, helping to align different teams and departments around a shared understanding of the organization’s processes. Enterprise process modelling also supports process documentation, training, and knowledge management, ensuring process knowledge is captured and communicated effectively across the organization. In this workshop, participants will learn how process architecture serves as a critical link between process excellence and enterprise process modelling, understand the benefits of enterprise process modelling, how to create (or enhance) an enterprise process model for their organisation, and prioritise their process optimization journey. They will also start formulating (or collating) the critical metrics for measuring key process outcomes to monitor how well their processes contribute to organizational goals.
4. Part 1 Month 4 Process Mining – Process mining and monitoring are two related approaches to analyzing and optimizing business processes. Both approaches involve the analysis of event data generated during process execution. They utilize data-driven techniques and algorithms to extract valuable insights from this data, such as process flow, activity durations, resource utilization, and compliance adherence, to uncover patterns, bottlenecks, and areas for improvement. However, process mining analyzes historical event data to gain insights into past process execution, understand their behavior, and drive improvement; process monitoring focuses on monitoring ongoing process executions in real-time, predicting likely outcomes, and recommending interventions to avoid undesirable outcomes. By continuously monitoring processes and identifying potential issues early on, businesses can take proactive steps to address these issues before they become more serious. This workshop will give participants a high-level overview of process mining and monitoring. They will receive a brief introduction to descriptive and diagnostic process mining as well as predictive and prescriptive process monitoring, along with the value delivered by each of these techniques – operational efficiency, process compliance and risk mitigation, continuous improvement, and customer satisfaction, among others. Participants will also obtain insight into emerging approaches such as augmented process management, which utilizes artificial intelligence and machine learning techniques to accelerate achieving desired business outcomes. Finally, they will learn about process mining and monitoring enablers such as high-quality event data, analytics tools, a culture of data-driven decision-making, organizational commitment, stakeholder engagement, and skilled resources, all of which play vital roles in the process excellence journey.
5. Part 1 Month 5 Process-Aware Data – Most of the information systems that support operations in organization are “process-aware”, meaning that the data generated by these systems can provide valuable insights into the performance and efficiency of these processes and can be used to identify areas for improvement and optimize operations. The quality and availability of this data play a critical role in the success of process mining and monitoring (i.e. the subject of workshop 4). Process-aware data, which includes event logs with detailed information about process activities, timestamps, and case identifiers, is essential for accurate and meaningful process mining analysis. It also facilitates the automation of process discovery and the timely creation of the enterprise process model (see workshop 3). Hence, this data is a key enabler for the enterprise-wide process optimization journey. However, an organization needs to address one or more of these common challenges to utilize the data for successful optimization of their processes: Data Quality: Incomplete, inconsistent, or erroneous data can lead to inaccurate process models and misleading insights. Data Integration: The need to integrate data from various sources and systems to comprehensively view the process. Privacy and Security: Adherence to data privacy regulations and implementation of appropriate security measures to protect sensitive data during process mining analysis. Scalability: Handling large volumes of data, which can pose challenges in terms of computational resources, processing speed, and storage capacity. In this workshop, participants will learn how to identify, extract, transform and load the key data required for process optimisation. They will also examine strategies for dealing with the challenges listed above.
6. Part 1 Month 6 Descriptive Process Mining – Descriptive process mining utilizes the process-aware data identified and transformed in workshop 5 to obtain insight into the actual performance of an organization’s core and supporting business processes. It involves automatically discovering process models, checking whether they conform to expectations and creating process performance analytics which enables an organization to answer questions such as “What is really going on with my process?” and “In which part of my process do rework and delays typically occur?” among others. Visualization of the actual flow of activities enables an organization to identify bottlenecks, and pinpoint areas where resources are underutilized or overburdened. This insight allows for targeted process improvement initiatives to eliminate waste, streamline operations, and enhance overall efficiency. By identifying and addressing process inefficiencies, organizations can reduce costs, improve productivity, and deliver better customer value. In addition, comparing event logs with predefined process models or rules facilitates the identification of process instances that do not adhere to business rules, which enables proactive identification of non-compliant activities, process variations, or potential instances of fraud. By addressing deviations and ensuring adherence to standard procedures, an organization can enhance compliance, minimize risks, and maintain control over their processes. Descriptive process mining facilitates understanding of how customers interact with their processes, identifying pain points, bottlenecks, and opportunities for improvement. This understanding enables an organization to design processes that align with customer needs and expectations, enhancing the overall customer experience and resulting in customer satisfaction, loyalty, and retention. Finally, this set of techniques enables an organization to identify patterns, trends, and dependencies within their processes. This knowledge allows data-informed decision-making, such as optimizing resource allocation, identifying process improvement opportunities, or prioritizing areas for investment. By leveraging process mining insights, an organization can make informed decisions that drive operational efficiency, agility, and strategic growth. In this workshop, participants will learn how to create, interpret and derive value from these descriptive process mining artifacts.
7. Part 1 Month 7 Diagnostic Process Mining – Descriptive process mining (the subject of workshop 6) will often uncover problems. However, while these techniques will provide insight into WHAT is the actual performance of business processes, they will not provide insight into WHY these problems occur. Different diagnostic process mining techniques are required to determine the causes of these problems. Problems often have multiple causes, and to eliminate or reduce a particular problem, identifying all the causes and the extent to which they contribute to the problem is essential. Finding the true causes of problems is critical because a failure to do so will likely result in the implementation of solutions that will consume time and resources but fail to resolve the problem. Diagnostic process mining utilizes causal inference techniques – statistical and analytical methods used to identify cause-and-effect relationships between variables – to obtain insight into the factors contributing to a problem of interest. These techniques aim to determine whether changes in one variable directly impact another variable. When applied to diagnostic process mining, causal inference techniques helps an organization uncover the underlying factors contributing to process inefficiencies, bottlenecks, and variations. An organization can thus target its process improvement efforts more effectively by establishing causal relationships and making data-driven decisions. In this workshop, participants will understand why diagnostic process mining is essential, how to define a problem of interest (including quantification of its impact), identify potential causal factors and estimate the causal effect of each of these factors.
8. Part 1 Month 8 Solution Implementation – Identifying, implementing and adopting appropriate solutions to resolve a problem is essential, following the insight obtained into the true causes of the problem and their respective contributors via diagnostic process mining. These solution typically involve process re-design or automation, among others. Identifying and prioritizing appropriate solutions regarding ease of implementation and impact is essential to optimizing processes. Also critical is the assessment of the impact of each solution. Organizations employ uplift modelling and simulation techniques to assess and prioritise solutions effectively. Uplift modelling (also known as treatment effect modelling or persuasion modelling) helps organizations determine the impact of interventions or treatments on desired outcomes. These techniques go beyond traditional predictive models by identifying the individuals most likely to be influenced by an intervention, thus enabling organizations to optimize resource allocation and target their efforts effectively. Simulation techniques, on the other hand, enable organizations to gain valuable insights into the impact of proposed process changes before their actual implementation. These techniques involve creating virtual models that mimic real-world processes and their interactions. By simulating process scenarios and experimenting with various parameters, organizations can analyze the potential impact of process changes on performance metrics, resource utilization, and other vital factors, providing them with a powerful tool to assess the effectiveness of process improvement solutions in a controlled environment. In this workshop, participants will learn how to assess solutions using simulation and uplift modelling to determine the most impactful solutions and techniques to drive the adoption of those solutions post-implementation.
