Supply chain dynamics and forecasting

Nowadays, the global supply chain system needs to respond promptly to changes in customer demand and adapt quickly to advancements in technology. Supply chain management becomes an integral approach which links together producers, distributors and customers in collaborative management of the whole system. The variability in orders or inventories in supply chain systems is generally thought to be caused by exogenous random factors such as uncertainties in customer demand or lead time. Studies have shown, however, that orders or inventories may exhibit significant variability, even if customer demand and lead time are deterministic. Most researchers have concentrated on the effects of the ordering policy on supply chain behaviour, while not many have paid attention to the influences of applying different forecasting to supply chain planning. This thesis presents an analysis of the behaviour of a model of a centralised supply chain. The research was conducted within the manufacturing sector and involved the breathing equipment manufacturer Draeger Safety, UK. The modelling process was embedded in the organization and was focused on the client's needs. A simplified model of the Draeger Safety, UK centralised supply chain was developed and validated. The dynamics of the supply chain under the influence of various factors: demand pattern, ordering policy, demand-information sharing, and lead time were observed. Simulation and analysis were performed using system dynamics, non-linear dynamics and control theory. The findings suggest that destructive oscillations of inventory could be generated by internal decision making practices. To reduce the variation in the supply chain system, the adjustment parameters for both inventory and supply line discrepancies should be more comparable in magnitude. Counter- intuitively, in certain fields of decision, sharing demand information can do more harm than good. The linear forecasting ARMA (autoregression and moving average) model and the nonlinear forecasting model Wavelet Neural Network were applied as the supply chain forecasting methods. The performance was tested against supply chain costs. A management microworld was developed, allowing managers to experiment with different decision policies and learn how the supply chain performs

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 survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Establishing a framework for the effective design of resilient supply chains with inherent non-linearities

Purpose of this paper: Previous control theory research on supply chain dynamics has predominantly taken a linear perspective of the real world, whereas nonlinearities have usually been studied via a simulation approach. Nonlinearities can naturally occur in supply chains through the existence of physical and economic constraints, for example, capacity limitations. Since the ability to flex capacity is an important aspect of supply chain resilience, there is a need to rigorously study such nonlinearities. Hence, the purpose of this paper is to propose a framework for the dynamic design of supply chains so that they are resilient to nonlinear system structures

The impact of freight transport capacity limitations on supply chain dynamics

We investigate how capacity limitations in the transportation system affect the dynamic behaviour of supply chains. We are interested in the more recently defined, 'backlash' effect. Using a system dynamics simulation approach, we replicate the well-known Beer Game supply chain for different transport capacity management scenarios. The results indicate that transport capacity limitations negatively impact on inventory and backlog costs, although there is a positive impact on the 'backlash' effect. We show that it is possible for both backlog and inventory to simultaneous occur, a situation which does not arise with the uncapacitated scenario. A vertical collaborative approach to transport provision is able to overcome such a trade-off. © 2013 Taylor & Francis

An evolutionary complex systems decision-support tool for the management of operations

Purpose - The purpose of this is to add both to the development of complex systems thinking in the subject area of operations and production management and to the limited number of applications of computational models and simulations from the science of complex systems. The latter potentially offer helpful decision-support tools for operations and production managers. Design/methodology/approach - A mechanical engineering firm was used as a case study where a combined qualitative and quantitative methodological approach was employed to extract the required data from four senior managers. Company performance measures as well as firm technologies, practices and policies, and their relation and interaction with one another, were elicited. The data were subjected to an evolutionary complex systems (ECS) model resulting in a series of simulations. Findings - The findings highlighted the effects of the diversity in management decision making on the firm's evolutionary trajectory. The CEO appeared to have the most balanced view of the firm, closely followed by the marketing and research and development managers. The manufacturing manager's responses led to the most extreme evolutionary trajectory where the integrity of the entire firm came into question particularly when considering how employees were utilised. Research limitations/implications - By drawing directly from the opinions and views of managers, rather than from logical "if-then" rules and averaged mathematical representations of agents that characterise agent-based and other self-organisational models, this work builds on previous applications by capturing a micro-level description of diversity that has been problematical both in theory and application. Practical implications - This approach can be used as a decision-support tool for operations and other managers providing a forum with which to explore: the strengths, weaknesses and consequences of different decision-making capacities within the firm; the introduction of new manufacturing technologies, practices and policies; and the different evolutionary trajectories that a firm can take. Originality/value - With the inclusion of "micro-diversity", ECS modelling moves beyond the self-organisational models that populate the literature but has not as yet produced a great many practical simulation results. This work is a step in that direction

The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains

We investigate the dynamics of a hybrid manufacturing/remanufacturing system (HMRS) by exploring the impact of the average return yield and uncertainty in returns volume. Through modelling and simulation techniques, we measure the long-term variability of end-product inventories and orders issued, given its negative impact on the operational performance of supply chains, as well as the average net stock and the average backlog, in order to consider the key trade-off between service level and holding requirements. In this regard, prior studies have observed that returns may positively impact the dynamic behaviour of the HMRS. We demonstrate that this occurs as long as the intrinsic uncertainty in the volume of returns is low —increasing the return yield results in decreased fluctuations in production, which enhances the operation of the closed-loop system. Interestingly, we observe a U-shaped relationship between the inventory performance and the return yield. However, the dynamics of the supply chain may significantly suffer from returns volume uncertainty through the damaging Bullwhip phenomenon. Under this scenario, the relationship between the average return yield and the intrinsic returns volume variability determines the operational performance of closed-loop supply chains in comparison with traditional (open-loop) systems. In this sense, this research adds to the still very limited literature on the dynamic behaviour of closed-loop supply chains, whose importance is enormously growing in the current production model evolving from a linear to a circular architecture