Why Study Inventory Management? Professors David Pyke and M. Eric Johnson*

Executive Summary

Studying inventory management can yield significant improvements in both inventory cost and customer service levels. In the context of a real case, this article illustrates the process of assessing the opportunities and the benefits that can accrue. We refer to, but do not present, advanced, yet relatively simple, formulas that can be implemented on a spreadsheet. Applying these formulas can yield near-term improvements in both inventory investment and service level through implementing the right inventory levels, without requiring significant changes to the supply chain. This process, which we call inventory rationalization, is illustrated by moving from the “current position” point on the graph below to the “efficient frontier” curve.

Beyond inventory rationalization, supply chain integration can help reduce forecast error and therefore can actually shift the entire efficient frontier to a whole new level. We describe several supply chain initiatives and illustrate their effects on inventory investment, as can be seen by the reduced-forecast-error curves on the graph. Finally, we illustrate the use of inventory tools as an input to strategic decisions such as outsourcing to China.

*This note was written by Professors David Pyke and M. Eric Johnson on July 18, 2005. Reproduction without written permission is prohibited. Permission to copy may be requested by contacting Ann Bunnell at ann.bunnell@dartmouth.edu. The authors may be contacted by emailing david.pyke@dartmouth.edu and m.eric.johnson@dartmouth.edu.

Introduction

Mike Edwards, special assistant to the president and owner of Corrugated Products (CP)1, looked over the shop floor and wondered if all those piles of inventory were necessary. Customer service was critical to CP, and Mike had learned enough about inventory to know that a significant decrease could be detrimental to CP’s reputation. However, was it possible that those leaning towers of corrugated cardboard contained more than enough inventory? Was there money to be saved?

Corrugated Products was a $70 million North Carolina company that bought 4x8 foot sheets of corrugated cardboard from large suppliers, and then cut and scored them according to customer-specific designs. The result was a flat piece of cardboard that CP’s customers could easily bend into a box. CP made about 650 different products or stock keeping units (SKUs) for delivery to customers who were all within about a day’s drive from CP’s plant.

Mike was hired to lead operational change, and ultimately, if things worked out with the current president and owner, to buy the company. His first idea was to implement some ideas he had learned about inventory management, but he wanted to be fairly certain that improvement was possible before spending political capital on widespread change. After a ½ hour phone call to one of his business school professors, Mike had a plan that should help him understand if a reduction in their $5 million of inventory was possible without damaging customer service. Furthermore, the president had asked him about two possible major initiatives that had been all over the local press recently – outsourcing to China and supply chain management. He wondered how his inventory management project would inform these discussions.

Assessing the Opportunity

Mike’s professor suggested that the first step would be to collect some basic data on a representative sample of items.2 After some thought, Mike decided that nine items would be sufficient. Lead times to replenish finished goods inventory of these items were one week, and CP used a service level of 98% for all items. Mike’s data are presented in Table 1. Annual demand for these items was over 2.4 million units, and the item values varied widely. CP’s current reorder points and order quantities are presented in the table.3 Now, the dollar value of the inventory is defined as the product of the average inventory and the item’s unit value. Notice that the dollar value of the total inventory of these items was $35,889. These data were readily available from sales records, so Mike did not have to spend excessive amounts of time collecting it. (The standard deviation seen in the table is a measure of forecast accuracy, or equivalently, variability or dispersion of demand.4)

1 The data for the items examined in this case are directly from the company, but other particulars and names have been disguised to preserve confidentiality.2 For a more complete treatment of assessing the opportunity, see (Pyke, 2004).3 The reorder point is the inventory level at which an order is triggered, and the order quantity is the amount of that order.4 See (Johnson, 2003).

2

Table 1: Corrugated Products Data and Current Inventory Policies

After collecting these data, Mike had to study and apply advanced inventory formulas to create recommended inventory policies (that is, order quantities and reorder points). Fortunately, the formulas were not too complicated, and Mike’s professor had them built into a fairly simple spreadsheet.5 The question at this stage was whether CP’s current inventory policies were correct, outdated or perhaps wrong.

The results were startling. He discovered that some items had way too much inventory (Item 2155, for instance), while others had too little (Item 9678). Using the spreadsheet, Mike then calculated the current service levels of the nine items, assuming that the current policies were followed. See Table 2 for these results. These numbers enabled him to check with his inventory managers and procurement personnel to see if his results passed the “sniff test.”