9. Part 1 Month 9 Predictive Process Monitoring – The approaches discussed in previous workshops utilize historical data from completed cases to optimize processes. However, the approaches that will be the focus of subsequent workshops combine historic data with data from in-flight cases to provide operation support and ensure these cases are complete with the desired outcome. The first of these approaches is predictive process monitoring. As the name suggests, predictive process monitoring aims to accurately predict a process metric of interest (e.g. remaining time or cost) or the future state of the process instance (e.g. outcome or next step) by leveraging historical process data. Effectively predicting process outcomes in operational business management has the potential to enhance Customer Relationship Management by enabling organizations to manage customer expectations better. Organizations can predict customer behaviour by analyzing historical data and process flows (e.g. “Is this customer likely to accept this offer?”) and proactively address customer concerns allowing them to tailor their marketing, sales, and service strategies to individual customers, which results in more personalized interactions and enhanced customer satisfaction. Predictive process monitoring also improves Enterprise Resource Planning by providing insights into process performance, resource utilization, and supply chain management. Organizations can predict resource requirements, identify potential bottlenecks, and optimize resource allocation by analysing historical process data. This enables them to improve production planning, minimize lead times, and optimize resource utilization, resulting in cost savings and improved operational efficiency. Finally, leveraging predictive insights unlocked by predictive process monitoring facilitates operational process improvement. For example, process operators can intervene as appropriate to minimise late completion by predicting which cases are likely to complete late. Organizations can thus implement targeted process optimization strategies, reduce cycle times, minimize errors, and enhance overall operational efficiency. In this workshop, participants will learn how predictive process monitoring can provide real-time operational support, identify relevant use cases within their organization and learn how to implement these techniques.
10. Part 1 Month 10 Prescriptive Process Monitoring – In contrast to diagnostic process mining approaches (see workshop 7), which are reactive (i.e. they seek to identify the causes of problems that have already occurred), prescriptive process monitoring approaches are proactive in that they seek to determine which cases are likely to be problematic and then apply appropriate interventions to prevent the problem from occurring. Prescriptive process monitoring builds on predictive process monitoring and aims to provide actionable recommendations on the best interventions to optimize process performance and achieve desired outcomes. It combines techniques like uplift modelling to assist organizations in assessing the effectiveness of interventions as well as definitions of intervention frequency and policy to help process operators determine when to act and the appropriate intervention, respectively. Uplift modeling (introduced in workshop 8) focuses on identifying the individuals or process instances that will benefit the most from specific interventions. It enables process operators to distinguish between those who naturally improve without intervention and those who require interventions to achieve desirable results. This assists organizations in targeting their interventions more effectively and optimize costs (given that interventions typically have associated costs). Two critical concepts associated with prescriptive process monitoring are intervention frequency and policy. The intervention frequency specifies the optimal frequency of interventions to achieve the desired outcomes. It enables process operators to find the balance between excessive and insufficient interventions to improve the process. The intervention policy guides decision-making regarding intervention types, timings, and magnitude. These policies establish guidelines for determining when an intervention is necessary, what intervention is appropriate and how the process operator should implement it. Intervention policies provide a framework for consistent decision-making, enable proactive intervention, and help organizations achieve their desired process outcomes. In this workshop, participants will learn how to build prescriptive process monitoring workflows for their processes, including deciding appropriate intervention frequencies and policies. They will also learn the potential limitations of these approaches and how to measure the effectiveness of these workflows on process outcomes.
11. Part 1 Month 11 Augmented Process Management – Augmented Process Management is an emerging approach to process management that combines domain knowledge with artificial intelligence and machine learning technologies to continuously adapt and improve a business process for one or more performance metrics. With the relative maturity of process mining and recent breakthroughs in artificial intelligence (e.g., Generative AI), combining both fields has enabled the development of self-improving and adapting processes. Considering the prevalence of significant amounts of process data, the timing for developing and adopting augmented process management systems is ripe. Augmented Process Management enables organizations to determine potential process changes to optimise relevant performance measures given historical process data, the performance measures to be optimized, permissible changes, and required rules (e.g. resource allocation and decision rules). This enables the design of an autonomous system which can act independently within a defined framework, adjust its actions to improve process performance continuously and react to changes in its environment. Given the independent nature of the system, it is essential that it can interact with human agents when required (e.g., to obtain instructions to update rules or explain its decisions when required). Advancements in Artificial Intelligence (e.g Large Language Model) have made these requirements feasible. In this penultimate workshop in the series, participants learn about a Process Mining and Monitoring Maturity Framework and how to build upon the value delivered in previous workshops to build Augmented Process Management Systems. They will also learn how to derive value from these approaches and determine when their organization is ready to adopt them.
12. Part 1 Month 12 Sustainable Process Optimisation – Having built a system to ensure that processes are optimized, sustaining these improvements over time across the enterprise is essential. This will entail adopting a comprehensive approach incorporating systems thinking, continuous monitoring and evaluation at an enterprise level. Systems thinking is a crucial mindset for ensuring sustained process improvements. It involves understanding processes as interconnected systems rather than isolated components. By taking a holistic view, organizations can identify the underlying interdependencies and interactions within their processes. Systems thinking enables organizations to recognize the potential ripple effects of process changes and consider the broader implications on other areas of the organization. This approach helps avoid sub-optimization and ensures that improvements are integrated into the larger system, supporting long-term sustainability. Continuous monitoring and evaluation play a vital role in sustaining process improvements. Continuous monitoring involves real-time or near-real-time tracking of process performance metrics, allowing organizations to detect deviations, bottlenecks, or other issues as they occur. This enables timely intervention and course correction, ensuring improvements remain on track. Evaluation involves systematically assessing the impact and effectiveness of process improvements against predefined targets. Regular evaluations provide valuable insights into the success of implemented changes, identify areas for further optimization, and guide decision-making regarding adjustments or additional interventions. Continuous monitoring and evaluation enable organizations to maintain visibility and proactively address emerging challenges, thus ensuring the sustainability of process improvements. A culture of continuous improvement is a critical factor in sustaining process improvements. It encompasses shared values, attitudes, and behaviors that promote innovation, learning, and collaboration. In such a culture, employees are encouraged to actively contribute to process improvement initiatives, share ideas, and provide feedback. It fosters an environment where continuous learning and experimentation are valued, driving ongoing process optimization. An organizational culture that embraces continuous improvement supports the long-term sustainability of process improvements by nurturing employee engagement, ownership, and accountability. Sustainable process optimisation also requires an assessment of the culture, including metrics tracking how involved employees are in the optimization process. In this, the last workshop in the series, participants will learn how to leverage their existing enterprise process model (see workshop 3) to develop, enhance and maintain their sustainable process optimisation framework.

