Digital Supply Chains

Key Facilitator to Industry 4.0 and New Business Models, Leveraging S/4 HANA and Beyond

Götz G. Wehberg

• Publisher: Routledge
• Year: 2020
• ISBN:
  9781000069990
• Edition: 1
• Topic: Business
• Subtopic: Operations
• Index: Business

This book provides a practical guide to digital supply chain modelling, demonstrating an agile approach to how such models can be applied to any manufacturing company to build competitive advantage, facilitate new business models and drive towards Industry 4.0. The agile approach of the book provides an attractive alternative to the conventional country-by-country deployment of S/4 HANA and other relevant technologies.

This book contains the expertise Gotz G. Wehberg has amassed over 20 years as a senior partner in a leading consulting company, working across industries and with globally recognized clients, advising on digitization. In it, he explains the scientific roots of digital supply chain management such as holism, cybernetics, self-organization and evolutionary theory to inform a deep understanding that can drive a supremely innovative strategy for Industry 4.0.

Beyond strategy, Wehberg introduces the practical tools and technologies used in supply chain modelling, for example, sensors, big data, artificial intelligence and the Internet of Things, as well as a reference framework that categorizes the technologies, together with the latest concepts and tools, such as DDMRP, predictive S&OP, pattern recognition, autonomous logistics and Lean. This framework supports decision making for developing supply chains in an end-to-end and cross-functional fashion, providing clear guidance for executives and managers on how to design supply chains for the future.

Part 1
Why supply chain complexity matters

1
The complexity issue

The complexity problem is now on everyone’s lips. The increasing scope of coordination, and thus efforts to coordinate supply chains, ultimately results from the growing complexity of both the internal and external environment of enterprises (Ulrich/Probst 1991). Complexity presents itself as the product of corresponding dynamics and diversity. The increasing number and variety of relevant variables, as well as the heterogeneity of their relationships, characterize diversity. Dynamics reflect the degree of renewal of supply chain–relevant elements, as well as their relationships and effects over time. A well-known and often used abbreviation is VUCA, which stands for volatility, uncertainty, complexity and ambiguity. Slightly different definitions of VUCA have been offered, and there is little value in having a discussion about nomenclature. Dynamics and diversity, therefore, represent substantial drivers of complexity.

If we consider the development of the complexity of supply chain–related parameters in recent years, we can see an overall rising trend. Higher complexity requires more time for analysis and decision making. However, the response time available to supply chain managers decreases with higher dynamics. A kind of time shear opens a gap (Figure 1.1).

Figure 1.1 The time gap of supply chain management (following Bleicher 1995, modified by Wehberg 1997)

For supply chain practitioners, the aforementioned time gap appears in the form of a whole series of relevant megatrends such as digitalization, individualization, data security needs, scarcity of resources, service competition, terrorism, tax optimization and sustainability. Moreover, unforeseen disruptions in the daily business impact the supply chain more often. For example, the production planning and detailed steering (PPDS) cycle traditionally have a freeze. Many companies are facing rush orders, reprioritization and change requests during this cycle. The companies are not able to redo the PPDS as fast as the new requests are coming in. Issues accumulate. Stocks increase, creating bottlenecks within the process, extending response times and making the situation even worse. The so-called bullwhip effect materializes. Examples for potential disruptions include infrastructure breakdowns, biological catastrophes and terrorist attacks. Supply chain management in practice has become increasingly complex in recent years, and will continue to become more so in the future.

In highly complex, non-trivial systems, statements about the effectiveness and efficiency of supply chain management can no longer be described as linear if-then relationships. In addition, long-term impacts often do not correspond to short-term effects. As a consequence, a complete analysis of the supply chain system is impossible. Supply chain management acts “blindly” to a certain extent. In other words, synoptic planning is no longer possible (Probst 1981).

Ten Hompel (2014) speaks in this context about a hydrostatic paradox. The classical planning of material flow systems is essentially based on so-called marginal cost calculation. In this context, the maximum performance has to be provided as the volume delivered per unit of time. The dilemma of this analytical approach in a complex environment lies in the lack of controllability of the marginal power, which means in its barely existing flexibility and resilience.

Supply chain resilience thus means the ability of the supply chain to be able to handle unforeseen events such as logistics infrastructure breakdowns, biological catastrophes or terrorist attacks (see also Weick/Sutcliffe 2001). Over and beyond external disruptions, resilience also includes the capability to cope with increasing internal complexity, such as the climbing number of make-to-order products and rush orders. In a way, of course, such internal disruptors are also a response to external developments. Bottom line, a sufficient resilience allows the companies to maintain a reasonable level of effectiveness and efficiency of their supply chain. Resilience includes both an active dimension being addressed by the concept of agility, as well as a reactive one called robustness. While resilience is a characteristic of supply chain potentials and activities, flexibility means service quality as performance criteria.

Even if the individual system details can no longer be fully grasped or mastered, supply chain management generally tends to increase or decrease the extent of its complexity. Complexity mastery is replaced by complexity management or handling. “The need for coping with complexity arises from the realization that every [open] system is embedded in an environment that usually has many behaviours. If a system wants to survive in its environment, it must be able to ‘oppose’ these behavioural possibilities” (Probst 1981, translated). The extent to which supply chain management deals with complexity determines its efficiency and effectiveness, and thus its contribution to the overall success of the company (Bleicher 1995; Kirsch 1984; Ulrich/Probst 1991).

