Identifying the causes of the bullwhip effect by exploiting control block diagram manipulation with analogical reasoning

Senior managers when solving problems commonly use analogical reasoning, allowing a current ‘target problem’ situation to be compared to a valid previous experienced ‘source problem’ from which a potential set of ‘candidate solutions’ may be identified. We use a single-echelon of the often-quoted Forrester (1961) production-distribution system as a case ‘target model’ of a complex production and inventory control system that exhibits bullwhip. Initial analogical reasoning based on ‘surface similarity’ would presuppose a classic control engineering ‘source model’ consisting of a phase-lag feedback system for which it is difficult to derive the transfer function. Simulation alone would have to be relied on to mitigate the bullwhip effect. By using z-transform block diagram manipulation, the model for a single-echelon, consisting of 17 difference equations with five feedback loops is shown to have exact analogy to Burns and Sivazlian’s (1978) second order system that has no feedback. Therefore, this more appropriate ‘source model’ is based on a deeper understanding of the ‘behavioural similarities’ which indicates that the bullwhip effect is not in the case of the ‘target model’ due to feedback control but due to a first-order derivative, ‘phase advance’, term in the feed forward numerator path. Hence a more appropriate 'candidate solution' can be found via the use of a 'recovery' filter. An interdisciplinary framework for exploiting control engineering block diagram manipulation, utilising analogical reasoning, in a practical setting is presented, as is an example in a contemporary supply chain setting

Extending a System for Measuring Dynamic Knowledge: Reconsidering Knowledge Flow Efficiency for Decision Support

Knowledge is key to competitive advantage, but it is inherently invisible, intangible and resistant to quantification, particularly when in dynamic motion. Recent research builds upon emerging knowledge measurement techniques and well-established knowledge flow theory to develop a system for measuring dynamic knowledge in the organization. Results from application to archetypical organization processes are highly consistent with extant theory. However, they also lead us to question some theoretic concepts and correspondences. In this article, we extend the measurement system and reconsider the effects of knowledge flow efficiency through dynamic measurement. We then illustrate how such extension establishes a novel decision support capability

Applying a System for Measuring Dynamic Knowledge: Reconsidering the Spiral Model

Knowledge is key to competitive advantage, but it is inherently invisible, intangible and resistant to quantification, particularly when in dynamic motion. Recent research builds upon emerging knowledge measurement techniques and well-established knowledge flow theory to develop a system for measuring dynamic knowledge in the organization. Results from application to archetypical organization processes are highly consistent with much theory. However, they also lead us to question some longstanding theoretic concepts and principles. In this article, we reconsider the well-known Spiral Model through dynamic knowledge measurement

Revisiting the whole systems approach: designing supply chains in a turbulent world

Purpose The systems approach is an exemplar of design science research (DSR), whereby specific designs yield generic knowledge. DSR is increasingly being adopted in logistics and operations management research, but many point to neglect of the human aspects of solutions developed. The authors argue that it is possible to look back at the history of the systems movement to seek precedent for ‘dealing’ with the social components, providing a methodologically pluralistic ‘research design’ framework. Thereby, systems approaches are foundational to providing a design-based ‘science’ to progressing the logistics and supply chain management field, dealing with contemporary topics such as resilience. Design/methodology/approach The authors undertake a discursive assessment of relevant streams of engineering, social science and systems research, with a conceptual development of how the latter influences supply chain design approaches. Findings Building on a phenomenological framework, the authors create a generic design science research design (DSRD) that enables researchers to choose and integrate the right tools and methods to address simple, complicated and complex problems, dealing with technological, process and social problems. Research limitations/implications The DSRD provides a framework by which to exploit a range of methodological stances to problem solving, including quantitative modelling perspectives and ‘soft’ systems social science approaches. Four substantive gaps are identified for future research – establishing the root cause domain of the problem, how to deal with the hierarchy of systems within systems, establishing appropriate criteria for the solution design and how best to deal with chaotic and disordered systems. Originality/value The authors argue that the systems approaches offer methodological pluralism by which a generic DSRD may be applied to enhance supply chain design. The authors show the relevance of the DSRD to supply chain design problems including in reducing supply chain dynamics and enhance resilience. In doing so, the study points towards an integrated perspective and future research agenda for designing resilient supply chains

Quality grading of returns and the dynamics of remanufacturing

Abstract: We consider a hybrid manufacturing/remanufacturing system where the returned products (cores) are classified into different quality grades. Each grade requires different remanufacturing operations and thus lead times. We examine the implications of the quality-grading scheme on the dynamic behavior of closed-loop supply chains, benchmarking this against a typical system where all the returns undergo the same remanufacturing process. Through control engineering techniques, we evaluate the Bullwhip and inventory performance of the supply chain by observing the step response of the orders and net stocks (the shock lens), analyzing the frequency behavior of these signals (the filter lens), and measuring their dynamics due to stochastic demand (the variance lens). Subsequently, we discuss the operational savings and additional costs derived from quality grading. We find that the pre-sorting mechanism allows for smoothing the supply chain operations; however, its impact on customer satisfaction is ambivalent. Indeed, we observe that the documented ‘lead-time paradox’ of the remanufacturing process in hybrid systems results here in a ‘quality paradox’: lower quality returns may increase the performance of inventories. This affects particularly low-frequency demands. Importantly, we analytically derive the optimal setting of the closed-loop pipeline estimation in order-up-to policies for avoiding long-term inventory drifts. This analysis reveals key potential benefits of information transparency for improving the operational performance, and thus the environmental and economic sustainability, of closed-loop supply chains

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