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Methodology

Process Optimization

Program Planning

Data-driven process excellence refers to using process-aware data to improve organizational processes continuously. It involves collecting, analyzing, and leveraging process-aware data to improve business process outcomes. Typical examples of outcomes include cost reduction, reducing defect rate or execution times. However, it could also include reducing the time to market for a product or service.

The theoretical underpinnings of the program are based on a melding of process and data science, combining process automation and operations management with machine learning and predictive analytics.

Program participants will explore how to deploy a data-driven process excellence strategy fully. The program commences by showing how the firm’s strategic positioning – how it distinguishes itself from its competitors and how customers perceive it – is a function of the effectiveness of its various processes. Then, following an exploration of process thinking, the organization’s process owners and senior managers explore how to build an enterprise process model describing its process landscape.

An integral part of program planning is identifying suitable candidate processes for the optimization journey. As such, participants will learn how to perform exploratory analysis on candidate processes to ensure that structured data is available and extractable. Additionally, they will confirm that the identified processes are modifiable based on findings from the process mining and monitoring artifacts produced.

The program will introduce participants to the expectation for role-holders involved in the successful implementation of their organization’s data-driven process excellence program, such as Process Owners (who is accountable for the business process), Subject Matter Experts (SMEs – who possess in-depth knowledge and experience on process execution), IT experts (who are familiar with the systems supporting the process), Process Analysts (who are skilled in analysing processes and applying Process Mining techniques) and Process Engineers (who possess the capability to build the required process mining and monitoring artifacts.

Finally, participants will learn to determine and document key process metrics, including objectives for their process excellence initiatives. They will also learn to determine which process insights are most valuable to key stakeholders, as the customized artifacts built later in the program will deliver these insights.

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Program Development

In this program phase, participants learn to develop the process-aware data at their disposal to obtain insight into process performance. Typically, participants for this phase include Process Owners, Data Engineers, and Business Process Analysts.

The development phase begins with examining data extraction and quality assessment approaches. The goal is to equip participants with the skills and knowledge required to extract the necessary event data and assess whether it is of the requisite quality to optimize the candidate processes. This phase includes determining the scope of data extraction (e.g. granularity of event data, data attributes for extraction and the period in scope).

Once the scope is determined, participants will learn how the event data can be extracted from the relevant system(s) and joined into a collection of events (e.g., in a table where each row represents an event). Additionally, they will be able to assess the extracted data on relevant criteria such as missing data (e.g. determining whether any attributes or events are missing and, if so, which attributes/events and what proportion the data), imprecise data (e.g. where the operator completes an activity offline and records the information in the system after a lengthy delay) or incorrect data (e.g. where data has been overwritten or corrupted).

Subsequently, this phase of the program will equip participants with the required skills to decide the appropriate actions to remediate the data (e.g. remove incorrect data, impute missing data), including creating a data remediation plan.

Finally, participants will learn how to enrich event data by deriving or computing additional events or data attributes based on data in the event log or adding external data, as well as filtering the data to reduce complexity and focus analysis.

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Program Implementation

In the implementation phase, participants learn how to build relevant process mining and monitoring artifacts from the event logs (e.g. process models, dashboards, etc), extract insights from these artifacts and implement changes to improve process outcomes. Participants for this phase typically include Process Owners, Business Process Analysts and Operational Managers.

Regarding building process mining and monitoring artifacts, participants will learn to automate the discovery of process models, which provides transparency into ‘real’ (as opposed to ‘assumed’) process behavior. They will also master conformance checking techniques to detect inconsistencies between the intended and real process behaviour. The output of conformance checking analysis drives the reduction or elimination of non-conforming behavior. Participants will also become competent in the production of descriptive and diagnostic process mining artifacts (e.g. tables, charts and dashboards, etc.) to enable process owners and managers to monitor process outcomes; as well as predictive or prescriptive process monitoring artifacts to provide operational support to process executors to avoid undesirable process outcomes.

Subsequently, participants will acquire skills to utilize the insights derived from these artifacts to drive the implementation of changes designed to improve process outcomes. These changes include process re-design or automation to reduce delays, late delivery and rework. Participants will also become proficient in implementing changes which provide operation support by detecting potentially problematic cases and acting on recommendations to prevent defects and delays.

Finally, participants will become skilled at maximizing change adoption by designing comprehensive training programs and providing on-the-job support and communication to impacted stakeholders. Participants will learn how to create training which caters to different learning styles and skill levels, empowering employees to embrace the process changes confidently and continuous support mechanisms, such as on-the-job coaching and access to subject matter experts, designed to help employees navigate challenges and build competence. They will also become competent in creating awareness and generating enthusiasm for the changes in a manner that fosters a sense of ownership and alignment with the organization’s vision.

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Program Review

The program review stage ensures that participants develop the skills and knowledge to sustain the organizational improvements delivered over time. The program design aims to uplift participants’ knowledge and skills, resulting in an associated uplift in business outcomes. As such, learning outcomes are linked to measurable organizational behaviors and related business outcomes. Over time, the output of these measurements drives adjustments to the program material or deployment of organizational changes.

