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 nonlinear dynamics on the resilience of a grocery supply chain

Purpose of this paper: In an effort to improve operational and logistical efficiencies, UK grocery retailers combined primary and secondary distribution increasing the importance of designing resilient replenishment systems in the distribution centre. This paper has the purpose to analyse the resilience performance of the distribution centre stock ordering system within a grocery retailer. Design/methodology/approach: A system dynamics approach is used for framing and building a credible representation of the real system. Mathematical analysis of the nonlinear model based on nonlinear control engineering techniques in combination with system dynamics simulation have been used to understand the behaviour of stock and shipment output responses in the distribution centre given step and periodic demand signals. Findings: Preliminary mathematical analysis through nonlinear control theory techniques has been undertaken in order to gain initial insights in the understanding of the replenishment control model. This practice allowed the researcher to identify specific behaviour change in the DC stock and shipment responses, which are key indicators for assessing supply chain resilience, without going through a time-consuming simulation process. Transfer function analysis and describing function serve as a guideline for undertaking system dynamics simulation. Value: This paper aims to fill the gap in the literature of supply chain resilience by using quantitative system dynamics methods to assess the resilience performance of a grocery retailer. In this way, we also supplement the literature with empirical data. Moreover, we explore different analytical methods since simulation is the predominant method for quantitative analysis of system dynamics. Research limitations/implications (if applicable): This research is limited to the dynamics of single-echelon supply chain systems. Although the EPOS sales data and the store replenishment system have been considered in the validation process, this study has focused on analysing the resilience performance of the DC replenishment system only. Considering the multi-echelon supply chain is intended for further research activities. Practical implications (if applicable): The findings suggest that the distribution centre replenishment system can be re-designed in order to improve the supply chain resilience performance. The ‘As Is’ scenario produces slow response of stock levels and inventory targets are never recovered due to a permanent offset

Improving demand forecasting in the air cargo handling industry: A case study

Air transportation plays a crucial role in the agile and dynamic environment of contemporary supply chains. This industry is characterized by high air cargo demand uncertainty, making forecasting extremely challenging. An in-depth case-study has been undertaken in order to explore and untangle the factors influencing demand forecasting and consequently to improve the operational performance of an Air Cargo Handling Company. It has been identified that in practice, the demand forecasting process does not provide the necessary level of accuracy, to effectively cope with the high demand uncertainty. This has a negative impact on a whole range of air cargo operations, but especially on the management of the workforce, which is the most expensive resource in the air cargo handling industry. Besides forecast inaccuracy, a range of additional hidden factors that affect operations management have been identified. A number of recommendations have been made to improve demand forecasting and workforce management

A technique to develop simplified and linearised models of complex dynamic supply chain systems

There is a need to identify and categorise different types of nonlinearities that commonly appear in supply chain dynamics models, as well as establishing suitable methods for linearising and analysing each type of nonlinearity. In this paper simplification methods to reduce model complexity and to assist in gaining system dynamics insights are suggested. Hence, an outcome is the development of more accurate simplified linear representations of complex nonlinear supply chain models.  We use the highly cited Forrester production-distribution model as a benchmark supply chain system to study nonlinear control structures and apply appropriate analytical control theory methods. We then compare performances of the linearised model with numerical solutions of the original nonlinear model and with other previous research on the same model.  Findings suggest that more accurate linear approximations can be found. These simplified and linearised models enhance the understanding of the system dynamics and transient responses, especially for inventory and shipment responses.  A systematic method is provided for the rigorous analysis and design of nonlinear supply chain dynamics models, especially when overly simplistic linear relationship assumptions are not possible or appropriate. This is a precursor to robust control system optimisation

The Rippling Effect of Non-linearities

Non-linearities can lead to unexpected dynamic behaviours in supply chain systems that could then either trigger disruptions or make the response and recovery process more difficult. In this chapter, we take a control-theoretic perspective to discuss the impact of non-linearities on the ripple effect. This chapter is particularly relevant for researchers wanting to learn more about the different types of non-linearities that can be found in supply chain systems, the existing analytical methods to deal with each type of non-linearity and future scope for research based on the current knowledge in this field

On the analysis of lead-time disturbances in production and inventory control models

Changes in the lead-time can lead to supply chain inefficiencies and risks. In this paper, we investigate the effects of lead-time disturbances on the system’s output responses of a production and inventory control model. In the adaption process of the control system for lead-time disturbance analysis, the resulting model becomes nonlinear. Hence nonlinear control theory in combination with simulation is used to analyse the impact of leadtime changes on the transient and steady state responses of order rate, inventory and work in process. Assuming constant customer demand, small perturbation theory is applied to linearise the model and to find the transfer functions relating the system’s outputs to the lead-time input. We find that the order rate, inventory and work in process transfer functions are input-dependent. In order words, the output responses depend on the input type, amplitude and direction of changes in the lead-time. When leadtime increases, the system has a relatively slow transient response and, as expected, work in process inventory levels increase and order rates are higher. However, step decreases in the lead-time can cause significant underdamped dynamics in the system. This work demonstrates that, although lead-time reduction is associated with service level improvement, increased flexibility and cost reductions, its implementation has to be carefully planned since a quick time compression may lead to undesirable oscillations in the supply chain system. In contrast, increased lead-times, associated say with a disturbance, yield slow recovery requiring adjustment of control parameters to increase resilience

IOBPCS based models and the customer order decoupling point

The inventory and order based production control system (IOBPCS) is mainly a model of a forecast driven production system where the production decision is based on the forecast in combination with the deviation between target inventory and actual inventory. The model has been extended in various directions by including e.g. WIP feedback but also by interpreting the inventory as an order book and hence representing a customer order driven system. In practice a system usually consists of one forecast driven subsystem in tandem with a customer order driven subsystem and the interface between the two subsystems is represented by information flows and a stock point referred to as the customer order decoupling point (CODP). The CODP may be positioned late, as in make to stock systems, or early, as in make to order systems, but in any case the model should be able to capture the properties of both subsystems in combination. A challenge in separating forecast driven from customer order driven is that neither one of inventory or order book should be allowed to take on negative values, and hence nonlinearities are introduced making the model more difficult to solve analytically unless the model is first linearized. In summary the model presented here is based on two derivatives of IOBPCS that are in tandem and interfaces related to where the demand information flow is decoupled and the positioning of the CODP