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In industrial production, distributed control is perceived to be a promising approach for dealing with challenges arising from the increasing dynamical and structural complexity in that field. Currently, future production systems are envisioned to be digitalized and networked systems bearing names such as “Industry 4.0”, “Manufacturing 2.0”, “Internet of Things”, and many others. These visions share the idea of assigning tasks of production control to “intelligent” objects, such as machines, parts, and products, in order to attain higher flexibility, higher adaptability, and therefore a higher logistics performance. However, limited information and restricted computation capacity may have negative effects: The production system behavior depends on the decisions made by intelligent objects with individual and selfish systems of objectives. This can deteriorate both the stability and the quality of achieved production planning and control solutions. This trade-off situation has led researchers to the belief that a combination of centralized and distributed—as well as of local and global—decision making in control might be the key to an improved and stable logistics performance. However, little is known about the mechanics of these combined, semi-heterarchical control structures. Based on a profound literature review, we compare the approaches and insights from the different research domains in order to classify design decisions that can be made already in the planning phase of a new production system. We take a first step towards the validation of our classification by mapping four paradigms of distributed control into it. Our research can help the designers of future production systems to understand how to avoid the emergence of “myopic” behavior, and it may serve as a basis for creating new control approaches by exploring the design space.

The implementation of different types of knocked down (xKD) supply chains have been one main element of the internationalization strategies of automotive original equipment manufacturers (OEMs). The original plants in the industrialized home markets create handling kits that are imported by the oversea plants where the kits can be easily assembled in high quality despite lacking qualification of local workforce and without a local supplier base. Over time, the global manufacturing networks mature and the oversea plants increasingly overtake value-adding processes and integrate local suppliers. However, typically the supply chain management and design are not adapted to this maturing process and the supply chains are characterized by high inventories and long lead times. In this research, we develop an integrated framework to align the global manufacturing network and xKD supply chains in order to improve the logistics performance and to exploit cost potentials by means of a six-fold cross-case study of globally operating automotive OEMs.

This study investigates the impact of Industry 4.0 technologies on supply chain risks and examines which factors moderate this relationship. Drawing on a database of 300 Industry 4.0 projects all around Germany, the study explores empirically how Industry 4.0 technologies are employed to address various sources of various risks within production and supply chains. In this context, not only relevant technologies, existing risk factors and intended benefits are explored, but also factors that affect the relationship between these elements. Therefore, this paper shed light on the question which technologies are suitable and applicable for which user groups in which situations.

This research aims to highlight the benefits of sharing financial resources in supply chain networks. By modifying a resource sharing scheme that is used successfully by Eastern African Masaai tribes to support other herders in case of an unexpected loss of cattle to a resource sharing system for supply chains as described by Hao et al (2015). The so-called Osotua-based resource sharing model has proven to increase the overall cattle herd size and that is therefore favorable to support other and even rival herders in critical situations. We implement the Osotua-based resource sharing model into a bankruptcy propagation model based on Battiston et al. (2007). We can show that there is a substantial increase of the network’s overall health when using the Osotua-based supply chain resource sharing. Additionally, we explore the effects of the number of partners in a network willing to share resources as well as the effects of increasing network complexity.

Manufacturing systems exhibit two types of synchronization phenomena: logistics and physics. Quantifying synchronization measures for both types have been suggested and it has been shown that both types of synchronization are correlated to the logistics performance of the manufacturing system. Previous studies have indicated that structural properties of the manufacturing network might be central influencing factors triggering synchronization emergence. Synchronizability is a network property widely studied in the complex networks field. It is a common measure to evaluate the stability of synchronization in networks based on its structural properties. The aim of this paper is to explore synchronizability in manufacturing for the first time in order to better understand the impact of structural properties of a network on the emerging synchronization phenomena. We apply a multi-method investigation by triangulating a profound literature analysis, a two-stage discrete-event simulation study and an empirical data analysis of feedback data from industrial practice. Our findings show that synchronizability relates positively to physics and negatively to logistics synchronization. Based on these findings we present first considerations towards a synchronization-oriented design and control of manufacturing systems.

Manufacturing systems exhibit two types of synchronisation phenomena: logistics and physics. Previous research has established synchronisation measures for both types and has shown that they are related to the due date performance. However, there is a lack of knowledge about the factors triggering synchronisation emergence as well as a holistic understanding of synchronisation effects on logistics performance. Thus, this research aims to further explore the relation between synchronisation, its influencing factors and its effect on logistics performance. Based on a profound literature review, we derive first hypotheses on the cause-and-effect-relationships between structural and dynamic properties of a manufacturing system and the emergence of logistics and physics synchronisation as well as logistics performance. By conducting a discrete-event simulation study on diverse manufacturing system types (line, flow shop and job shop production), we are able to test these hypotheses. We conclude that manufacturing network architecture as a structural property as well as processing time variability and system workload as dynamic properties may be exploited for an advanced and synchronisation-oriented manufacturing system design.

