Production, Manufacturing and LogisticsRevisiting the value of information sharing in two-stage supply chains
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 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).
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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