Managing Demand Risk in Tactical Supply Chain Planning for a Global Consumer Electronics Company

We consider the enigma of managing demand risk in tactical furnish chain planning for a particular global consumer electronics company. The company go in the rear [i]or[/i] in the wake ofs a deterministic replenishment-and-planning process despite considerable demand uncertainty. As a possible way to formally address uncertainty, we provide sum of two units risk measures, "demand-at-risk" (DaR) and "inventory-at-risk" (IaR) and sum of two units linear programming models to help manage demand uncertainty. The first prototype is deterministic and can be used to allocate the replenishment schedule from the plants among the customers as by the existing process. The other design is stochastic and can be used to determine the "ideal" replenishment asking from the plants under demand uncertainty. The gap between the output of the sum of two units models as regards requested replenishment and the values of the risk measures can be used through the company to reallocate capacity among different productions and to thus manage demand/inventory risk.

Key words: consumer electronics; demand uncertainty; stochastic programming; demand allocation; risk measures

Submissions and Acceptance: Received January 2004; revision received July 2004 and December 2004; accepted January 2005

1 Introduction

The consumer electronics industry is notorious for risk stemming from short fruits lifecycles and high demand uncertainty. However, a particular global consumer electronics company tread on the heels ofs a deterministic planning process with a rolling 26-week horizon. We outline a proces to manage this risk through suggesting two risk measures for demand- and inventory-related risk respectively and sum of two units linear-programming (LP) models: one for allocating the plants' replenishment schedule among the customers and the other to guide the solicitation to plants for replenishment above the horizon. Our contribution is a model-aided proces to manage demand and inventory risk in tactical endue chain planning using simple moulds and risk measures.

Of the sum of two units LP models, the first archetype is deterministic and allocates the plants' replenishment schedule for the coming 26 weeks among the different customers each week. This protoplast can be used to automate the existing manual proces The other original is stochastic and provides the optimal replenishment from the plants beneath demand uncertainty. The two risk measures we suggest are "demand-at-risk" (DaR) and "inventory-at-risk" (IaR). Another measure we suggest is the expected cost of unmet demand and exces inventory at the retailers as a function of changing the magnitude of the endue continuously between ?±20 percent. The prototypes can be used to determine the values of the risk measures associated with the output The values of these risk measures across harvests and the gap between the ideal and the actual values of these measures can be used by means of the company to real-locate capacity among different products

The not absent work is exploratory and we have not implemented the originals and the proposed process at the company although we do provide design solutions to illustrate the designs and the risk measures. Our effort sole explores modeling issues and the stochastic pattern we present is one of many possible with different motivations and different steps of sophistication. It should not be seen as a definitive solution flat for the company under consideration. Our focus is upon managing demand risk in tactical invest chain planning; refer to Sodhi (2003) for a strategic provide chain planning application. We limit the discussion of risk to unmet demand and exces inventory within tactical supply-chain planning; diocese Chopra and Sodhi (2004) for other sources of uncertainty. Kleindorfer and Saad (2005) deal with disruption risk, something that does apply to tactical planning, on the contrary is not under consideration here. Game-theoretic issues or risk-sharing between the company and its customers do not apply to the at hand context.

2. Motivating Example

Consider the fulfillment proces at the digital camera-and-videocamera division of a global consumer-electronics company. The company has regional offices from one extremity to the other of the world. These offices deal with customers in their region and coordinate with the company's headquarters for orders and fulfillment. Each region comprises single or more countries. Customers in a region include electronics retail chains like Circuit City or Best purchase in the US and Dixons in the UK as well as distributors. each week, every customer provides a weekly "order" or forecast for each week of a 26-week rolling horizon for each of the division's fruitss at the SKU-level to the appropriate regional office. Each regional office aggregates these orders through week and sends the aggregate weekly orders to headquarters. Headquarters allocates popular and planned supplied to these orders and ships against the common week's orders from a central warehouse either to customer warehouses directly or to regional warehouses from where they can be dispatched to customers in the region.

There are restrictions upon customers to revise their weekly orders relative to the singles they had provided a week earlier for the same period in the 26-week horizon. Likewise, plants are restricted by means of necessity to keep their production schedule and replenishment stable each week. For weeks 1-4 the time hedge for order revisions, customer, and region orders are considered firm, while customers can change their "orders" for weeks 5-26 with restrictions. Likewise, the plants confirm their replenishment schedule for weeks 1-12 the time wire-guard for production, but are more flexible for changes beyond that.