Data collection is a crucial aspect of the program review process, involving gathering feedback from participants, trainers, and relevant stakeholders through surveys, interviews, and focus groups. This feedback provides valuable insights into the program’s effectiveness, strengths, and areas for improvement.

The program rollout team will support relevant organizational stakeholders to utilize quantitative metrics to measure the program’s impact. Key performance indicators (KPIs) related to process efficiency, quality, and productivity will be tracked before, during and after the training to gauge its impact on business outcomes.

Another critical element of the program review is assessing the application of knowledge and skills acquired during the training. Follow-up assessments or practical exercises are incorporated into the program to help determine how well participants apply what they learned daily.

In addition to participant-focused evaluation, the program rollout team and relevant stakeholders will assess the training program’s alignment with the organization’s needs and culture. This assessment includes examining whether the training content aligns with the specific challenges and processes of the company and if it resonates with the participants’ roles and responsibilities.

The review process will typically involve a cross-functional team that includes representatives from Human Resources, Training, Process Improvement, and business stakeholders. Their diverse perspectives will provide a comprehensive understanding of the program’s impact and effectiveness.

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Industries

Banking & Financial Services

The Banking & Financial Services sector has a rich history of striving for process excellence. Over the years, financial institutions have recognized the importance of efficient and streamlined processes to enhance customer experience, reduce operational costs, and comply with regulatory requirements. As a result, approaches which had driven process improvement in manufacturing industries such as Lean Six Sigma, were adopted. However, while delivering significant improvements, these initiatives primarily relied on manual analysis, which often proved time-consuming and labor-intensive. However, a new era of process excellence has unfolded with the advent of technology and process mining. Process mining utilizes advanced data analytics techniques to extract insights from event logs and transactional data, providing a comprehensive understanding of the actual processes executed within an organization. This historical perspective allows financial institutions to identify bottlenecks, inefficiencies, and compliance issues, enabling them to optimize processes and achieve higher operational excellence.

However, as financial institutions face increasing competition, changing customer expectations, and evolving regulatory landscapes, efficient processes have become more critical than ever. For example, customer requirements for greater efficiency and reduced costs in the entire trade lifecycle have driven the evolution of Straight-Through Processing (STP) which automates trade execution from the initial order to the final settlement, eliminating the need for manual intervention, which can lead to errors and delays. Process mining offers real-time visibility into end-to-end processes, enabling organizations to identify process variations, exceptions, and root causes of inefficiencies. By leveraging this valuable insight, banks and financial institutions can streamline operations, reduce costs, minimize errors, and enhance customer satisfaction.

Furthermore, as financial services regulators globally have become more stringent in reducing the risk of Money Laundering or Terrorist Financing through financial platforms, process mining has assisted organizations in meeting compliance requirements by identifying potential risks and ensuring adherence to regulatory guidelines. With the advancements in technology and the availability of robust process mining tools, the sector is witnessing a significant uptake of process excellence initiatives, leading to improved operational performance and competitive advantage.

Looking ahead, the future of process excellence in the Banking & Financial Services sector holds immense potential, driven by advancements in process mining and emerging technologies. As digital transformation continues to reshape the industry, financial institutions increasingly adopt automation, artificial intelligence, and machine learning to optimize processes and deliver superior customer experiences. Process mining will play a pivotal role in this transformation by enabling organizations to leverage data-driven insights to identify process inefficiencies, predict bottlenecks, and drive continuous improvement. Integrating process mining with robotic process automation (RPA) and intelligent automation will enhance process efficiency, accuracy, and scalability. Additionally, applying process mining beyond internal operations to customer-facing processes, such as loan applications, fraud detection, and customer onboarding, will enhance the customer journey and foster innovation. The future of process excellence in the Banking & Financial Services sector lies in embracing technological advancements and leveraging process mining to achieve operational excellence, regulatory compliance, and sustainable growth.

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Case Study

PostFinance:
PostFinance is one of Switzerland’s major retail banking institutions. Its primary business is in national and international payments, with a lesser but rising presence in savings, pensions, and real estate.

The Problem:
PostFinance believed there were opportunities to optimize its account opening operations and make it more transparent. The objective was to create leaner, defect-free processes with shorter turnaround times.

How Process Mining Helped:
Process mining brought transparency to the account opening process enabling the organization to understand the significant gap between how they assumed their processes worked and how they worked. As early as the proof-of-concept phase, they swiftly gathered key preliminary insights and transformed them into tangible actions.

Results Delivered:
Process Mining insights acquired from the account opening process analysis drove the development of the PostFinance app. As a result, customers can now open an account in only 10 minutes by utilising the app to provide their info from their preferred device.
(Source: tf-pm.org)

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Healthcare

Healthcare providers face numerous challenges, including fragmented systems, rising costs, complex regulations, and the need to deliver personalized care. These challenges have impeded the universal goal of these organizations to provide high-quality patient care, improve operational efficiency, and ensure regulatory compliance. Process mining has helped overcome many challenges by utilizing advanced data analytics techniques to extract insights from healthcare data, such as electronic health records and operational logs, providing a comprehensive understanding of the processes executed within healthcare organizations. This historical perspective allows healthcare providers to identify process variations, bottlenecks, and potential areas for improvement, leading to enhanced patient safety, reduced costs, and improved overall operational performance.

Patient pathway discovery is one of the most critical applications of process mining in healthcare. Patient pathways are the steps that patients typically take through a healthcare system, from diagnosis to treatment to discharge. Healthcare organizations can identify opportunities to improve patient experience and reduce costs by understanding patient pathways.

For example, process mining facilitates the identification of patients who are falling through the cracks. These patients are not receiving the care they need, either because the right providers are not seeing them or because they are not following the recommended treatment plan. Process mining also enables healthcare organizations to identify patients who are waiting too long for care, resulting in the development of interventions to improve the flow of patients through the healthcare system.

In addition to patient pathway discovery, process mining helps improve a wide range of healthcare processes, including admission and discharge, medication management, diagnostic testing, surgery, rehabilitation and home healthcare.

As healthcare providers progressively move towards value-based care, patient-centered approaches, and interoperability, process mining will play a critical role, enabling healthcare providers to leverage data-driven insights for process optimization, clinical decision support, and population health management. Integrating process mining with emerging technologies such as Internet of Things (IoT) devices, wearable sensors, and telemedicine will further enhance patient monitoring, remote care delivery, and preventive interventions. Additionally, process mining can aid in precision medicine by analyzing large-scale genomic and clinical datasets to identify patterns and improve treatment protocols.