Traditionally, the capacity dimensioning step within the manufacturing system design process takes a known and fixed distribution of production flow across path alternatives to derive capacity dema...

The term ‘synchronization’ in manufacturing refers to the provision of the right components to the subsequent production steps at the right moment in time. It is widely assumed that synchronization is beneficial to the logistics performance of manufacturing systems. However, it has been shown that synchronization phenomena can be detrimental to systems in which they emerge. To study if synchronization phenomena also occur in and affect manufacturing systems’ performance, a formal quantification and holistic understanding of the types of synchronization phenomena emerging in manufacturing are needed. This article aims to fill this research gap by developing synchronization measures for manufacturing systems, applying these measures to real-world production feedback data and utilising them to test the assumption about synchronization’s beneficial effect on logistics performance. We identify two distinct synchronization types occurring in manufacturing systems, logistics and physics synchronization, and show...

The scheduling of processes in a network is a core logistic challenge with a multitude of applications in our complex industrialized world. Often, scheduling decisions are based on incomplete and unreliable information. Here, a simple rule of ’more information, better decisions’ may no longer hold and heuristics balancing global and local information, or centralized and autonomous control, may yield better performance. So far, only anecdotal evidence for the potential benefit of autonomous control in scheduling exists. Here, we explore this hypothesis within a minimal model derived from scheduling principles and the phenomenology of dynamical processes on graphs. In this model, centralized and autonomous control can be represented and quantitatively assessed, performance is well defined and problem complexity can be varied. Our model shows that a balance of centralized and autonomous control can enhance the performance in networks of decision-making entities. The mechanistic insight gained from the model also reveals the limitations of hybrid control setups: We find that communication at a high hierarchy level can give an advantage to centralized control. Counter-intuitively, it arises not from a higher degree of coordination and quicker convergence towards a common solution, but rather from an accelerated sampling of candidate choices leading to a measurable increase in information flow from higher to lower hierarchical levels. Our study allows us to formulate a new view of autonomous control in industrial production and derive a set of suggestions with the potential to enhance performance under realistic conditions of scheduling heuristics of jobs in a production process.

Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future - Proceedings of the 48th CIRP Conference on Manufacturing Systems / Edited by Roberto Teti 48th CIRP Conference on Manufacturing Systems, CIRP CMS 2015, Ischia, Italy, 24 Jun 2015 - 26 Jun 2015; Amsterdam [u.a.] : Elsevier, Procedia CIRP 41, (2016). doi:10.1016/j.procir.2015.08.105, Published by Elsevier, Amsterdam [u.a.]

The aim of production planning and control is to ensure the achievement of the logistic targets of high due date reliability, low lead times, high capacity utilization, and low WIP levels, while maintaining productivity and quality targets. If order due dates are missed, a common intuitive reaction of production planners is to adjust planned lead times. How often and to what extent updates are reasonable has previously been unclear because, while trying to improve the logistic target achievement, planned lead time adjustments may actually cause an opposite effect, which is known as the Lead Time Syndrome (LTS) of Manufacturing Control [1] . Previous research on the LTS interactions has shown that the line of argumentation of the LTS is valid [2] . Knollmann et al. showed by means of mathematical modeling, control-theoretic simulation and case study research that planned lead time adjustments lead to a short-term increase in lead time variation, thus to an increase in lateness variation and to a decrease in due date reliability [2] , [3] , [4] , [5] . The authors suggest to choose update frequency depending on the ratio of latency period and the update frequency (the period between two consecutive adjustments) as the misbalance of these two parameters turns out to be the main trigger of the LTS. Selcuk investigated the LTS by means of queuing theory in an independent approach [6] , [7] , [8] . The authors concluded that planned lead time adjustments lead to an increase in process variability, thus to high WIP levels and long lead times. However, they suggest to reduce update frequency, to decrease process variability and thus to avoid LTS. This conclusion is not in line with the conclusions drawn from the research presented by Knollmann et al.. Therefore, this paper compares the different research approaches methodologies and discusses how the different research methodologies impact the conclusions drawn for practice application. This comparison provides further insights into LTS research and indicates further research fields.

Manufacturers continuously face the challenge of driving down costs while being subjected to increasingly globalized market pressures to shorten production lead times and increase delivery reliability. The early prediction of the expected logistics performance of single production orders as well as for the entire manufacturing system is a pivotal strategic corporate activity. However, companies frequently find themselves struggling to foresee and integrate operational dynamic effects related to production orders into production planning decisions. Such dynamic interdependency effects between orders in close temporal and spatial neighbourhoods can have an impact on logistics performance. In this research, we introduce an index measure that quantifies the spatial and temporal relation of production orders and investigate dynamic effects of production order interdependencies using real production feedback data and derive first results for the improvement of the prediction of logistics performance in an early production planning stage as well as for the configuration of production planning and control.

