Retail Ordering Policy Risk

Retail Ordering Policy Risk

Copyright: © 2018 |Pages: 29
DOI: 10.4018/978-1-5225-2703-9.ch009
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Abstract

The method is applied to Retail Ordering Policy to manage the associated risk. DMAIC framework applies stochastic techniques. Stochastic optimisation determines the optimal retail ordering policies to maximise profit. Simulate every determined optimal ordering policy and calculate profits, risks, and Six Sigma metrics to measure against specified target limits. Analyse simulation results and identify and quantify the main contributors to the profits variability by using sensitivity analysis. The optimal retail ordering policies are ranked based on their profits and associated risk factors. The technically best optimal retail ordering policy is recommended to the management for implementation. Control stage is elaborated by reusing the data and presented stochastic optimisation and simulation models for ongoing management of the optimal strategy. Some changes are applied to the data and models however, in order to emulate the scenario of an implemented strategy.
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Introduction

This chapter presents the third Income Management application class of the method. It is applied in Retail to manage the risk in ordering policy plan for goods of a retailer for the next season.

Retail ordering policy consists of rules which determine when an inventory replenishment order is to be issued. Typically, a retail business will determine its ordering policy based-on customers’ demand, with objectives to manage their inventory of goods, reduce costs, and increase overall retail efficiency and profit. Generally, customers’ demand is inherently uncertain, thus necessarily generating risks in retail planning. This chapter discusses Retail Ordering Policy Risk Management. Ordering policy techniques are generic across retail businesses. So, the generic techniques consider and resolve the retail business specific aspects. The major challenge for ordering policy is to determine an optimal strategy which maximises the expected profit considering the inherently uncertain demand.

In their paper, Chen and Samroengraja (2000) discussed a one-warehouse, N-identical-retailer model considering uncertain customer demands. In this model, retailers restock the goods from the warehouse, which orders the merchandise from an external supplier. Retailers periodically place orders and reorders are limited at only one in a period. The warehouse applied an (s, S) ordering policy. Authors presented two policies specifying how retailer orders are filled at the warehouse, i.e. past priority allocation (PPA) and current priority allocation (CPA). They developed an approximate model to determine near-optimal control parameters for PPA and CPA in order to compare policies performance.

Choi, Li and Yan (2003) published an optimal two-stage ordering policy for seasonal goods. In the first stage, market information is collected. This data is used to update the demand forecast in the second stage by using Bayesian approach. The article formulated a two-stage dynamic optimisation problem which determines an optimal policy by using dynamic programming.

In their article Kelle and Milne (1999) argued that the variability of orders significantly depends on the variability of the customers’ demand, which makes supply chain planning difficult. They considered basic elements of a supply chain such as individual and total retailers’ purchase orders, and the supplier’s ordering or production strategy. The paper demonstrated how the (s, S) policy parameters, the demand parameters, and the cost coefficients influence the variability of the orders by using approximations to the exact quantitative models.

Pan, Lai, Liang and Leung (2009) claimed that retailers should make dynamic pricing and ordering decisions based on market demand forecast, in order to obtain maximum cumulative profit from the product during its lifecycle. They considered this scenario and developed a two-period model to resolve pricing and ordering problems for a main retailer with demand uncertainty in a decreasing price condition.

Zhu, Hong and Lee (2013) studied a risk-averse retailer and measured the risk by conditional-value-at-risk (CVaR). They investigate how inventory imprecision relates to permanent shrinkage and temporary shrinkage scenarios. The article presented two models for reducing inventory shrinkage. They proposed optimal policies for the two scenarios by applying the CVaR criterion.

Six Sigma is recognised and used as a process improvement methodology across industries today. It has also been generally employed in retail but mostly for operations improvement by using Lean Six Sigma. For example, Curtis, An and Gettys (2008) published an article arguing that only recently, retail businesses started to apply Lean Six Sigma (LSS) to improve their operational processes for building a continuous-improvement capability to enable competitiveness, growth and high performance. This article explores how to apply LSS in retail businesses considering the arising challenges and provided some answers to this question by highlighting examples from pioneering retailers that have successfully deployed LSS, achieving significant results.

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