The value of nonlinear control theory in investigating the underlying dynamics and resilience of a grocery supply chain

In an empirical context, a method to use nonlinear control theory in the dynamic analysis of supply chain resilience is developed and tested. The method utilises block diagram development, transfer function formulation, describing function representation of nonlinearities and simulation. Using both ‘shock’ or step response and ‘filter’ or frequency response lenses, a system dynamics model is created to analyse the resilience performance of a distribution centre replenishment system at a large grocery retailer. Potential risks for the retailer’s resilience performance include the possibility of a mismatch between supply and demand, as well as serving the store inefficiently and causing on-shelf stock-outs. Thus, resilience is determined by investigating the dynamic behaviour of stock and shipment responses. The method allows insights into the nonlinear system control structures that would not be evident using simulation alone, including a better understanding of the influence of control parameters on dynamic behaviour, the identification of inventory offsets potentially leading to ‘drift’, the impact of nonlinearities on supply chain performance and the minimisation of simulation experiments

A methodology for benchmarking replenishment‐induced bullwhip

Purpose – The aim of this article is to provide a concise methodology for the design of a widely used class of decision supply systems (DSS) which will enable precise control of bullwhip variance and inventory variance induced within a supply chain echelon. Design/methodology/approach – The study exploits recent research that derived analytical formulae for calculating these performance metrics germane to the delivery process when the demand is randomly varying about a constant mean value. These formulae have been verified via extensive simulation-based cross-checks. Findings – The design methodology focuses on the specification of bullwhip variance as an input. The output is to identify combinations of parameter settings to meet this target. Hence these parameters may be mapped to provide a visual display of competing designs with their associated inventory variance. Research limitations/implications – Although the analytical solutions apply only to the case where the pipeline error and inventory error correction terms are equal, this is not a severe limitation. Both theoretical studies of dynamic response and industrial experience support this feedback gain equally as enabling good practice. Practical implications – Design of this particular DSS to control bullwhip is now greatly simplified, and guaranteed via extensive verification tests. The formulae are equally sound as a means of establishing system robustness. Originality/value – The methodology is unique in enabling transparency of both bullwhip variance and inventory variance computation. Not only are system design time saved and normal performance guaranteed, but considerable management insight is generated thereby

Vendor‐managed inventory and bullwhip reduction in a two‐level supply chain

Compares the bullwhip properties of a vendor managed inventory (VMI) supply chain with those of a traditional “serially-linked” supply chain. The emphasis of this investigation is the comparative impact the two structures have on the “bullwhip effect” generated. Particular attention is paid to the manufacturer's production ordering activities as demonstrated using a simulation model based on difference equations. Documents and considers each of the four important sources of the bullwhip effect in turn. The analysis shows that with VMI implementation two sources of the bullwhip effect may be completely eliminated, i.e. rationing and gaming or the Houlihan effect, and the order batching effect or the Burbidge effect. VMI is also significantly better at responding to rogue changes in demand due to the promotion effect or to price induced variations. However, the effect of VMI on demand signal processing induced bullwhip or the Forrester effect is less clear cut. Concludes that on balance VMI offers a significant opportunity to reduce the bullwhip effect in real-world supply chains

Dynamic simulation modelling for lean logistics

The Law of Industrial Dynamics ensures that if a production control system can amplify then it will surely find a way of doing so despite the best efforts of production schedulers to take corrective action. In fact, practical studies show that such human intervention frequently aggravates the situation with both stock levels and order rates fluctuating alarmingly. The solution is to design an effective system via simulation. This requires the selection of the appropriate control system structure, agreement on the test cases to be used to mimic the operating environment, and finally setting the system parameters to achieve best performance for this scenario. Demonstrates a system which has three controllers utilizing sales, inventory and work in progress (WIP) data to set production order rates. The resulting decision support system (DSS) is a generic tool that can be used by production schedulers with confidence in the knowledge that the Law of Industrial Dynamics effects may be minimized. Simulation experiments can determine the best available trade-off in any particular situation such as achieving the lean logistics aim of minimum reasonable inventory (MRI) while retaining high customer service levels (CSL). The experimental facility available within the simulation model includes provision for assessing the impact of variable production lead times and information delays on system performance. Describes a specific application of the DSS and the specific improvements in a company’s performance. Places the DSS in the context of a case-based reasoning environment in which a knowledge base of system structures and their dynamic properties is achieved. Outlines the opportunity of utilizing the DSS in uncertain lead-time environments in a range of industry sectors

On the impact of order volatility in the European automotive sector

Order volatility is an unfortunate fact of life facing most suppliers of both products and services. In this paper we are concerned with establishing the magnitude of the problem faced by the European automotive sector. The evidence has been acquired via the site-based Quick Scan Audit Methodology (QSAM). Production scheduler strategy is thereby classified according to a new five-set schema as observed via individual value-stream volatilities. System variables have then been codified and correlated with customer order volatility. Powerful statistically significant relationships emerge from this evidence. This generally (but not wholly) supports intrinsic views on what constitutes good practice. A specific interface between supplier and OEM shows the existence of a positive loop that acts as a vicious circle to create unnecessary volatility in material flow