Measuring Supply Chain Performance

Measuring Supply Chain Performance

As indicated in previous sections, inventory is a key element in supply chain management. On one hand, it enables a company to cope with uncertainty by serving as a buffer between stages in the supply chain. Inventory allows items to flow smoothly through the system to meet customer demand when stages are not in sync.

On the other hand, inventory can be very costly. Thus, a company needs to maintain an efficient supply chain by lowering inventory levels (and costs) as much as possible. To accomplish this objective, several numerical measures, also called key performance indicators (KPIs) or metrics, are often used to measure supply chain performance. Three of the more widely used key performance indicators are inventory turnover, inventory days of supply, and fill rate.

Key Performance Indicators

Inventory turnover (or turns) is computed by dividing the cost of goods sold (i.e., the cost of annual sales) by the average aggregate inventory value:

Inventory turnover = Cost of goods sold / Average aggregate value of inventory

The average aggregate value of inventory is the total value (at cost) of all items being held in inventory, including such things as raw materials, work-in-process (WIP), and finished goods. It is computed by summing, for all individual inventory items, the product of the average number of units on hand in inventory at any one time multiplied by the unit value:

Average aggregate value of inventory = ε (average inventory for item ) unit value item i i u ( )

The cost of goods sold is only for finished goods, valued at cost, not the final sale price (which might include discounts or markups).

Every time product items are sold that are equal to the average amount of money that was invested in those items, then the inventory has been turned. An item whose inventory is sold (i.e., turns over) once a year has higher holding costs (for rent, utilities, insurance, theft, etc.) than one that turns over twice, three times, or more in that same period.

A poor, or comparatively low, inventory turnover indicates that a large amount of inventory is required to satisfy demand. In general, a good (or poor) number of inventory turns is relative to what is being achieved at various stages across a company and what the industry norm is. Only comparisons of inventory turns for companies within the same industry are meaningful.

Comparing a supermarket to a car dealer is not meaningful; a supermarket sells fast-moving products so its inventory turns will be higher than a car dealer that sells slow-moving items. In the 1980s, inventory turns for many manufacturing companies were less than five; however, the advent of lean production and the increased focus on quality management and supply chain management have increased inventory turns in much of the manufacturing sector to about six turns per year for a typical company.

Although this seems like a small change, it still represents a significant decrease in costs and an increase in profits. On the other hand, a typical computer company or grocery store will have 12 turns or more per year. Another commonly used KPI is days (or weeks) of supply. This is a measure of how many days (or weeks) of inventory is available at any point in time. It is computed by dividing the aggregate average value of inventory by the daily (or weekly) cost of goods sold.

Days of supply = Average aggregate value of inventory / (Cost of goods sold)/(365 days)

Automotive companies typically carry about 60 days of finished goods supply. Another frequently used KPI is the fill rate. Fill rates are the fraction of orders placed by a customer with a supplier distribution center or warehouse that are filled within a specific period, typically one day. High fill rates indicate that inventory is moving from the supplier to the customer at a faster rate, which thereby reduces inventory at the distribution center.

For example, Nabisco’s fill rate for its Planter’s peanuts at Wegman’s grocery store chain is 97%, meaning that when the store places an order with the Nabisco distribution center, 97% of the time it is filled within one day.

Process Control

Although we tend to think that process control is used to monitor and control quality for manufacturing operations, it can also be used to monitor and control any of the processes in the supply chain. If products are defective, then the effects are obvious. However, other problems along the supply chain that create uncertainty and variability are most often caused by errors.

If deliveries are missed or are late, if orders are lost, if errors are made in filling out forms, if items with high obsolescence rates (like PCs) or perishable items are allowed to stay too long in inventory, if demand forecast errors are made, if plant and equipment are not properly maintained, then the supply chain can be disrupted, thereby reducing supply chain performance. Thus, at any stage in the process, statistical process control charts can be used to monitor process performance.

SCOR

The supply chain operations reference (SCOR) model is a supply chain diagnostic tool that provides a cross-industry standard for supply chain management. It was developed by the Supply Chain Council, a global not-for-profit trade association organized in 1996 with membership open to companies interested in improving supply chain efficiency primarily through the use of SCOR.

The Supply Chain Council (SCC) has almost 1,000 corporate members around the world, including many Fortune 500 companies. In 2014 the SCC merged with the American Production and Inventory Control Society (APICS), with over 40,000 members, which maintains the SCOR model.

The purpose of the SCOR model is to define a company’s current supply chain processes, quantify the performance of similar companies to establish targets to achieve “best-in-class” performance and identify the practices and software solutions that will yield “best-in-class” performance. It is organized around a set of six primary management processes—plan, source, make, deliver, return, and enable.

These processes provide a common set of definitions, or building blocks, that SCOR uses to describe any supply chain, from simple to complex. This allows supply chains for different companies to be linked and compared.

A primary feature of the SCOR model is a set of five performance attributes of a supply chain—reliability, responsiveness, agility, costs, and asset management—that enable it to be analyzed and evaluated against other competing supply chains.

Reliability, responsiveness, and agility are more customer-focused attributes, while cost and asset management are more internally focused. Associated with each performance attribute are various quantitative metrics that an organization can calculate to determine how well its supply chain is performing within its industry.

There are over 250 SCOR metrics organized within a three-tiered hierarchy according to level 1 (strategic), level 2 (processes), and level 3 (diagnostic). Table below summarizes and defines the SCOR performance attributes and shows the level 1 metrics for each attribute with their description.

These metrics can be used to develop a “SCOR mark” that benchmarks a company’s current supply chain performance for different processes against its industry competitors’ metrics. The company then projects the level of metrics it needs to be on par with its competitors, to have an advantage over its competitors, or to be superior. The value associated with these measured improvements in performance is then projected for the different performance attributes.

SCOR provides a framework not only for measuring performance but also for diagnosing problems and identifying practices and solutions that will enable a company to achieve its competitive performance objectives. SCOR also has a special application called GreenSCOR designed to address environmental sustainability efforts.

The SCOR framework provides a structure for measuring environmental performance and where it can be improved using the following strategic metrics: carbon emissions, air pollutant emissions, liquid waste, solid waste, and percent recycled waste.