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Case Study

Universitario Lucus Augusti:

The Problem:
Though priority lanes existed for certain types of cancer to ensure the prompt referral of patients to specialists, there was a hypothesis that a relatively low proportion of eligible patients. This study aimed to determine if this was the case and recommend ways to increase the proportion of patients referred through the pathway as early treatment for these cancers resulted in better outcomes and ensured that the hospital met its legal obligations.

How Process Mining Helped:
The analysis enabled the hospital to confirm its hypothesis about the proportion of eligible patients going through the priority lanes. In addition, it was able to identify bottlenecks in the process as well as other inefficiencies.

Results Delivered:
Transparency was delivered into true patient pathways enabling comparison to the desired pathway. The hospital also understood why certain eligible patients were not following the expected pathway.
(Source: tf-pm.org )

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Government/Public Service Sector

Public service providers are facing several challenges including:

• Financial pressures: Factors such as rising service provision costs, an aging population, and economic uncertainty have contributed to these pressures, making it difficult to provide the services that citizens demand.

• Increasing citizen demand: Citizens are demanding more and better services from service providers putting a strain on service provider budgets and resources.

• Changing demographics: The demographics of the population are changing, with more people living longer, more people living in urban areas, and more people from diverse backgrounds, making it more difficult for public service providers to plan and effectively meet the needs of all citizens.

These challenges are complex and interconnected, and there is no easy solution. However, innovations such as process mining have provided an innovative way to assist public service providers in delivering these services more efficiently and effectively, as detailed below:

• Cost pressures: Process mining can help governments identify and eliminate inefficiencies leading to significant cost savings, which can be used to fund other essential services.

• Increasing citizen demand: Process mining can help governments improve the efficiency and effectiveness of their service delivery, resulting in shorter wait times, better customer service, and increased citizen satisfaction.

• Changing demographics: Process mining can help governments adapt their services to the population’s changing needs. For example, by exposing the true manner in which service users interact with the service, service providers can better refine the service to meet the preference and needs of service users.

• Identifying fraud and abuse: Process mining can identify patterns of fraud and abuse in public service programs, helping governments recover lost funds and protect taxpayers.

• Ensuring compliance: Process mining can ensure that service providers comply with regulations, enabling them to avoid compensation and penalty costs.

• Making better decisions: Process mining can provide public service providers with insights into their processes that can assist them in making better decisions about how to allocate resources and improve service delivery.

Looking ahead, integrating process mining with emerging technologies such as robotic process automation, natural language processing, and blockchain will enable public service providers to enhance their processes and service delivery further. Using process mining in citizen-facing processes, such as permit applications, tax filings, and social service delivery, will streamline interactions, reduce processing times, and improve overall satisfaction. Furthermore, applying process mining in regulatory compliance and risk management will strengthen governance and transparency in the public sector.

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Case Study

Bolton Council:

The Problem:
Bolton Council’s Adult Services division was given the toughest challenge of its history: it had to make significant % budget cuts totaling 40% over three years without sacrificing the breadth or quality of the vital front-line services it provides to vulnerable individuals. There was pressure to identify areas where significant cost savings might be made without having an unfavourable effect on service delivery.

How Process Mining Helped:
When the Adult Services helpdesk received a call, the process directed that an appointment be scheduled for a social worker to visit. However, the process mining analysis revealed that many social worker appointments that did not require a visit were arranged, wasting important time and resources. The time needed for social workers to attend the visit and perform the evaluation would range from half a day to a day and a half, representing a significant time commitment.

Results Delivered:
Due to the process mining analysis, the organization decided to introduce a screening process that triaged calls enabling the helpdesk to escalate a case to a professional for expert opinion rather than automatically arranging a visit. Adding this process has ensured that social workers can spend more time with those who require their service.
(Source: tf-pm.org)

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IT System Development and Support

The Information technology (IT) sector is facing many challenges in its development and management of systems, including:

• Unclear requirements: Often, customers are unclear about what they want from a software system or digital transformation project leading to problems during the development process, as the software development team may not be able to build a system that meets the customer’s needs.

• Complex customer processes: Many businesses have complex processes that are difficult to understand and model, making it difficult for software teams to build systems that integrate these processes and deliver the desired benefits.

• Benefits realization: Measuring the benefits of a software system or digital transformation project can be difficult, making it difficult to justify the investment in these projects and ensure that they deliver the desired results.

Below is a brief synopsis of how process mining enables IT service providers to effectively tackle these challenges.

Complex and opaque business processes often involve numerous interconnected steps and stakeholders, making it challenging to understand how these processes function in practice comprehensively. Process mining addresses this challenge by extracting and analyzing event data from various IT systems and applications, creating process models that represent the actual flow of activities. These models provide a clear visual representation of how processes unfold, highlighting potential pain points and areas for enhancement.

Requirements gathering, a pivotal phase in software development, has also been transformed by process mining. Traditional methods often rely on subjective interpretations and assumptions, leading to misaligned expectations. Process mining provides an objective view of existing processes, uncovering hidden nuances and intricacies, enabling stakeholders to capture accurate and comprehensive requirements, minimizing the risk of misunderstandings, and ensuring that software solutions truly align with business needs.

Furthermore, process mining’s impact extends to benefits capture, a critical aspect of software development and digital transformation projects. By monitoring and analyzing post-implementation processes, organizations can measure the actual benefits realized from software initiatives. This data-driven approach enables accurate assessment of return on investment and identifies potential areas for further optimization.

Rapid technological advancements and the integration of emerging technologies characterize the outlook for the IT sector. The application of process mining in emerging areas such as cybersecurity, data privacy, and ethical AI will ensure responsible digital practices and enhance customer trust. The future of the IT sector lies in embracing technological advancements and leveraging process mining as a key enabler for digital transformation, customer-centricity, and sustainable growth.

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Case Study

ICT Directorate, Ana Aeroportos de Portugal:

The Problem:
The organization needed to find a way to balance the requirements to promptly respond to business changes to their IT infrastructure whilst keeping it stable. Their complex change management process thoroughly assessed change requests and ensured change implementation did not cause disruption or performance degradation. The organization believed opportunities existed to optimize the process and increase performance but required assistance to identify these opportunities.

