The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

A Critical Evaluation Of Empirical Non-Linear Control System And System Dynamics Modeling Theories For Mitigating Risks Arising From Bullwhip Effect

Bullwhip effect is a threat observed in multi-echelon supply chains, which is one of the prominent indicators of inefficiencies in a supply chain. Primarily, bullwhip effect occurs as a result of disruptions in information and materials flow, lead-time delays, lack of coordination, and panic stocking amidst visibility into local risk factors. When bullwhip effect occurs, the demand variations entering the supply chain from the customer end amplifies gradually as it flows upstream towards the supplier ends. This may cause unused inventory and may later lead to wastage and obsolescence. Bullwhip effect can be curbed through many approaches. This study has focused on control theory approach that promotes small-scale control behaviors throughout the supply chain to dampen the bullwhip tidal waves. The approach investigated in this research is a combination of control system modeling and systems dynamics modeling, which is not researched adequately by bullwhip academics. Based on the investigations, a six-step approach for reducing Bullwhip effect is proposed in this research and illustrated with examples. The six-step approach comprises of first-level multi-echelon survey to derive the initial system dynamics model, second-level survey to collect primary data for all the variables and relationships formed, principal component analysis and Cronbach Alpha / split-half testing for reliability, verification, and validity testing and exploring the best optimal construct using structural equation modeling, and finally, applying controllers to the optimal systems dynamics model through interpretive analysis of the model

Factors causing reversed bullwhip effect on the supply chains of Kenyan firms

The study sought to determine the factors that cause supply variability along the supply chain of organisations. The study location was Kenya Pipeline Company, Kenya where from a population of 7 depots, purposive sampling was used to select a sample of 5 depots. Data was collected through the use of questionnaires with both open and closed ended questions to capture the qualitative and quantitative characteristics of the pipeline operations. Descriptive survey and a case study research design that encompasses both quantitative and qualitative methods to collect and analyse data were utilized. The findings suggested that capacity constraint was the major factor contributing to supply chain inefficiency.The conclusion was that the supply chain was inefficient because of capacity challenges and government intervention. Recommendations included capacity adjustment strategies, equipment upgrade, additional man and machine hours, reliable source of power and a non-disruptive government intervention. Keywords: Reverse bullwhip, variability, supply chain, Keny

The bullwhip effect: Progress, trends and directions

This is the final version. Available on open access from Elsevier via the DOI in this recordThe bullwhip effect refers to the phenomenon where order variability increases as the orders move upstream in the supply chain. This paper provides a review of the bullwhip literature which adopts empirical, experimental and analytical methodologies. Early econometric evidence of bullwhip is highlighted. Findings from empirical and experimental research are compared with analytical and simulation results. Assumptions and approximations for modelling the bullwhip effect in terms of demand, forecast, delay, replenishment policy, and coordination strategy are considered. We identify recent research trends and future research directions concerned with supply chain structure, product type, price, competition and sustainability

On the dynamics of closed-loop supply chains with capacity constraints

In this paper, we investigate the dynamic behavior of a closed-loop supply chain with capacity restrictions both in the manufacturing and remanufacturing lines. We assume it operates in a context of a twofold uncertainty by considering stochastic demand and return processes. From a bullwhip perspective, we evaluate how the four relevant factors (specifically, two capacities and two sources of uncertainty) interact and determine the operational performance of the system by measuring the variability of the manufacturing and remanufacturing lines and the net stock. Interestingly, while the manufacturing capacity only impacts on the forward flow of materials, the remanufacturing capacity affects the dynamics of the whole system. From a managerial viewpoint, this work suggests that capacity constraints in both remanufacturing and manufacturing lines can be adopted as a fruitful bullwhip-dampening method, even if they need to be properly regulated for avoiding a reduction in the system capacity to fulfill customer demand in a cost-effective manner

Methodological approach to study the dynamics of production networks: Discrete-event simulation modelling

This paper shows how discrete-event simulation represents an appropriate tool for approaching the dynamics of production networks. Three important factors influencing production network dynamics, specifically finite production capacity, manufacturing lead time, and its variability are discussed and a basic discrete-event simulation model is presented. Such model, which in its basic form represents a simple retail/distribution two-stage supply chain, is then extended in order to take into account those factors that can not be included in a classical control theoretical model

The value of sharing planning information in supply chains

Purpose - The development of information technology has made it possible for companies to get access to information about their customers’ future demand. This paper outlines various approaches to utilize this kind of visibility when managing inventories of end products on an operative level. The purpose is to explain the consequences, for capital tied up in inventory, of sharing four different types of planning information (point-of-sales data, customer forecasts, stock-on-hand data, planned orders) when using re-order point (R,Q) inventory control methods in a distribution network. Design/methodology/approach - A simulation study based on randomly generated demand data with a compound Poisson type of distribution is conducted. Findings - The results show that the value of information sharing in operative inventory control varies widely depending on the type of information shared, and depending on whether the demand is stationary or not. Significantly higher value is achieved if the most appropriate types of information sharing are used, while other types of information sharing rather contribute to decreased value. Sharing stock-on-hand information is valuable with stationary demand. Customer forecast and planned order information are valuable with non-stationary demand. The value of information sharing increases when having fewer customers, and when the order quantities are large. Sharing point-of-sales data is not valuable, regardless of the demand type. Research limitations/implications - The use of simulation methodology is a limitation, because the study has to be limited to a specific model design, and because it is not based on primary empirical data. The study is especially limited to dyadic relationships in supply chains, and to distribution networks with a rather limited number of customers. Practical implications - Guidance is given about what type of information should be appropriate to share when different types of demand patterns and distribution networks, and how order batch sizes and lead times affect the value of information sharing when using re-order point (R,Q) methods. Originality/value - Very limited research providing specific assessments of potential inventory control consequences when sharing planning information in various contexts has been found in the literature. The findings and conclusions should also be valuable for the supply chain integration and collaborative planning literature

Information to share in supply chains dedicated to the mass production of customized products for decentralized management

In an upstream supply chain dedicated to the mass production of customized products, decentralized management can be an efficient and effective method in a steady state in which stochastic characteristics of customers' demands remain stable. However, this is possible only if all echelons that precede the final assembly line use periodic replenishment policies that restrain the stockout risk to a low predetermined probability. The safety stocks' levels are more difficult to define for alternative or optional parts, as well as the components they use, whose demands are weighted sums of random variables, affected by several random factors and organizational constraints. The factors and constraints to consider are not the same for supplied and produced components. The random demand of a component depends on the demand of alternative or optional parts mounted in the final product, through a double transformation involving the bill of materials explosion, which is at the origin of the weighted sum of random variables, and time lags. In the steady state, the knowledge of the probability distribution of that random variable allows for the determination of safety stocks that decouple the management of upstream supply chains. Progressive changes in the steady state require periodic and progressive adaptations of the safety stocks that do not directly depend on the final demand knowledge

The causes and determination of safety stocks in upstream supply chains for mass production of customized products

In an upstream supply chain dedicated to the mass production of customized products, many sources create production instability: the level and structure of production in the final assembly line, variability of lead times, quality issues, packaging and loading constraints on transportation, demand anticipation, and the synchronization of the flows of components sent, received, and produced. For periodic replenishment systems, each member of the supply chain must have two different safety stocks to prevent some sources of fluctuations: a safety stock of produced components to meet the demand of downstream links and a safety stock of supplied components to ensure its own production. Procedures must take the organizational framework of information and products exchanges into account. The relevance of supply and production rules depends on the relevance of structural information broadcast along the supply chain