Volker Stich

High resolution supply chain management: optimized processes based on self-optimizing control loops and real time data

The efficient dealing with the dynamic environment of production industries is one of the most challenging tasks of Supply Chain Management in high-wage countries. Relevant and current information are still not used sufficiently, to handle the influence of the dynamic environment on intra- and inter-company order processing adequately. Among other things, the problem is caused by missing or delayed feedback of relevant data. As a consequence of that, planning results differ from the actual situation of production. High Resolution Supply Chain Management describes an approach aiming on high information transparency in supply chains in combination with decentralized, self-optimizing control loops for Production Planning and Control. The final objective is to enable manufacturing companies to produce efficiently and to be able to react to order-variations at any time, requiring process structures to be most flexible.

Produktion am Standort Deutschland

Kurzfassung In diesem Beitrag werden Ergebnisse der Studie „Produktion am Standort Deutschland” vorgestellt, in der relevante Faktoren zur nachhaltigen Wettbewerbs- und Standortsicherung identifiziert werden konnten. Zu den Wettbewerbsfaktoren für produzierende Unternehmen in Deutschland zählen u.a. eine integrierte Unternehmenssoftware, eine echtzeitfähige Datenverarbeitung sowie die Gestaltung wandlungsfähiger Produktionssysteme.

Produktionsplanung und -steuerung in Logistiknetzwerken

Ein standig wachsender Preisdruck und immer individuellere Kundenauftrage sind nur zwei Kennzeichen der industriellen Produktion im europaischen Wirtschaftsraum. Gerade in Deutschland ansassige Unternehmen konnen im internationalen Wettbewerb in den wenigsten Fallen allein aufgrund des Produktpreises konkurrenzfahig bleiben. Stattdessen bauen diese Unternehmen ihre Wettbewerbsvorteile anderweitig aus und verfolgen vielmehr eine konsequente Kundenorientierung, hohe Logistikleistung oder Prozessbeherrschung (Arnold 2000; Schuh u. Westkamper 2006; Wiendahl et al. 2006; Schonsleben 2007). In diesem Umfeld setzten zahlreiche Unternehmen bereits fruhzeitig auf eine Reduzierung ihrer Wertschopfungstiefe und verlagerten verschiedene Entwicklungs- oder Produktionsschritte auf andere Unternehmen mit komplementaren Kompetenzen. Damit ruckte die uberbetriebliche Zusammenarbeit bzw. Koordination der Auftragsabwicklung entlang einer mehrstufigen Lieferkette oder innerhalb eines polyzentrischen Unternehmensnetzwerks zunehmend in den Mittelpunkt betrieblicher Anstrengungen (Nyhuis et al. 2005; Wiendahl 2005; Bretzke 2006; Nyhuis u. Wiendahl 2007; Schuh u. Schmidt 2007). So gilt es also heute, in Netzwerkstrukturen zu denken, diese ganzheitlich zu gestalten und effizient zu organisieren.

Cognition-Enhanced, Self-optimizing Production Networks

This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.

High Resolution Supply Chain Management – Resolution of the Polylemma of Production by Information Transparency and Organizational Integration

High Resolution Supply Chain Management (HRSCM) aims to stop the trend of continuously increasing planning complexity. Today, companies in high-wage countries mostly strive for further optimization of their processes with sophisticated, capital-intensive planning approaches [3]. The capability to adapt flexibly to dynamically changing conditions is limited by the inflexible and centralized planning logic. Thus, flexibility is reached currently by expensive inventory stocks and overcapacities in order to cope with rescheduling of supply or delivery. HRSCM describes the establishment of a complete information transparency in supply chains with the goal of assuring the availability of goods through decentralized, self-optimizing control loops for Production Planning and Control (PPC). By this HRSCM pursues the idea of enabling organizational structures and processes to adapt to dynamic conditions. The basis for this new PPC Model are stable processes, consistent customer orientation, increased capacity flexibility and the understanding of production systems as viable, socio-technical systems [1, 2].

Big Data Technology for Resilient Failure Management in Production Systems

Due to a growing complexity within value chains the susceptibility to failures in production processes increases. The research project BigPro explores the applicability of Big Data to realize a pro-active failure management in production systems. The BigPro-platform complements structured production data and unstructured human data to improve failure management. In a novel approach, the aggregated data is analyzed for reoccurring patterns that indicate possible failures of the production system, known from historic failure events. These patterns are linked to failures and respective countermeasures and documented in a catalog. The project results are validated in three industrial use cases.

The implementation of game based learning as part of the corporate competence development

Game based learning is still not widely accepted for work based learning purposes. Though game based learning has been integrated into formal education in school and university, corporate human resource departments still rely on more traditional learning approaches, since computer games are often seen as more of an opposite to work processes. In this paper an implementation guideline for the integration of game based learning into the corporate competence development is established. Starting from an extensive literature research the concept presented in this paper has been validated in expert workshops with managers and human resource experts. It was examined in how far game based learning could contribute to the vocational work based training and which steps need to be undertaken to apply game based learning. Starting from the theoretical concepts of motivation theory the main design options in creating instructional games are analyzed. In addition to that a classification of different types of games in relation to their corporate application fields has been developed. The authors recommend increasing the corporate use of game based learning as part of the vocational work based learning to reach better the future qualification requirements of companies as well as the training of less education experienced workers. A structured implementation process is suggested and a matching concept of game design in relation to the work tasks is presented.

Cyber Physical Production Control

Currently the control of constantly increasing market dynamics and the simultaneously increasing individualization of process chains represent the central challenges for manufacturing companies. These challenges are caused by a lack of transparency in production planning, non-real-time processing of data as well as poor communication between the planning and control level. The research project ProSense addresses this problem and intends to eliminate the current problems in production by developing a high-resolution, adaptive production control based on cybernetic support systems and intelligent sensors. Through the development of a cyber-physical production control as one part of the project, which forms the basis for an innovative self-optimizing advanced planning system, ProSense provides a contribution to accomplish the goals of industry 4.0.

A Cybernetic Reference Model for Production Systems Using the Viable System Model

Designing viable and integrative production systems is challenging for big companies. Researchers often fail to holistically consider the production system. Thus, the aim of this paper is to propose a holistic approach how the supply chain, the production and shop floor planning intermesh. Hereby a Viable System Model was applied. Standardized communication channels were able to be defined among three entities. In conclusion this newly proposed approach enables companies to reduce necessary stocks, production lead times and manpower allocation. This proposed approach boosts the efficiency of all production planning processes. This in turn translates to decreased stocks, shorter lead times and to more efficient manpower allocation. This holistic approach is a key to success for companies, in particular, in high-wage countries.

Big data implementation for the reaction management in manufacturing systems

Big Data is one of the most discussed trend themes worldwide in both research community and industrial practice. Thus, researchers as well as company representatives focus on the study of Big Data technologies and the potentials deriving from gaining and structuring data. In this paper, we firstly conduct a literature review. Then, we describe the approach to implement reaction management in manufacturing environment before we finally report on a research project aiming at applying Big Data technologies in producing companies. The project goal is to develop a real-time capable platform in consideration of industrial requirements. With the help of a Big Data platform, producing companies will be enabled to use several applications like e.g. monitoring, prognosis or reaction. By receiving appropriate measures defined in the platform, the ability of producing companies to detect and to react proactively to failures deriving in manufacturing will sustainably increase.