How Process Mining Helped:
Process mining enabled the automated discovery of the current state process, resulting in the insight that the design of the change management process was sub-optimal. Specifically, a process gap was identified that prevented an adequate response to an issue the organization frequently encountered.
Process mining also facilitated process performance analysis, providing intelligent insights on specific activities that needed to be completed quickly to make the process more efficient.

Results Delivered:
The insights from the process mining analysis drove changes, including more effective workforce balancing, process re-design and selection of better performance measures for the entire process and specific activities. In addition, the organization applied these principles to other similar IT Management processes.
(Source: tf-pm.org)

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Education Sector

The education sector currently faces several challenges that are driving innovation, including:

• More sophisticated student demand: Students increasingly demand more personalized and relevant learning experiences to enable them to learn at their own pace and in their way.

• The move towards self-directed learning: Students are also increasingly taking control of their education as they want to learn what they wish, when, and how they want.

• Customization: Students want to be able to customize their learning to fit their individual needs and interests, which permits them to choose the courses they want to take, the pace they want to learn at, and the methods they want to use.

These challenges force educational institutions to innovate to meet learners’ needs as they develop new technologies and pedagogical approaches that allow students to learn in more personalized, relevant, and self-directed ways.

An innovation that has helped meet this challenge is process mining, as its data-driven approach enables educational institutions to identify areas for improvement, personalize learning experiences, and implement targeted interventions to support struggling students.

One of the most profound impacts of process mining in education is its ability to predict student outcomes based on learning behaviors and academic performance, as the analysis of the digital footprints left by students in learning management systems (LMS) and virtual learning environments (VLE) can provide valuable insights into individual learning patterns. These insights enable educators to identify struggling students early on and provide timely interventions, fostering a more personalized and effective learning experience.

Furthermore, process mining enhances the quality and actionability of feedback, a cornerstone of effective learning as it facilitates the examination of patterns of student engagement, progress, and interaction with learning materials, enabling educators to provide timely and relevant feedback. This personalized feedback loop facilitates more profound understanding and encourages continuous improvement.

Process mining empowers students to take charge of their educational journey in the realm of self-directed learning by enabling the visualization of their learning behaviors and progress, so students can identify areas for improvement, set goals, and monitor their growth over time. As a result, a sense of ownership and agency in learning is promoted, fostering lifelong learning skills crucial in the ever-evolving knowledge landscape.

Process mining also aids in optimizing administrative processes, such as admissions, enrollment, and resource allocation, leading to increased operational efficiency and cost savings. Additionally, process mining helps institutions align their processes with best practices and regulatory requirements, ensuring compliance and accountability. In summary, the education sector is witnessing a shift towards evidence-based decision-making and continuous improvement, leveraging process mining to achieve process excellence and enhance student outcomes.

The future of the education sector is expected to be shaped by advancements in technology and a data-driven approach to education. Integrating process mining with emerging technologies, such as artificial intelligence and machine learning, will enable educational institutions to derive deeper insights from data, predict student behavior, and personalize learning experiences at scale. Furthermore, the application of process mining in identifying learning gaps, improving curriculum design, and evaluating the effectiveness of teaching methodologies will drive continuous improvement in educational practices. Process mining can also facilitate collaboration and knowledge sharing among educational stakeholders, enabling benchmarking and adopting best practices. As educational institutions increasingly embrace digital transformation, process mining will be essential in optimizing processes, enhancing student engagement, and providing high-quality education in the digital age.

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Case Study

Masaryk University, Czech Republic:

The Problem:
Although the institution had a learning management system the manner students interacted with it was relatively opaque as lecturers did not typically have many opportunities to monitor what exactly is happening in their online courses, how students behave in them, how they approach studying online materials, or how they proceed when engaging in learning activities.

How Process Mining Helped:
Utilizing process mining, the organization discovered several student quiz-taking behaviours such as standard, feedback misuse, misuse of study resources, and multitasking. Analysts used students’ behavior data for five tests (formative and final evaluation) for this analysis.

Results Delivered:
The insight provided by this analysis laid the foundation for detecting undesired behaviour (e.g. feedback misuse, misuse of study resources) for the quizzes designed for final evaluation. These visualizations would be reasonably simple to use by lecturers and increase detection rates, improving the integrity of the educational system.
(Source: sciencedirect.com)

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Locations

Eindhoven, Netherlands

Eindhoven, the fifth-largest city in the Netherlands, is situated in the country’s south. Often referred to as the “City of Light,” it is well known for its cutting-edge technology, flourishing design sector, and wide range of exciting attractions.

Among the well-known businesses situated in Eindhoven are organisations like Philips (established in 1891), ASML (formed in 1984 as a joint venture between ASM International and Philips and presently Europe’s most valuable tech firm), and NXP Semiconductors (founded in 1953).

In 1956, businesses, local government, and academics joined to form the Eindhoven University of Technology. This institution was the birthplace of process mining in the early 2000s. The University is at the heart of Brainport Eindhoven, one of the most significant technological centres in the world. Eindhoven also hosts a High Tech Campus considered the most innovative square mile in Europe, providing an ecosystem of hundreds of high-tech companies and thousands of innovators, researchers, and engineers.

As the place where process mining was born, it is no surprise that companies in Eindhoven (and across the Netherlands) are adopting process mining to improve efficiency, reduce costs, and mitigate risk. For example, these organisations have used process mining to identify supply chain bottlenecks, enabling them to make changes to the supply chain to improve efficiency and reduce costs. They have also used Process mining to identify fraud and errors in their business processes, facilitating corrective action to prevent future fraud and errors.

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Case Study

Philips MR:

The Problem:
Located in Best (near Eindhoven), Philips MR is a branch of Philips Healthcare that creates magnetic resonance imaging devices. Philips MR aimed to address a major issue: a lack of transparency about how their client utilized their devices and how that behavior deviated from the expected (and specified) behaviour.

How Process Mining Helped:

Process mining enabled the organization to analyze the usage profile of an MRI application by examining the series of scans (which was a critical requirement). A practitioner could compare the estimated usage profile to published medical guidelines.

Results Delivered:

The organization confirmed that the most typical workflow was the one specified in their guidelines. They did, however, notice several variances, which were analyzed to check if they were attributable to specialized processes used by certain practitioners or deviations caused by system or human error.
(Source: tf-pm.org)

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London UK

With the iconic River Thames flowing through it, London is a global financial center and one of the most important economic hubs in the world. However, in recent years, the city has faced several economic challenges, including Brexit and rising inflation, to name a couple.

