Production, Manufacturing and Logistics
Revisiting the value of information sharing in two-stage supply chains

https://doi.org/10.1016/j.ejor.2018.04.040Get rights and content

Highlights

  • We analyse the value of information sharing in a two stage supply-chain.

  • We consider two demand processes: stationary or random walk.

  • The retailer and manufacturer forecast demand using a moving average.

  • The value of information sharing is shown to be negative in the case of random walk.

Abstract

There is a substantive amount of literature showing that demand information sharing can lead to considerable reduction of the bullwhip effect/inventory costs. The core argument/analysis underlying these results is that the downstream supply-chain member (the retailer) quickly adapts its inventory position to an updated end-customer demand forecast. However, in many real-life situations, retailers adapt slowly rather than quickly to changes in customer demand as they cannot be sure that any change is structural. In this paper, we show that the adaption speed and underlying (unknown) demand process crucially affect the value of information sharing. For the situation with a single upstream supply-chain member (manufacturer) and a single retailer, we consider two demand processes: stationary or random walk. These represent two extremes where a change in customer demand is never or always structural, respectively. The retailer and manufacturer both forecast demand using a moving average, where the manufacturer bases its forecast on retailer demand without information sharing, but on end-customer demand with information sharing. In line with existing results, the value of information turns out to be positive under stationary demand. One contribution, though, is showing that some of the existing papers have overestimated this value by making an unfair comparison. Our most striking and insightful finding is that the value of information is negative when demand follows a random walk and the retailer is slow to react. Slow adaptation is the norm in real-life situations and deserves more attention in future research – exploring when information sharing indeed pays off.

Section snippets

Background and motivation

The current backdrop of uncertainty against which supply chains evolve has rendered forecasting and inventory control increasingly challenging tasks. Within this context the bullwhip effect (BE), the phenomenon of order variability amplification as one moves up any given supply chain (Cachon, Randall, & Schmidt, 2007), is one of the causes of excess inventories in supply chains and low customer service levels. Although the term ‘bullwhip effect’ was first coined by Procter & Gamble in the

Assumptions and models

We consider a discrete time model with a single manufacturer and a single retailer. There is a deterministic lead-time L from the supplier (not modelled) to the manufacturer and from the manufacturer to the retailer. We remark that the analysis could be extended to unequal lead-times, but that would make it lengthier and more tedious without providing further insights.

The sequence of events in any period t is as follows. Items shipped by the supplier in period tL arrive. End-customers demand D(

Analysis of the forecast error variance reduction

It is well known and also apparent from the analysis conducted in the previous section that the safety inventory needed to attain a certain service level is positively related to the forecast error variance. Therefore, we analyse the value of information sharing (hereafter denoted by VOI) by considering the (percentage) variance reduction of basing the manufacturer demand forecast on end-customer demand (DIS) instead of retailer demand (NIS).VOI=V[FEMNIS(t)]V[FEMDIS(t)]V[FEMNIS(t)]

In this

Stock control implications

In previous sections, we discussed the effects of sharing information on the forecast error variance of the manufacturer. Since forecast error variance is closely linked to the safety stock (needed to attain a certain service level), we expect increased/reduced variance to translate into increased/reduced inventory levels.

However, the exact safety stock calculations are not straightforward to conduct. An important complicating factor is that deliveries from the manufacturer to the retailer are

Conclusion

It is well known that demand variance may be amplified as we move upstream in any given supply chain. For a two-stage supply chain with, for example, a retailer and a manufacturer, this means that the end-customer demand faced by the retailer would typically be less variable than the retailer demand faced by the manufacturer. Consequently, if manufacturers are allowed to have visibility of the downstream less variable demand, they should be able, in principle, to improve their forecasting and

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