Supply Chain Processes Modelling for Digitalization: A Scientific Classification and Practical Recommendation Model

Supply Chain Processes Modelling for Digitalization: A Scientific Classification and Practical Recommendation Model

Matthias Lederer (ISM International School of Management, Germany), Anna Quitt (ISM International School of Management, Germany), Remzi Avci (University of Erlangen-Nuremberg, Germany) and Mario Büsch (ISM International School of Management, Germany)
DOI: 10.4018/978-1-5225-7700-3.ch014

Abstract

Process orientation is often seen as a central starting point for optimizing business performance. This is especially true for supply chain workflows because modeling, understood as specifying activities, data, resources, and control flows, is usually the first step in selecting and implementing digital approaches such as smart factories, virtual agents, and autonomous systems. Many different process notations are known (e.g., detailed vs. rough, flexible vs. standardized, etc.), and at the same time, fundamentally different SCM process types make various assumptions about suitable modeling (e.g., integration of value chain partners, standardization). This chapter presents an association model that suggests which notation fits best to which SCM type. The chapter discusses the benefits of specific notations for classical as well as modern challenges of SCM to help in the selection of notations. The resulting specifications serve as the basis for the holistic digitization of supply chains.
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Introduction

Nowadays, multiple fragmented (e.g., use of decision-making systems in individual steps such as supplier selection) or holistic (e.g., Efficient Consumer Response) digitization ideas for supply chains are discussed (Madjid, 2017). In most successful cases, data is used to implement better decisions and procedural improvements through digital information or virtually stored knowledge. In a first step, primarily routine tasks (e.g., interpreting invoices, ordering, desktop purchasing, tracking and tracing, etc.) were automated in supply chain. But with the ongoing megatrend of a digital world, even complex and extraordinary processes can be digitized (e.g., complex supplier negotiation using virtual agents, driverless transport systems in the warehouse, automatic goods receipt or exit control, neural networks for detecting critical points in a bill of material, etc.) (Brunet-Thornton and Martinez, 2018).

All these developments are accompanied by the digitization of processes. Modern business science and management practice stated for years that business challenges of SCM can be overcome by means of consistent and customer focused, i.e. value adding, process orientation (Page, 2016). Studies show that international markets, shorter lifecycles, and individual customer expectations can often only be handled with an internal enterprise configuration that thinks less in functions (e.g. sourcing department) than in workflows (e.g. production process) (Singh 2012; Aggarwal, 2004). With the availability of more sensors (e.g. from suppliers, within transportation medium, at the warehouse, at shipment) and advanced processing techniques (e.g., neural networks, generic algorithms, etc.), many approaches are being developed to digitize processes - SCM can greatly benefit from this (Nicoletti, 2016; Zhou and Chen, 2011). Supply chains (SC) in particular face the challenge of integrating several departments and coordinating multiple processes (Marchesini and Alcantara, 2016) and at the same time have to integrate data, methods and resources. Studies as well as individual case studies show clear evidences that positive effects within supply chain processes can only emerge when all required parties – internally as well as externally (e.g. suppliers, production, assembly, warehousing and sales) – are integrated into one fundamental process. Optimization, driven by individual departments or isolated target systems (e.g. focus on costs vs. customers), weaken digitalization initiatives for the entire supply chain (Vanathi and Swamynathan, 2016).

One of the essential prerequisites for digitization of supply chains is the ability to model underlying processes. This means that events, work steps, control flows and resources are described roughly or even specified in full detail. Resources include, for example, machines, people and parts as well as documents and information. Even in the classic BPM lifecycle, it is formulated that workflows are first modeled and then optimized (von Rosing, von Scheel and Scheer, 2014; Papajorgji 2013).

Two examples of the digitization of SCM processes will outline the need for modeling:

  • 1.

    Picking errors should be avoided in logistics. As part of the automation a quality control, the process is initially modeled. This is the only way to analyze in which steps and for which resources digital techniques can be used to avoid order picking errors. For example, a new software might rely on the continuous measurement of weights (e.g., unloading, transport, and packaging) in the processes. A comparison with the integrated database automatically detects errors.

  • 2.

    Industry 4.0 goes beyond the pure automation of known processes. Rather, it is about creating a smart factory that controls itself. For this, models are needed in all involved entities. This can be the product to be created (e.g., virtual model of the components), the necessary workflow (e.g., routes through the production lines), or background logistics (e.g., which events in processes indicate that parts need to be ordered).

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