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

The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains

The paper compares the expected performance of a vendor managed inventory (VMI) supplychain with a traditional “serially linked” supply chain. The emphasis of this investigation is the impact these two alternative structures have on the “BullwhipEffect” generated in the supply chain. We pay particular attention to the manufacturer's production ordering activities via a simulation model based on difference equations. VMI is thereby shown to be significantly better at responding to volatile changes in demand such as those due to discounted ordering or price variations. Inventory recovery as measured by the integral of time×absolute error performance metric is also substantially improved via VMI. Noise bandwidth, that is a measure of capacity requirements, is then used to estimate the order rate variance in response to random customer demand. Finally, the paper simulates the VMI and traditional supply chain response to a representative retail sales pattern. The results are in accordance with “rich picture” performance predictions made from deterministic inputs

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

A procedure for the optimization of the dynamic response of a Vendor Managed Inventory system

This paper considers the performance of a production or distribution-scheduling algorithm termed Automatic Pipeline, Inventory and Order Based Production Control System (APIOBPCS) embedded within a Vendor Managed Inventory (VMI) supply chain where the demand profile is deemed to change significantly over time. A dynamic model of the system using causal loop diagrams and difference equations is presented. The APIOBPCS ordering algorithm is placed within a VMI relationship and a near saturated search technique evaluates optimum solutions based on production adaptation cost, system inventory cost and distributors' inventory costs. The procedure can also cope with supply chains that operate in a localized region (where small, frequent deliveries are possible) or on a global scale, where large batch sizes are needed to gain economies of scale in transport costs. Properties of the optimal systems are highlighted via various Bullwhip, customer service level and inventory cost metrics. Managerial insights are gained and a generic decision support system is presented for ‘tuning’ VMI supply chains. An important feature of the optimization procedure is the ability to generate a number of competing ordering algorithm designs. Final selection of the ‘best’ system is then made via managerial judgement on the basis of the simulated response to typical real-life demands. We finish with a discussion of how the procedure may be used in an industrial context to design and strategically manage VMI supply chains

Managing bullwhip-induced risks in supply chains

We discuss the exploitation of a well-established replenishment rule, the order-up-to policy, to control the supply chain risk resulting from the Bullwhip effect. To avoid this risk within supply chains, two specific recommendations are highly important. First, we provide strong evidence that availability of high fidelity up-to-date delivery lead-time information is essential. This inevitably requires transparency and trust between various 'players' within the supply chain. For many practical reasons this is easier to recommend than to actually achieve. Secondly, such reliable data needs to be automatically fed into the replenishment system to enable it to function in an adaptive mode. This step offers a reasonable guarantee of a well-controlled performance. It is also advisable that at the design stage, replenishment rule parameters should be set so that 'conservative' operation is achieved. The new theoretical advances described in the paper are validated via simulation models of the delivery process

Eliminating drift in inventory and order based production control systems

An Inventory and Order Based Production Control System lies at the heart of many commercial and bespoke ordering systems based on periodic review of stock and production targets. This simple and elegant control system works well, even when dealing with scenarios in which there are many competing value streams. However, such “interferences” inevitably cause some uncertainty in pipeline delivery times. We show via linear z-transform analysis that the consequences may include the possibility of inventory drift and instability. In this paper we establish the stability boundaries for such systems, and demonstrate an innovative method of eliminating inventory drift due to lead-time effect. This new principle is confirmed by simulation results