Service Science

Analysis and Improvement of Business Processes

John Maleyeff

• Publisher: Routledge
• Year: 2020
• ISBN:
  9781000075366
• Edition: 1
• Topic: Negocios y empresa
• Subtopic: Operaciones

To remain relevant in today's world, practitioners should presume that they have two jobs: first, to do their work effectively so that they provide value to the organization; second, to improve how the work is done so that their organization remains competitive. This book offers clear guidance to excel at this ubiquitous second job.

Informed by an appreciation that most personnel that work in any firm, even firms that are manufacturing-oriented, routinely provide services as a key element of their jobs, this book explains how to provide and improve internal customer service, regardless of industry or role. It illustrates the common features, or service process "DNA, " while providing a diverse set of examples to enhance understanding. Written by a pioneer in the development of principles and methodologies that address services in a structured and distinctive manner, this book stresses that service processes are distinctly different from manufacturing processes.

Rigorous and practical, this book will appeal to students and professionals alike, in business, hospitality, industrial management, public health, and other fields.

Online resources include Excel files that act as templates to help with quantitative analysis routines.

1

Service Science

Service Science Foundations

Service Science is an emerging academic discipline created in response to the need for organizations (businesses, industries, non-profits, governments, etc.) to better understand how to create, manage, and improve services for the benefit of consumers, internal entities, and external partners. The term science is used because service science researchers use observation and experimentation to describe fundamental underpinnings. This knowledge leads directly to a better understanding of how a business can effectively manage, analyse, and improve the services it offers and how best to deliver those services.

This book makes contributions to the field of service science by studying the structure of services from a process-oriented perspective derived from Lean management and Six Sigma principles. It avoids copying methods that work well in manufacturing settings because, at their core, service processes are structured in unique ways. Services need to be considered using a distinctive set of principles, methods, and tools.

Service science is, by necessity, a multi-disciplinary subject. It includes elements of industrial engineering, computer science, marketing, information technology, and business analytics. It is also known by the acronym SSME (Service Science, Management, and Engineering). Service science was initiated around 2002 at the IBM Almaden Research Center in conjunction with Professor Henry Chesbrough of the University of California at Berkeley. As IBM transitioned from a manufacturing-oriented to a more service-oriented business, many of its leaders were concerned that their management system needed to change accordingly. It became apparent that a better appreciation of how to design and deliver services to maintain profit margins at acceptable levels was necessary.

Any service organization with either internal or external customers can benefit from a better understanding of service science, especially those that experience the following challenges:

• Service quality is good but costs are high or the services takes too long.
• Service quality is inconsistent across customers or across service providers.
• Service delivery has been automated but problems remain or new problems are created.
• Cross-departmental information handoffs take too long or are prone to errors.
• Workers are frustrated with inadequate cooperation from other business functions.
• Workers often resort to fire-fighting which ineffectively uses resources.

Manufacturing Vs. Service

The study of manufacturing as a science began with the 1909 publication of The Principles of Scientific Management by the American engineer Frederick Winslow Taylor (1856–1915). Taylor advocated for the simplification and standardization of production work. He determined the most efficient approach to each work task using structured workplace experiments. An oft-cited example is Taylor’s shovel experiments, which resulted in specialized shovels that corresponded to the task at hand (for instance, a larger shovel for ash and a smaller one for iron ore). He performed similar “time and motion” experiments to determine standard work for bricklayers based on studying the motions required to build brick structures. This work also was used to document specific requirements that personnel departments used to hire workers. Taylor’s research was possible because so many tasks in manufacturing settings are machine-based or require humans to perform repeated, identical tasks (in fact, many of the tasks done in Taylor’s era are now automated).

In the years since Taylor’s work, additional advances to the state of knowledge in manufacturing management have taken place. Many of these advances have originated in university research departments (e.g., mathematical models for forecasting, inventory management, and optimization). Significant breakthroughs have been initiated by practitioners (e.g., Lean production which originated at Toyota; Six Sigma which originated at Motorola). Advances in information technology have resulted in new approaches, especially in the management of manufacturing supply chains (e.g., material requirements planning and enterprise resource planning).

Today, manufacturing management is a mature discipline. The applicability of most manufacturing management approaches from firm to firm is enhanced due to the commonality of characteristics across manufacturing systems. With a few exceptions, manufacturing processes deal with tangible items whose physical characteristics are changed (e.g., size, shape, color, hardness, etc.) as a result of the process. For example, not only is a plastic molding process very similar from firm to firm, but plastics molding processes share common traits with machining, painting, or curing processes for metal. They all require setup before production can begin, they all change the physical nature of the inputs to outputs; the amount of time to perform a task is constant or near constant, and success can be easily measured. Therefore, methods for control and improvement are essentially the same across all manufacturing processes, regardless of their product type, location, and customer traits. That is why university degree programs in mechanical, manufacturing, or industrial engineering prepare students to be experts across all types of manufacturing processes.

Services, on the other hand, vary a great deal from process to process (a detailed description is included in Chapter 2). Professionals who perform jobs such as consultants, accountants, actuaries, human resource professionals, auditors, salespersons, lawyers, insurance adjusters, nurses, and data scientists all deliver services. However, expertise in any of these fields is only focused on learning the skills needed to perform the necessary tasks. Little or no time is spent on optimizing the business processes involved in delivering these services. Lacking a scientific approach, these services would appear to have little in common. Hence, the approach to the design and management of service delivery tends to be ad hoc and inconsistent.

Service Science Practitioners

According to the Bureau of Labor Statistics, less than 10% of U.S. workers are employed within a manufacturing firm. But, most workers in a manufacturing firm have jobs classified as overhead, including those working in service departments such as sales, marketing, engineering, design, accounting, finance, information technology, human resources, analytics, planning, research & development, legal services, logistics, and various other support functions. With over 90% of workers employed by non-manufacturing firms, and most workers in manufacturing firms delivering services, one can estimate that service science is relevant to almost all jobs in the United States (this characterization would be similar in most developed economies and lower in developing economies).

