Supply Chain Management and Management Science: A Successful Marriage

The last century has witnessed extant studies on the applications of Management Science (MS) to a diverse set of Supply Chain Management (SCM) issues. This paper provides an overview of the contribution of MS within SCM. A framework is developed in this paper with a sampling of MS contributions to major SCM dimensions. Future research directions are presented

Supply chain integration strategies in fast evolving industries

Purpose - The purpose of this paper is to define the "fast evolving industry" (FEI) and its supply chain management (SCM) challenges. The authors review and structure the literature regarding integration strategies and implementation methods to develop a strategic decision-making framework for SCM in the FEI. Design/methodology/approach - The authors conduct a review of SCM literature, including supply chain strategy, supply chain integration (SCI), agile and responsive supply chain and SCM for innovative and fast-changing industries. The authors develop a conceptual model and a decision-making framework and use four mini cases to provide support for the model and framework. Findings - The FEI, characterised by a high level of innovation and differentiation, short products/services lifecycle and high variety, is yet to be fully defined. Inherent uncertainty in FEI supply systems makes SCM in these industries a complex but strategic task for their managers. The framework and the model offered in this study, which employ a core competency concept and provide risk management strategies, offer a strategic tool for managers and scholars in the field to optimise their integration strategies and to operationalise integration decisions. Originality/value - Little research has been published on transferable and cross-industrial SCM in FEIs. This paper defines the FEI and its resource-related concerns and then offers a conceptual model and a strategic decision-making framework for SCI in FEIs

System dynamics modelling, analysis and design of assemble-to-order supply chains

Background and purpose: The assemble-to-order supply chains (ATO) is commonly-adopted in personal computer (PC) and semiconductor industries. However, the system dynamics of PC and semiconductor ATO systems, one of the main sources of disruption, is not well-explored. Thereby this thesis aims to 1) develop a nonlinear system dynamics model to represent the real-world PC and semiconductor ATO systems, 2) explore the underlying mechanisms of ATO system dynamics in the nonlinear environment and 3) assess the delivery lead times dynamics, along with bullwhip and inventory variance. Design/methods: Regarding the semiconductor industry, the Intel nonlinear ATO system dynamics model, is used as a base framework to study the underlying causes of system dynamics. The well-established Inventory and Order based Production Control System archetypes, or the IOBPCS family, are used as the benchmark models. Also, the IOBPCS family is used to develop the PC ATO system dynamics model. Control engineering theory, including linear (time and frequency response techniques) and nonlinear control (describing function, small perturbation theory) approaches, are exploited in the dynamic analysis. Furthermore, system dynamics simulation is undertaken for cross-checking results and experimentation. Findings: The ATO system can be modelled as a pull (order driven) and a push (forecasting driven) systems connected by the customer order decoupling point (CODP). A framework for dynamic performance assessment termed as the ‘performance triangle’, including customer order delivery lead times, CODP inventory and bullwhip (capacity variance), is developed. The dynamic analysis shows that, depending on the availability of CODP Abstract iii inventory, the hybrid ATO system state can be switched to the pure push state, creating poor delivery lead times dynamics and stock-out issues. Limitations: This study is limited to the analysis of a closely-coupled two-echelon ATO systems in PC and semiconductor industries. Also, the optimization of control policies is not considered. Practical implications: Maintaining a truly ATO system state is important for both customer service level and low supply chain dynamics cost, although the trade-off control design between CODP inventory and capacity variance should be considered. Demand characteristics, including variance and mean, play an important role in triggering the nonlinearities present in the ATO system, leading to significant change in the average level of inventory and the overall transient performance. Originality / value: This study developed system dynamics models of the ATO system and explored its dynamic performance within the context of PC and semiconductor industries. The main nonlinearities present in the ATO system, including capacity, non-negative order and CODP inventory constraints, are investigated. Furthermore, a methodological contribution has been provided, including the simplification of the high-order nonlinear model and the linearization of nonlinearities present in the ATO system, enhancing the understanding of the system dynamics and actual transient responses. The ‘performance triangle’ analysis is also a significant contribution as past analytical studies have neglected customer order lead time variance as an inclusive metric

Supply chain integration strategies in fast evolving industries

Purpose – We aim to define the ‘fast-evolving-industry’ (FEI) and its supply chain management (SCM) challenges. We review and structure the literature regarding integration strategies and implementation methods to develop a strategic decision-making framework for SCM in the FEI. Design/methodology/approach – We conduct a review of SCM literature, including supply chain strategy, supply chain integration (SCI), agile and responsive supply chain and SCM for innovative and fast-changing industries. We develop a conceptual model and a decision-making framework and use four mini cases to provide support for the model and framework. Findings – The FEI, characterised by a high level of innovation and differentiation, short products/services lifecycle and high variety, is yet to be fully defined. Inherent uncertainty in FEI supply systems makes SCM in these industries a complex but strategic task for their managers. The framework and the model offered in this study, which employ a core competency concept and provide risk management strategies, offer a strategic tool for managers and scholars in the field to optimise their integration strategies and to operationalise integration decisions. Original Value – Little research has been published on transferable and cross-industrial SCM in Fast Evolving Industries (FEIs). This paper defines the FEI and its resource-related concerns and then offers a conceptual model and a strategic decision-making framework for SCI in FEIs

Delivery time dynamics in an assemble-to-order inventory and order based production control system

System dynamics play a critical role in influencing supply chain performance. However, the dynamic property of the assemble-to-order (ATO) system remain unexplored. Based on control theory, the inventory and order based production control system (IOBPCS) family, can be utilized as a base framework for assessing system dynamics. However, the underlying assumption in traditional IOBPCS-based analytical studies is that the system is linear and the delivery time to end customers is negligible or backlog is used as a surrogate indicator. Our aim is to incorporate customer delivery lead-time variance as the third assessment measure alongside capacity availability and inventory variance as part of the so-called ‘performance triangle’– capacity at the supplier, the customer order decoupling point (CODP) inventory and the delivery lead-time. Using the ‘performance triangle’ and adopting non-linear control engineering techniques, we assess the dynamic behaviour of an ATO system in the electronics sector. We benchmark the ATO system dynamics model against the IOBPCS family. We exploit frequency response analysis to ensure a robust system design by considering three measures of the ‘performance triangle’. The findings suggest delivery LT variance can be minimised by maintaining the ATO system as a true Push-Pull hybrid state with sufficient CODP stock, although increased operational cost driven by bullwhip and CODP variance need to be considered. However, if the hybrid ATO system 'switches' to the pure Push state, the mean and variance of delivery LT can be significantly increased

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research