5 For more detail on finding these advanced formulas, see (Johnson, 2005), or (Silver et al., 1998), Chapter 7.

3

Table 2: Current Service Levels

Mike’s recommended inventory policies included both revised order quantities, which capture the tradeoff between inventory holding costs and fixed ordering costs, and revised reorder points, which capture the tradeoff between holding costs and service levels. These policies, along with the resulting dollar value of the inventory, are presented in Table 3. Based on the inventory formulas, the service level for all these items would be 98%.

Table 3: Recommended Inventory Policies and the Resulting Inventory Value

One can see that the potential savings were huge. Look at Item 2155 for example. Inventory managers at CP had set the order quantity to 30,000 units and reorder point to 45,000 units, leading to an average inventory value of $11,000 for this item (Table 1). The advanced formulas, however, recommended an order quantity of 31,670 and a reorder point of 28,272. Now the two order quantities were fairly similar, but the recommended reorder point was significantly lower than the current one; and the resulting average inventory value was $6,653, a reduction of 40%. The total value of the recommended inventory was $24,759, or a savings of 31% from the current value of $35,889. In addition, CP would be providing a consistent 98% service level for each

4

item. Mike could adjust the order quantities and reorder points to more round numbers, but that would not significantly change the results.

If these nine items were representative of CP’s entire inventory of 650 SKUs, they could save over $1.5 million in inventory. Note that this is a one-time savings. Nevertheless, the ongoing savings at a carrying charge of 20% is over $300,000 per year, every year! All because of applying a few advanced, yet simple, inventory formulas on a spreadsheet. Many firms have far more than $5 million of inventory, so the potential is impressive indeed.

Now it is important to point out that the savings will not appear instantaneously. Items that have been operating at a low service level will likely require immediate purchase, whereas it will take some time to sell off items that have too much inventory. If a firm’s inventory turnover averages two to four turns per year, it will often take one to two quarters to see the results of an inventory improvement like that at Corrugated. But if the opportunity assessment yields results of this magnitude, it will be well worth the wait.

In our experience with dozens of firms, both from our own consulting as well as from student projects, we have seen just two cases where the potential savings was minimal. In every other case, the savings ranged from about 10% to upwards of 35%. And in almost every case, the firm could both save inventory investment and improve service at the same time. One key insight from our experience, and from the formulas, is that if the standard deviation of forecast error is less than about 20% of the mean lead time demand, the opportunity for savings is often on the lower end of the spectrum.

Sensitivity Analysis

Mike was quite excited about these results, and he immediately called his professor. After another ½ hour conversation, Mike decided to look at the effects of operational improvements on CP’s inventory. For instance, what if the president and owner decided that he was spending too much money on inventory? Alternatively, what if 98% service was too low to be competitive in this market?

Using the same spreadsheet and formulas, Mike performed a simple sensitivity analysis on the service level for the nine sample items. The results are in Figure 1. Recall that if Mike used the recommended formulas, his inventory investment for these nine items would be $24,759. Now, if he wanted to improve the service level to 99.9%, his inventory investment would increase by 41% to $34,860. Alternatively, if he were willing to provide 90% service, the investment would be $16, 450, or a savings of 34%. This analysis, of course, must involve sales and marketing managers, but Mike now had a spreadsheet and graphical analysis that could provide valuable input to the discussion.

5

Figure 1: Sensitivity Analysis – Inventory versus Service Level

Strategic initiatives: Outsourcing to China

Mike knew that there could be many potential benefits of outsourcing to China, including dramatically lower unit cost, gaining a foothold in Asia, globalizing the business, and learning how to conduct business in another country. On the other hand, CP might run several risks, including lower quality, poor delivery performance, difficulty handling supplier relationships, and issues with the domestic workforce. Corrugated Product’s president realized that one input to the China decision should be potential inventory effects. And Mike realized that an analysis similar to the service/inventory tradeoff he had prepared could be used.

Recall that the current replenishment lead times were one week for all of CP’s products. Mike’s preliminary investigations suggested that lead times from China would be roughly eight weeks. He applied the spreadsheet with the advanced formulas to create the tradeoff graph in Figure 2, and came to the startling conclusion that inventory investment for the nine sample items would increase from $24,759 to over $51,000, or more than a 100% increase. If these items are representative of all 650 SKUs, and if Mike were to outsource all of CP’s products to China, the inventory investment would grow to more than $10 million. Of course, the unit cost savings might overwhelm the increased inventory and transportation costs, but clearly CP should include the inventory effects in this important decision. Note that this same graph captures the effect of an operational improvement. If, by sourcing from a local supplier or other operational improvements, Mike could reduce the replenishment lead time from one week to 1 day, his inventory investment would decrease by about 28%, with no decrease in service.