The traditionally propagated way of supply chain management was the avoidance or reduction of complexity, for example, by external regulation and specialization. However, such an approach is inappropriate to provide the necessary resilience and variety in the supply chain system necessary for the survival of the enterprise. Complexity management should therefore not always mean the reduction of complexity, but it may also imply its conscious increase. The ability to increase complexity is even a central prerequisite for the development and survival of open systems, such as supply chains par excellence (Röpke 1977). On the other side, an unconditional flexibility of supply chains, as is often discussed, is not always appropriate. For supply chains, complexity management in this sense means to handle the interplay between complexity increase and reduction in the form of an agile development, such as a trial-and-error process. It must take steps to reduce or increase complexity and at the right time, to the right extent. Although supply chain management does not have complete knowledge of the system’s interrelationships, it can recognize certain rules or patterns of order resulting from the interplay of the structures and behaviour of the system or the members of the system, and align its actions accordingly. Such an approach outlines the subject area of digital supply chain management as a whole.

2
Need for a framework

The high importance attributed to the complexity management of supply chains raises the question of an adequate design. This problem has not yet been fully addressed by scientific efforts. As will be proved in the course of the second part of this volume, the business sciences offer little theoretical-conceptual support regarding the handling of supply chain complexity. Even if the development of supply chain management is addressed, as for example with Copacino in 1992, Dubbert 1991, Ihde 1991, Weber/Kummer 1994 and Pfohl 1991, a comprehensive theoretical appreciation of complexity management for supply chains hardly takes place. In addition, the contributions are often limited to selected management subsystems, such as the organization and human resources, or to a subarea of supply chains, such as production logistics. The most comprehensive approaches are provided by the conceptual work of Warnecke, Wildemann, and Fey, as well as Smith and Ptak. In recent years, the platform Industry 4.0, acatech and Fraunhofer emphasize the importance of cyberphysical systems for such complexity management.

Warnecke uses his concept of the “fractal factory” to design a system for production logistics. The fractal structure offers a hierarchizing of the manufacturing organization and enables sufficient complexity in this area via decentralized control loops (Warnecke 1992). Wildemann offers the “modular factory” in response to the growing complexity of manufacturing. With his concept of manufacturing segmentation, he offers an organizational principle based on flexibility and process orientation for production (logistics). His concept implies complexity-increasing approaches, in particular in connection with the creation of structured networking and self-regulating, partially autonomous subsystems (Wildemann 1994). The implementation of Warnecke’s and Wildemann’s ideas are made possible by the use of cyberphysical systems in their pure variety, so that Warnecke’s and Wildemann’s reflections through the fourth industrial revolution experienced a sort of renaissance for production. From the point of view of supply chain practice, both approaches have been considered very important and relevant. The appreciation and interest of the supply chain practitioner in concepts such as the fractal factory and the modular factory prove that the desire for solutions for dealing with increasing complexity is very strong and increasing. In this respect, Warnecke and Wildemann set the central milestones for the development of complexity management in production. Both approaches offer principles that can also be rolled out to the entire supply chain.

Fey (1989) embeds his concept of integrated supply chain management in the company’s management system and derives development steps for supply chains (Kummer 1992). In doing so, he takes aspects of resilience into account as he sheds light on the development of supply chains from a traditional to a cross-sectional understanding. Fey concentrates his statements on the evolution of supply chain management. Like Warnecke and Wildemann, Fey also concentrates mainly on the area of production logistics.

In my publications of 1994 and 1997 (Wehberg 1994, 1997), I am pick up the aforementioned approaches of Warnecke, Wildemann and Fey, amongst others, to design a management concept for the complexity management of the entire supply chain and its resilience, respectively. In the first edition of this publication (Wehberg 2015), I considered the possibilities of Industry 4.0 and digitalization, respectively. By doing so, I sought to combine the principles of a demand-driven and resilient supply chain. This third edition considers further practical experience from another four years acting as a consultant in transforming supply chains (e.g. Wehberg 2018, 2018b; Wehberg/Berger 2018). This includes cooperation with technology providers such as SAP Leonardo and Siemens Mindsphere, in particular.

In 1996, Beckmann developed a prototyping approach for supply chain planning based on agile principles. Later on, the resilience of organizations was addressed by Bell (2002), amongst others, in light of the terrible attacks of 11 September 2001. In particular, resilient supply chains became a focus of interest, for example, by Christopher/Pack (2004). These publications focused on the vulnerability of supply chains in cases of unexpected events such as terrorist attacks. While the theoretical fundamentals of these approaches were not broadly elaborated, they primarily focused on risk mitigation. New opportunities generated by digital business models were not incorporated comprehensively.

Smith and Ptak (2011) then deployed the ideas of de-coupling, postponement, etc. to MRP-based standard software for supply chain planning, in terms of demand-driven material resource planning. Their concept helped to develop standard solutions in planning tools and the practical application of relevant concepts. A full leveraging of digitalization potentials, though, still has to be provided from both sides Smith’s and Ptak’s Demand Driven Institute, as well as respective vendors, which they are working on (e.g. SAP has introduced a demand-driven approach within its IBP tool set and others are working on it).

Among others, the Federal Government (through its Platform Industry 4.0 initiative, with the participati...