Trimming Safety Stock: Empirically, Realizing Working Capital Gains

Trimming Safety Stock: Empirically, Realizing Working Capital Gains

Tanuj Sood (JDA Software, India)
DOI: 10.4018/978-1-4666-9894-9.ch014
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Abstract

Today's retail environment has become extremely competitive with retailers offering low prices almost on daily basis through various promotional techniques. Majority of their products placed on their shelves are promoted to boost sales, compete efficiently and gain market share. Retailers have a natural tendency to keep a very close watch on various costs in whichever way they can be curtailed or controlled. Costs like Labor, Transportation, Vendor Deals and Inventory reduction are some of the key areas that are tracked and renegotiated very frequently by retailers worldwide. Safety Stock holding is one critical area where a lot of work can be done “empirically” by retailers and distributors to create stock efficiencies across their established supply chain networks. Application of appropriate statistical techniques on the right set of products can help us getting a trimmed down safety stock numbers which are still capable in addressing the demand and supply variability while holding much lesser stock and still achieve greater customer service levels.
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Introduction

The theory of Bull Whip Effect explains the problem of amplification of demand variability signal as we move up in the supply chain (Lee, 1997 and Balakrishnan, 2004). This amplified or inaccurate information traversing up the supply chain leads to multiple inefficiencies in the entire system leading to stock excesses, inventory shortages, loss in customer sales and revenues, inappropriate capacity planning and production schedule and finally falling customer satisfaction at global retail outlets.

To avert this, the retail organizations have to maintain high level of safety stocks at various levels of their global supply chains. In this chapter we will emphasize largely on the reduction in safety stock build ups by focusing on the key contributing factors like demand variability, supply variability, customer service levels and competition and how by applying appropriately chosen statistical distribution models and other home-built empirical techniques we can arrive at intelligent and optimized safety stock figures for different stock keeping units (SKUs = item at a location) present at different echelons of the supply chain network.

Robert N. Boute (2007) has strongly emphasized on the coordination problem in supply chain, more specifically the issue of coordinating the retailer’s safety stock requirements and manufacturer’s lead time decisions which are quite closely related as longer and more variable lead times lead to higher level of safety stocks. Having just the right safety stock at the right place is a significant challenge in itself as it requires a inputs from multiple directions of organization’s ecosystem where it is operating from like planners, customers, vendors, competitors, transporters, economy and shareholders etc.

Disney (2006) who analyzed P&G’s home care & family care product categories, the presence of weekly promotions actually might lead to deceleration of customer demand before and after the promotion period due to customer stock piling during promotions. Similarly we refer to Raju (1992) who has related the promotional activity in a product category to its variability in sales.

Oeser (2011) emphasized enough on the concept of risk pooling at a higher echelon in supply chain network like a distribution center to address the problem of demand variability wherein the variability for individual products is consolidated in order to reduce the product portfolio’s total variability.

Quite naturally, in this chapter, we will also focus on the various prevailing constraints which retail organizations of today’s world operate under like lead time and capacity constraints, varying customer expectations, promotions, profitability, alignment of supply chain information systems with varying customer needs. We will discuss how addressing variability of different kinds will lead to significant cost savings not only in working capital investments but also in other areas of the retailer’s business and consequently in what all areas these monies saved can be spent to enhance profitability component of the balance sheet, adding multiple new revenue generation streams and aligned customer satisfaction levels. Killingsworth (2011 p6) states “The dynamics of supply chains and large supply networks are still not well understood and major inefficiencies are the costly result. As supply chains have become more global and increasingly complex, supply chain dynamics and the associated risks and costs plague companies around the world.”

Key Terms in this Chapter

Demand Planning: Demand Planning is a function in any supply chain organization wherein forecasts or expected sales patterns are created based on historical sales, trends, seasonality for a particular product or a category of products.

Capacity: The work that can be done over a specified period of time. It can be calculated at the Work Center, Work Area or Plant Level. It is usually stated in hours. Capacity = (number of machines) X (utilization) X (efficiency) per time period.

Inventory Turns (Inventory Turnover): The number of times that your inventory cycles or turns over per year.

ABC Classification: A method of classifying inventory into groups. A typical method is to multiply the Unit Price by the annual Expected Volume.

Statistical model: A statistical model embodies a set of assumptions concerning the generation of the observed data, and similar data from a larger population. A model represents, often in considerably idealized form, the data-generating process.

Safety Stock: The stock held any location in the supply chain network that is in excess of what the company expects to sell or ship. The purpose of Safety Stock is to act as buffer inventory to account for unexpected customer orders, longer than expected manufacturing or transportation time.

Statistical: The likelihood that a result or relationship is caused by something other than mere random chance.

Supply Chain Network: The network created amongst different companies producing, handling and/or distributing a specific product. Specifically, the supply chain encompasses the steps it takes to get a good or service from the supplier to the customer.

POS (Point of Sale Data): Data that shows the actual units sold. Usually tracked by bar code scanning.

Supply Variability: Variability in expected supply at a location from the source due to variation in lead times, transportation, logistics, supply or information sharing issues.

Stock Outs: A stock out, or out-of-stock (OOS) event is an event that causes inventory to be exhausted. While out-of-stocks can occur along the entire supply chain, the most visible kind are retail out-of-stocks in the fast moving consumer goods industry (e.g., sweets, diapers, fruits).

Empirical: It is derived based on what is experienced or seen rather than on theory.

Logistics: It deals with managing and controlling the flow of goods from the source of the production to the marketplace.

Variability: Variability is basically the difference between what we expect from something and what actually happens. It is the statistical distribution of outcomes one can expect from a process.

Lead Time: The time between the need for goods and the receipt of the goods. This time can be made up of order preparation time, manufacturing time, transportation time, receiving time and quality check time.

Standard Deviation: A quantity expressing by how much the members of a group differ from the mean value for the group.

Forecast Error: A comparison between actual demand and forecasted demand. It is usually stated as a percentage.

Customer Service Levels: Certain goals are defined and the service level gives the percentage to which those goals should be achieved.

Demand Variability: How variable is your demand or actual sales from expected behavior or trend.

Supply Chain Planning: Supply chain planning (SCP) is the forward-looking process of coordinating assets to optimize the delivery of goods, services and information from supplier to customer, balancing supply and demand.

Forecast: An estimation of the future demand for a product. It is usually stated as a quantity (or value) over a specific time period. There are a number of inputs into a forecast, such as: historical data, market trends, marketing data and sales force feedback.

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