Supply Chain Risk Management

In his 2009 paper Brian Tomlin analyzes strategies to mitigate disruption risks in a three echelon supply chain.

Setting
Focus in his research is a single company, with its suppliers and customers. The objective is to maximize expected utility, while demand and supply are uncertain. There are two products available which can be used as substitutes. The time horizon for the decision maker is one season where the products can be sold.

Three different sourcing structures are considered.

I already reviewed two other articles about agile supply chains. One on the role of distribution centers in supply chains and one on the migration from lean to agile supply chains.

But the question comes to mind if lean and agile supply chains have to be mutually exclusive or if it is possible to combine them. Christopher and Towill (2001) had a look at this question regarding the supply chain design.

The article reviewed here takes a look at typical biases in supply chain demand planning and how to avoid it. This work could prove very valuable for many companies who rely on manually adjusted forecasts.
Usually the forecasting process uses two steps:
1) statistical forecast by the forecasting system
2) manual adjustment to include additional effects (eg. additional analysis of demand pattern not included in step 1)

Data
The authors acquired data by four supply chain companies (industries: pharmaceuticals, food, household products and one retailer). As a whole over 68'000 datasets (statistical forecast, final (adjusted) forecast, actual outcome) were analyzed.

LeBlanc, Hill and Harder emphasis the uncertainty in forecast accuracy in their 2009 paper. And therefore address a huge gap in current SCRM research.

Model
In the model of LeBlanc et al. exist two uncertainties: Uncertainty over forecast accuracy and demand uncertainty.

There are two decision points where the manager can decide if / how much he wants to produce: A and B. At time C the finished goods are shipped. Restrictions to the production quantity at A an B can be applied.

LeBlanc et al. address three questions with this model.

• How much should the firm pay to improve the accuracy of the forecast?
• How much should the firm pay to achieve strategic changes, such as reducing the cost of incurring each shortage or the cost of delaying procurement and production until time B?
• For different forecast accuracies, shortage and holding costs, postponement add-on percentages, etc., what percentage of the forecast should a manufacturer postpone until time B, instead of producing at time A?