It is also apparent that many (probably most) workers who deliver services have customers who are internal to their employer. For example, accountants provide financial reports to headquarters, designers provide blueprints to manufacturing, human resources hire workers for other departments, and forecasters deliver sales projections to the supply chain manager. Often, the effectiveness of these services are “hidden” because all costs associated with their operation are aggregated within the general, selling, and administrative expense line on a profit-and-loss statement. Consider a data scientist working within a marketing department. It has been reported that data scientists spend 50-80% of their time collecting and cleaning up data sets before they can be used to perform useful analyses. To a service scientist, rather than accepting that the time they spend as a “data janitor” is a necessary evil, an effort should be made to reduce this percentage for future projects.

During the course of their workday, most workers routinely encounter similar situations where much of their time is wasted. It has been reported that at least 20% of marketing budgets were spent on wasted efforts and that 50% of their budgets were spent on generating sales leads that were never pursued. Human resource managers reported that the ineffective efforts finding job candidates for technical jobs takes time away from other critical activities such as engaging and interviewing candidates. Some business processes are expected to be less than 100% effective in creating value. Innovation processes, for example, generate some ideas that create value and others that do not create value. But, a study showed that some firms’ innovation processes were much more effective than others [1]. These differences can be attributed to more effective business process management.

Unfortunately, this wasted time translates to higher than necessary spending, much of which is also hidden from view of a supervisor or manager. Based on observation alone, they may not be able to distinguish valuable time from wasted time, or they may accept the situation as normal. In time, a service worker’s job satisfaction is negatively affected, which impacts service quality and customer satisfaction.

Many other workers in service processes spend considerable time on activities that, in an ideal setting, would be unnecessary. They include: (a) an auditor waiting to meet with a client regarding suspicious transactions, (b) an accountant calling a division manager to clarify confusing ledger entries, (c) a human resource manager interviewing a perspective candidate who is not qualified for a position, (d) a product designer revising a blueprint because the manufacturing department cannot make a product that conforms to its design specifications, and (e) a proposal writer waiting for pricing information from the sales department. It may not seem critical if each worker spends an average of one hour per day on these unnecessary activities, but this one hour corresponds to over 240 hours per year (30 working days) of lost opportunity to use that worker on tasks that could positively impact an organization’s profitability.

Evolution of Business Process Management

Business Process Management (BPM) is not precisely-defined. It is rooted in a number of disciplines, including operations management, marketing, computer science, information technology, and strategy. For purposes of this book, the evolution of BPM from the perspective of operations management (OM) is most relevant. OM itself has broadened from focusing on production and inventory control to focusing on production management based on the realization that manufacturing effectiveness requires a system that integrates management, processes, and technology. As the service economy grew, OM became the preferred title of the discipline that included both manufacturing and service OM principles.

Today, OM consists of a combination of academic (mostly analytically-oriented) methodologies and industry (most practice-oriented) methods. Unlike many other scientifically-oriented disciplines, OM has always been influenced by business leaders and their firms. Henry Ford (1863–1947) invented the automobile assembly line around 1908. This innovation enabled him to produce the black Model T at lower costs and expanded his consumer base due to its affordable selling price. The quest for economies of scale characterized OM practice for many years. In 1909, Ford was quoted as stating “Any customer can have a car painted any color that he wants so long as it is black.” This statement effectively describes a limitation of the economies of scale approach.

In the years after the Second World War (1939–1945) a fundamental paradigm change took place, led by Taiichi Ohno (1912–1990) and Shigeo Shingo (1909–1990) at Toyota Motor Corporation in Japan. Their approach, which forms the basis of the Toyota Production System (TPS), achieves productivity improvements by studying the creation of value and removing all activities that do not add value for customers. Their approach is described in many publications, starting with The Machine That Changed the World [2]. Although sometimes referred to as the TPS, this approach is more commonly referred to as Lean production.

As productivity-focused advancements were evolving, a parallel effort existed that concerned quality management. As manufacturing moved from craftsman style approaches to systems with interchangeable parts, the need to ensure that quality standards were met became more important. In the U.S., methods of sampling inspection were developed during the Second World War by Harold Dodge (1893–1976). Later, these methods, which focused on separating good from bad items, were replaced by approaches pioneered by Walter Shewhart (1891–1967) and popularized by W. Edwards Deming (1900–1993). The new approach attempts to ensure quality by focusing on the processes that create products. Maintaining control of the production process became the goal, with various statistical and problem solving methodologies employed for this purpose. This new approach, originally called Total Quality Management, has become known as Six Sigma.

While the practice of manufacturing operations management matured, most developed economies became increasingly dominated by service-based industries. In response, the discipline of service operations management (SOM) was born. SOM focused on firms that delivered services as their core mission, such as hotels, retailers, airlines, and restaurants. SOM methodologies include some that were variations of production management and others that combined service marketing, information technology, and project management.

Little attention has been paid to the management of the secondary or internal services commonly referred to as business processes. A strategy known as Business Process Reengineering (BPR) was popular in the 1990’s. BPR practitioners advocated the design of business processes to account for new realities, such as modern information technologies, increased competitiveness, and the increase in speed within the global economy. The most important tendency in business processes has been the proliferation of outsourcing. Today, it is common for corporations to outsource many business processes, including information technology, human resources, analytics, legal, medical image analysis, call centers, copy editing, and audio transcription. There has, however, been somewhat of a backlash in recent years as the hidden costs of outsourcing have become more apparent and the additional value of insourcing business processes is better understood.

The applicatio...