Impact of market demand mis-specification on a two-level supply chain

This paper investigates the impact of mis-specifying the market demand process on a serially linked two-level supply chain. Box–Jenkins models are used to represent both the true and a mis-specified market demand processes. It is shown that the impact of mis-specification on cost is minor if the supply chain tries to minimise the market demand forecast errors. Furthermore, our analysis suggests that mis-specification does not always result in additional costs. A managerial insight is revealed; poor forecast accuracy is not always bad for the total supply chain costs. In other words, employing more accurate forecasting methods may actually result in higher total supply chain costs

A unified theory of the dynamics of closed-loop supply chains

We investigate the dynamics of a closed-loop supply chain with first-order auto-regressive (AR(1)) demand and return processes. We assume these two processes are cross-correlated. The remanufacturing process is subject to a random triage yield. Remanufactured products are considered as-goodas- new and used to partially satisfy market demand; newly manufactured products make up the remainder. We derive the optimal linear policy in our closed-loop supply chain setting to minimise the manufacturer’s inventory costs. We show that the lead-time paradox can emerge in many cases. In particular, the auto- and cross-correlation parameters and variances of the error terms in the demand and the returns, as well as the remanufacturing lead time, all influence the existence of the lead-time paradox. Finally, we propose managerial recommendations for manufacturers

The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation

Altruistic behaviour in a two-echelon supply chain with unmatched proportional feedback controllers

We study a two-echelon supply chain with first order autoregressive demand and unit replenishment lead-times. Each echelon of the supply chain uses conditional expectation to generate Minimum Mean Squared Error forecasts. Both echelons use these forecasts inside the 'Order-Up-To' policy to generate replenishment orders. We investigate three different scenarios. The first is when each echelon aims to minimise their own local inventory holding and backlog costs. The second scenario is concerned with an altruistic retailer who is willing and able to sacrifice some of his own performance for the benefit of the total supply chain. The retailer does this by smoothing the demand placed on the manufacturer by using a matched proportional controller in the inventory and Work-In-Progress feedback loops. The third scenario is concerned with an altruistic retailer with two, unmatched controllers. The matched controller case outperforms the traditional case by 14.1%; the unmatched controller case outperforms the matched controller case by 4.9%

A delayed demand supply chain: Incentives for upstream players

We study a decentralized supply chain where only delayed market demand information is available for making replenishment decisions. The impact of this delay is quantified in a serially linked two-level supply chain where each player exploits the order-up-to replenishment policy. The market demand is assumed to be a first-order autoregressive process. It is shown that the first level of the supply chain benefits from shorter time delays; however, the benefit for the second level is quite minor at best and can sometimes even be (counter-intuitively) detrimental. We conclude that the second level does not have a strong incentive to reduce the time delays in the shared market demand information

On the replenishment policy when the market demand information is lagged

We consider a situation where the most up-to-date information on the market demand and the inventory levels is not available to a replenishment decision maker in a single echelon of a supply chain. The objective of the decision maker is to minimise the sum of the inventory and the production costs. An intuitively attractive strategy under this setting might be to reduce the information time lag as much as possible by utilising information technologies such as RFID. We call this strategy the Time lag Elimination Strategy (TES). However, this course of action requires investment in information systems and will incur a running cost. We propose an alternative strategy that has similar economic consequences as the TES strategy, but it does not require new information systems. We call this strategy the Controlling Dynamics Strategy (CDS). The benefit coming from CDS is quantified and is compared to that from TES. We also quantify the benefits gained from the combined use of these two strategies. A new ordering policy is introduced that is easy to implement without any forecasting systems and can reduce the production cost significantly

Closed loop supply chains: The impact of advance notice and lead-times

This research investigates the impact of advance notice of the product returns on the performance of a closed loop supply chain when lead-times exist. Our closed loop supply chain consists of a manufacturer and an external remanufacturer. The market demand and the product return are stochastic and correlated with each other. A proportion of the sold products in the market are returned to an external remanufacturer. After a predetermined time period, the used products are converted into "good-as-new" products to be used to meet the market demand, together with the newly manufactured products. We quantify the benefit of the manufacturer obtaining advance notice of product returns from the remanufacturer. Furthermore, we demonstrate that the (re)manufacturing lead-times and some parameters in the product return rate can have a significant impact on the manufacturer's performance