Now these numbers and percent changes are specific to this case. Nevertheless, the general shape of Figures 1 and 2 are consistent across many inventory systems that we have seen.

6

Figure 2: Outsourcing to China: Lead time versus Inventory

What had Mike learned thus far? He knew that CP’s current position was not optimal, assuming that the nine items were representative. He also knew that if CP made some strategic choices about customer service, he could provide valuable input to the discussion using the inventory/service tradeoff curve. Finally, he had discovered that the same tools could provide input to long term strategic decisions about outsourcing and to initiatives for operational improvement. Figure 3 highlights CP’s current position relative to an “efficient frontier” on the inventory/lead time tradeoff curve. Mike could easily move CP to the efficient frontier if lead times remained at one week, simply by applying the recommended inventory policies. Or, he could shift CP along the efficient frontier, assuming he could influence lead times accordingly. A similar picture applies to the inventory/service level tradeoff.

Figure 3: Corrugated Products’ Current Position Relative to an Efficient Frontier

7

Strategic Initiatives: Supply Chain Improvements

The other initiative raised by CP’s president was supply chain management. Mike had studied supply chain management in school, so he knew that the potential benefits were huge. Supply chain initiatives such as Collaborative Planning, Forecasting and Replenishment (CPFR) can provide a supplier with visibility to their customers’ production and inventory plans. Therefore, instead of reacting to customer orders, the supplier can plan ahead based on actual customer demand, thereby reducing forecast error. A further development, called Vendor Managed Inventory (VMI), assigns the responsibility for managing the customer’s inventory to the supplier. VMI, like CPFR, gives better visibility to customer demand well in advance of when the customer would typically place an order. Forecast error again can decrease substantially.

Mike wondered if CP could save inventory investment if he pursued CPFR or VMI with their largest customers. He knew that CP had the capability to work with customers in this way, and he was excited to get some support for the discussions. To quantify the effect, he decided to see what would happen if, as a result of the collaboration, the standard deviation of forecast error for each product decreased. The same spreadsheet and inventory formulas yielded the graph in Figure 4. For example, the graph shows that if CP could reduce the standard deviation of forecast error to 70% of its current value, they would cut inventory investment for these nine items to about $21,000, or a savings of about 14%. If they could reduce standard deviation by half, the savings would be 22%.

Figure 4: Supply Chain Improvements: Reduction in Forecast Error

8

9

Summary

Mike’s experience at Corrugated Products is similar to many other examples of dramatic inventory improvement – lower costs and better service – that we have observed. Why are such opportunities so prevalent? Sometimes, managers are simply too busy to review their inventory policies. Demand patterns change, new products are introduced, supply chains become more integrated, suppliers move offshore, but inventory policies are not updated. In these cases, it is fairly simple to recompute the policies and make the appropriate adjustments. Other times, as in CP’s case, managers do not have access to advanced formulas and insights. The materials referenced in this note can be very helpful in this regard, as can a number of other inventory teaching materials. These cases are represented by the “current position” point on Figure 5. In spite of implementation challenges, it can be relatively straightforward to move from the current position to the efficient frontier (the highest curve on Figure 5), by rationalizing inventory via advanced inventory formulas.

Often, however, the most impressive improvements come from shifting the efficient frontier to a whole new level. These shifts require fundamental changes in supplier and customer relationships and forecast accuracy. Supply chain management innovations can help companies achieve these results, and the inventory management tools discussed in this note can be used to analyze the associated inventory benefits.

Figure 5: Supply Chain Improvements: Shifting the Efficient Frontier

10

References

Johnson, M. E. (2003). Understanding Supply Chain Variability and Measuring Forecast Performance. Tuck School of Business Note, Dartmouth College, Hanover, NH.

Johnson, M. E. (2005). Calculating Safety Stock. Tuck School of Business Note, Dartmouth College, Hanover, NH.

Pyke, D. F. (2004). Opportunity Assessment. Tuck School of Business Note, Dartmouth College, Hanover, NH.

Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory Management and Production Planning and Scheduling (3 ed.). New York: John Wiley & Sons.

11