Brexit has had a significant impact on London businesses. The UK’s departure from the European Union has created uncertainty and volatility in the markets, making it more difficult for businesses to plan for the future. In addition, Brexit has increased costs for businesses, as they must now comply with new regulations and trade barriers.

Rising inflation is another challenge facing London businesses. Inflation recently reached a 40-year high in the UK, putting pressure on businesses’ margins. Businesses have to raise prices to offset the rising costs of goods and services, which could decrease demand.

In response, several London-based organizations (and across the UK) are utilizing process mining to address some of their challenges. Process mining has enabled them to identify inefficiencies and make changes to improve efficiency and reduce costs.

Specifically, process mining is enabling these organization to:

• Identify and eliminate waste in their processes.
• Improve decision-making by providing insights into how processes are performing.
• Ensure compliance with regulations.
• Mitigate risk by identifying and preventing fraud and errors.

Looking ahead, the adoption of process mining in London and across the UK holds immense promise. The UK government’s emphasis on digital transformation and innovation across sectors will drive the adoption of process mining, ensuring that organizations can adapt and thrive in the digital age.

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Case Study

BP:

The Problem:
Headquartered in the heart of London, BP is an oil and gas company with operations worldwide across many business divisions, sectors, and processes. This global reach made standardizing their procurement processes challenging. Though the organization knew how it wanted processes to flow ideally, it was unclear where processes were non-conforming.

How Process Mining Helped:
Process mining enabled the organization to visualize the potential effects of the recommended process modifications, resulting in more engaged stakeholders who could see the benefits that interested them. It also clarified how these changes would improve the process and result in better outcomes.

Results Delivered:
The organization combined process mining with automation to address execution gaps. For example, when confirmation that the client has received goods was missing at the point of invoicing, the client received an automated email to obtain this information, the response to which was then entered straight into SAP. Additionally, the organization obtained insight into the average processing time for invoicing and the activities that took place to identify inefficiencies.
(Source: celonis.com)

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New York, USA

Located at the intersection of the Hudson River and the Atlantic Ocean, New York City is one of the most economically important cities in the world. However, it has faced several challenges in recent years, as outlined below.

The tech sector is a major driver of the New York economy, but layoffs have hit it hard due to increasing competition and rapidly changing customer requirements. This trend is expected to continue, and these layoffs are having a ripple effect on the city’s economy, as they affect businesses that support the tech industry, such as restaurants, bars, and retailers.

The COVID-19 pandemic also had a significant impact on the New York economy. The city was one of the hardest hit by the pandemic, and the economy took a long time to recover. The city is still on the path to full recovery, and businesses are still struggling to attract and retain customers.

Process mining can help New York businesses address some of their challenges by enabling them to identify inefficiencies in their processes and make changes to improve efficiency and reduce costs, as illustrated below.

Tech companies are utilizing process mining to identify and compare the true customer journey with the assumed journey. This information can help quickly unearth changes to customer requirements and enable the company to make changes to delight customers.

Several retailers use process mining to identify fraud in their returns process, enabling them to take corrective action and prevent future fraud.

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Case Study

Johnson & Johnson:

The Problem:
The organisation’s process mining center of excellence (COE) was required to swiftly support the improvement of on-time delivery in the Latin America (LATAM) region without burdening local teams or compromising strategic direction. They needed to assist the LATAM team in identifying bottlenecks in the process and obtain buy-in from other stakeholders.

How Process Mining Helped:
The COE provided a pre-built process mining analysis to the LATAM team, which was further refined to better suit their goals. That customised analysis enabled the team to show stakeholders the big picture clearly, pinpoint the shortcoming of the ‘As-Is’ process, and bring them along on the process redesign journey.

Results Delivered:
The process mining project yielded a 30% decrease in contact time, a 40% reduction in pricing modifications, and substantial improvements in project on-time delivery. Additionally, the analysis template was made available for deployment to other teams, enabling local project teams to complete these transformations rapidly.
(Source: celonis.com)

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Frankfurt, Germany

Situated in the cape of the Taunus Mountain range, Frankfurt is the largest financial center in continental Europe and a hub for the German automotive and technology industries. In recent years, the city has faced several economic challenges, including slow progress in energy transition and dependence on the supply of rare metals and semiconductors, to mention a couple. Below is a brief discussion of these challenges and how process mining can help address them.

Slow progress in energy transition

Frankfurt’s economy is closely linked to the energy sector. However, the city (and Germany as a whole) has been slow to transition to renewable energy sources, exposing local businesses to rising energy prices and the volatility of the energy market due to factors such as the war in Ukraine, among others.

Process mining can help businesses address the challenges of the energy transition by providing insights into their energy consumption. For example, by annotating each activity it performs with energy consumption data, organizations can use process mining to produce energy-oriented process models, facilitating the identification of unnecessary steps in their business process that consume energy or efficient equipment use. This analysis can facilitate changes to eliminate waste or optimize equipment use to reduce energy consumption.

Dependence on the supply of rare metals and semiconductors

The automotive and technology industries in Frankfurt heavily depend on the supply of rare metals and semiconductors. However, the supply of these materials is vulnerable to supply chain disruptions, creating problems for local businesses, as they may be unable to get the raw materials required for production.

Process mining can help Frankfurt businesses address the challenges of supply chain disruptions by providing insights into their supply chains. For example, companies can use process mining to identify unreliable suppliers with a history of delays, enabling the diversification of the supply chain and reducing the risk of disruptions.

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Case Study

Deutsche Telekom Services Europe (DTSE):

The Problem
The organization sought to eliminate waste in their procurement processes using the massive data created daily. Despite successfully digitalizing their primary operations and being conscious of some problems with the process, DTSE still needed to identify and address the fundamental causes of these problems. Some of the problems identified included duplicate payments resulting in significant monetary losses, inability to maximise cash discounts due to blocked payments and poor delivery date accuracy resulting in hefty fines, among others.

How Process Mining Helped:
Process mining enabled the organization to link data from more than ten source systems to obtain unparalleled visibility into their procurement processes. With a real-time understanding of how their processes worked, DTSE could now define more appropriate process metrics and create leading indicators alerting relevant stakeholders that these metrics were trending in the wrong direction. Additionally, process mining helped the organization’s procurement processes to be led by data.