Adjusting planned lead times when order due dates are missed is an intuitive reaction, but these adjustments can be counterproductive when inherent order lead time instability endangers achievement of logistic targets. In this paper, dynamic models are used to characterize lead time instability in two approaches to work system lead time adjustment. Minimum damping is calculated as a function of lead time adjustment parameters. Industrial data are used to illustrate how the approach provides guidance in selecting production planning and control parameters, with the goal of ensuring stability as a key dynamic foundation for satisfying work system logistic performance targets.

The future of manufacturing and logistics is currently envisioned under many names: Industry 4.0, Manufacturing 2.0, Physical Internet, etc. They share the vision of distributing control tasks to “smart” machines and products to attain higher flexibility, adaptability, and, in the light of increasingly complex and dynamic environmental conditions, higher logistic performance. The flip-side of such systems under distributed control is the rise of “myopic” (short-sighted) decision making, leading to system nervousness and loss of performance. Designing manufacturing (control) systems for distributed control hence is a significant challenge: With the system performance becoming an emergent property of the interplay of various decision making entities, system designers become conductors of societies of cyber-physical systems, seeking to balance the desirable traits of distributing control while limiting the negative effects of myopic decision making. In this contribution, we set out to help manufacturing system designers to better understand myopic behavior and the design decisions that are known to affect it. Our contribution can serve as a design aid for planners of distributed control systems by structuring the solution space of design decisions to control myopic behavior. By pointing to examples from various research streams, we provide guidance for system designers, seeking to maximize the performance of distributed production control systems.

Capacity dimensioning in production systems is an important task within strategic and tactical production planning which impacts system cost and performance. Traditionally capacity demand at each worksystem is determined from standard operating processes and estimated production flow rates, accounting for a desired level of utilization or required throughput times. However, for distributed production control systems, the flows across multiple possible production paths are not known a priori. In this contribution, we use methods from algorithmic game-theory and traffic-modeling to predict the flows, and hence capacity demand across worksystems, based on the available production paths and desired output rates, assuming non-cooperative agents with global information. We propose an iterative algorithm that converges simultaneously to a feasible capacity distribution and a flow distribution over multiple paths that satisfies Wardrop's first principle. We demonstrate our method on models of real-world production networks.

Elementary flux modes (EFMs) are a concept from Systems Biology, where they serve as an indicator of component relevance in metabolic networks. An elementary flux mode is a functionally relevant, non-decomposable path through a given network. In this paper, we apply elementary flux mode analysis to manufacturing systems, with the aim of using the number of EFMs as a predictor for resource significance in the manufacturing system. For this, we formulate a network representation of a manufacturing process, which allows us to define the manufacturing equivalent of a stoichiometric matrix to draw an analogy between metabolic and manufacturing systems. This, in turn, allows the computation of EFMs, which we conduct in a case-study for a real manufacturing system. We further show that the change of EFMs under resource breakdown is a good indicator of the average order lateness in the manufacturing system. In this way, EFMs provide insight into the relationship of network structure and function in manufacturing.

The term “synchronization” in manufacturing refers to the provision of the right components to the subsequent production steps at the right moment in time. It is still unclear how manufacturing system characteristics impact synchronization. Thus, the purpose of this paper is to investigate the effect of manufacturing systems’ characteristics on the emergence of logistics synchronization in them. We conduct a discrete-event simulation study to examine the effect of three system characteristics: (1) material flow network architecture, (2) work content variation, and (3) order arrival pattern. Our findings suggest that the material flow network architecture and the work content variation are related to logistics synchronization. Linear manufacturing systems with stable processing times such as flow shops operate at high logistics synchronization levels, while highly connected systems with high variability of processing times such as job shops exhibit lower synchronization levels.

Synchronization as a dynamic process has found applications in many fields. However, it remains unclear how this phenomenon relates to manufacturing systems. The aim of this study is to investigate the conditions for emergence of synchronization and its effects on the wide spectrum of production logistics performance objectives. Using queueing theory as the underlying methodology for deductive modeling of manufacturing systems, we run computer simulations on networks of queueing systems and investigate synchronization measurements in relation to system parameters and performance indicators. Our initial findings suggest that different types of manufacturing systems display different synchronization behaviors and that periodically driven systems with deterministic arrival and service rates display higher synchronization in comparison to stochastic ones. Further, we show that intrinsic physics synchronization is correlated to capacity utilization, throughput times and WIP levels, suggesting the co-activity of operations is related to highly utilized systems, while external physics synchronization is anticorrelated to throughput times and WIP levels, suggesting that higher efficiencies emerge with workstation repetitive behavior.