Results Delivered:

• DTSE implemented synchronous reporting, which facilitated the release of blocked invoices assisted the organization to achieve a cash discount rate of 96%, resulting in significant savings of €40 million per year.
• Combining process mining with automation enabled invoices to be prioritised rapidly and increased on-time payments, resulting in an on-time payment percentage of more than 90%.
• DTSE dramatically boosted their no-touch rate, freeing up capacity and resulting in € 12M in savings. Additionally, the improved procurement process efficiency has resulted in Purchase Orders which are now correct the first time, more than 90% of the time.
• The organization implemented duplicate payment analysis utilizing real-time data, enabling them to save €3 million and prevent future duplicate payments.
(Source: celonis.com)

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Zurich, Switzerland

Nestled at the northwestern end of Lake Zurich, Zurich is the largest city in Switzerland. Its financial center is considered one of the most competitive in the world and it is also renowned for its light industry, machine, and textile industries. However, the city has recently faced many economic challenges, including the increasing cost of regulation, and high employment costs, which are briefly discussed below.

Cost of regulation

Zurich is a highly regulated city, which can be challenging for businesses. The cost of complying with regulations can be high, which can strain businesses’ margins. In addition, regulations can sometimes be complex and time-consuming to comply with, which can burden businesses.

Process mining is currently utilized by many Zurich-based businesses to optimize regulatory compliance by comparing the actual process to the desired process, ensuring they can identify control gaps such as deviations from the desired process e.g., tasks that are not being performed or decisions that are not being made correctly. In addition, process mining is helping to facilitate compliance monitoring enabling corrective action to be taken before they lead to a violation, resulting in cost avoidance from penalties or fines.

High employment costs

The cost of labor in Zurich is comparatively high, making it difficult for businesses to compete with businesses in other locations. In addition, the Swiss labor market is very regulated, which introduces additional complexity to recruitment processes.

Several firms are using process mining to address this challenge by identifying and eliminating waste in processes, such as unnecessary steps, delays, and rework, freeing up resources to be used more effectively elsewhere. In addition, they are combining process mining with automation to close execution gaps, reducing risk and ensuring that resources can focus on more value-added tasks.

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Case Study

GSK:

The Problem:
In a bid to focus on managing costs, the organization decided to explore whether process mining could help identify non-value-added activities in its quality management process and provide insight into losses to the organization due to these activities. It also wanted to find out the cost impact of non-conforming processes.

How Process Mining Helped:
Process mining enabled the organization to leverage its structured and unstructured ERP data to understand the costs of processes and variations. It utilized natural language processing (NLP) approaches to determine relevant activity names from the unstructured data, which was subsequently mapped to cost drivers. Subject matter experts (SMEs) validated the results to check whether they conformed to their reality.

Results Delivered:
The process mining analysis drove targeted improvement, resulting in a 37% improvement over baseline performance. The wider production network currently utilizes this approach to eliminate waste from diverse operational processes.
(Source: tf-pm.org)

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Program Benefits

Testimonials

Executive Director, Nomura

“I have known Dr Ogunbiyi for nearly 10 years, and he is a consummate profession. He was the Process Lead on a Process Re-engineering initiative at Barclays bank, to completely re-engineer the wealth and investment management processes for the bank. Despite already being a process expert with Master Black Belt in Lean Six Sigma, Dr Ogunbiyi has been continuously developing his expertise in this space, and has completed his PhD in Process Mining alongside a full-time job in process mining. This demonstrates his thirst for knowledge, his ability to ruthlessly multi-task and prioritise and his willingness to continue to develop this area into a more mature capability. Dr Ogunbiyi more recently has introduced process mining to Nomura, a completely new concept for us, and has taken the time to explain the concept and how it could be applied not only at Nomura more broadly, but also on specific processes. This level of knowledge is unparalleled and has been received very well internally. Dr Ogunbiyi is a fantastic professional and extremely competent, and I would certainly recommend him for future process mining training programmes.”

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Vice President, JPMorgan Chase & Co

“I am pleased to endorse the process optimisation training program developed by Dr Ogunbiyi given his practical experience and academic qualifications. I met him when I first attended the UK process mining community. He wasted no time in making sure I met others in the field and kept me in the loop for process mining events and activities. Dr. Ogunbiyi’s leadership in running the Process Mining Community Group demonstrates his ability to facilitate collaboration between industry and academia, fostering valuable dialogue. We collaborated with him on a successful event at my former employer, which showcased his adeptness in curating insightful discussions across sectors. The event was a big success and attracted process mining practitioners from major international banks and the healthcare sector. On several occasions, I’ve witnessed him present process mining work from both the financial services and healthcare sectors, showcasing his expertise in process architecture, and descriptive, and diagnostic process mining. I believe the training program will be an excellent fit for most organisations, not only due to his blend of practical experience and a holistic understanding of process mining but also because of his admirable personal character.”

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Founder & CEO, Cogni.Dx

“I’ve had the pleasure of knowing Dr Ogunbiyi for the past four years, during which we’ve engaged in frequent and enriching discussions about process mining. Dr Ogunbiyi’s exceptional depth of knowledge, well-rounded perspective, and consistent insights have positioned him as a reliable source for valuable advice and informed recommendations on process mining best practices and technological insights. His reliability and expertise have made him a dependable resource, always available to offer solid guidance and help. Dr Ogunbiyi played a pivotal role in securing our inaugural process mining project in the Middle East region, showcasing his invaluable competence and notable problem-solving skills within process automation and digital transformation. It has been a pleasure to work with him!”

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Professor, University of Westminster

“I have known and worked closely with Dr Ogunbiyi for around 5 years. In that time I have been impressed by his Process mining and monitoring expertise, evidenced in both his teaching and practice. As such, I highly recommend his process optimization training program. The training program is comprehensive and informative, covering a wide range of essential topics such as selecting the right processes to optimize, and how to effectively diagnose process problems and fix these problems sustainably, among others.

I am confident it would be a valuable asset to participating organizations and by improving the skills and knowledge of their employees, enable them to sustainably optimize their processes and contribute significantly to achieving their business objectives.”

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Senior Business Analyst, Department of Works and Pensions

“I was introduced to process mining by Dr. Ogunbiyi almost two years ago. His clear and motivating explanations helped me quickly grasp the potential benefits of process mining techniques, especially about reducing the time needed for requirements gathering and analysis for digital transformation projects, while improving the quality of the outputs. His expert and patient coaching enabled me to rapidly develop process mining skills that made me a more valuable Senior Business Analyst. Having personally benefitted from his competence and know-how, I strongly endorse his training program.”

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More detailed achievements, references and testimonials are confidentially available to clients upon request.

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