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Join ASCM today. Subscribe to SCM Now. Subscribe to this weekly e-newsletter. ASCM research cuts through the clutter and brings you critical ideas and innovations in supply chain management, best practices, how-to steps, and practical advice that give you and your organization a competitive advantage. APICS, through a partnership with The Manufacturing Institute, explores how manufacturing and supply chain can attract, retain and advance women.

Most popular apics authors. The annual budget season can quickly turn into a standoff between line managers and finance professionals. One approach to this problem is to issue a year-over-year cost reduction target—and require the plant to meet it any way possible. This has the advantage of being simple and effective, but it may leave money on the table. For example, how do we know if the required reduction is enough? Could the plant do better?

And if a plant is coming up short, how do we determine which lines should reduce their costs? To solve this problem, our team applied a well-known, but often ignored, empirical model known as the experience curve. Equipped with a library of such curves—particularly their slopes—we have a better basis on which to evaluate post-production cost reductions for products still in development. However, as arcane as all of this sounds, experience curves are surprisingly easy to create in a simple spreadsheet.

How it works This specific type of learning curve was first described by T. Wright in the s with regards to aircraft construction after he noticed that the labor hours required to build one airplane decreased by a fixed percentage with every doubling of cumulative volume.

Graphically, this means that plotting the hours per airplane against the cumulative volume produces a curve when plotted linearly and a straight line when plotted logarithmically. This relationship has since been renamed and extended to other per-unit models—most importantly to unit cost as a function of cumulative volume, for which it is most commonly referred to as an experience curve. The experience curve in Figure 1 shows a 20 percent reduction in cost with every doubling of cumulative volume also known as an 80 percent progress function.

The bunching together of plot points toward the right side of the chart is due to logarithmic scaling; it also illustrates the decrease of period-to-period cost reductions, even though the production unit is still following the 20 percent cost reduction trend. Experience curve effects should be intuitively familiar to anyone who has observed the maturation of a manufacturing line. At constant monthly production volumes, the cumulative volume will double at months 2, 4, 8, 16, and so on—three times in the first year, once in the second, once late in the third 32 , and not again until the sixth If the slope of the line on the plot is such that each doubling of cumulative volume produces a 20 percent reduction in cost, unit cost will decrease by 20 percent three times in the first year and once in the second, third, and sixth.

Cost reductions happen continuously along the curve. However, the rate of year-over-year change is decreasing—and this is the connection back to our intuition: The largest cost reductions in a new product come early on. As processes and operations stabilize, improvements are more difficult to achieve.

The experience curve quantifies this in a mathematical model that can be applied over a wide time range. Creating the models The cost most appropriate to use in an experience curve model is not the unit standard cost or any other readily available accounting or transfer-pricing construct.

Our experience followed the four general production unit scenarios outlined in Table 1. Direct expenses, single product. This exact arrangement is uncommon, but makes a good first example because modeling the next two production unit scenarios requires making off-line adjustments to have them function like this one. The unit cost is the sum of all relevant costs for the period divided by the number of units completed.

We exclude costs from service and administrative groups because they are arbitrarily allocated, and we use unadjusted dollars because the real cost is more useful.

An example of such a curve is the first regression line in Figure 2, which shows a 20 percent cost decrease with every doubling of volume.

The graph also illustrates what can happen when a product is redesigned: After a cost increase followed by a stabilization period, expenses continue decreasing. In this particular case, the second slope is steeper at 50 percent , reflecting the intense application of six sigma process improvement tools at the time the new product went into production. Here, the effects of a product redesign and subsequent cost-reduction initiatives are evident.

The new product used more expensive materials and had lower yields initially, but a strong focus on cost reduction accelerated rates of improvement. Bill of material and direct expenses for a single product. To make this arrangement mimic the direct-expense case, we added the cost of the materials consumed in production. Beyond that, the process is the same as with a direct-expensed, single-product model. A few products sharing one line. The single-product concept used in the first two cases also applies to multiple models that vary slightly in material cost different colors, sizes, or configurations of the same thing, for example.

For products that differ significantly in labor and other value-add, one option is to carefully split the costs among those products using the best data available. This can be a daunting task, but it may be the only way to get a comprehensive cost model for two or more products separately. Expenses should be captured at the transaction level in order to identify those directly attributable to a single product, allocate labor and other inputs proportional to relevant activity drivers, and split the rest by unit volume.

Multi-product, limited-cost-model scope. At some point, splitting costs among products using the previous method becomes impossible. The most likely place to find learning effects is in direct labor—and the first question is whether to model hours or dollars per unit. Even though T. Making predictions Armed with a slope, an intercept, and a correlation coefficient from the regression fit, we now can set about the business of predicting future costs.

Following are some points to keep in mind: 1. The slope alone is the most useful parameter because it predicts the cost reduction one should expect from one period to another where the volumes are the midpoint volumes for the current and future periods. Future cost is governed by the equation: In this way, the unit cost for the next period is always a function of the unit cost of the previous period, which prevents the model from diverging over time.

Comparing cost reductions from one period to the next using only the slope is more realistic than attempting to extrapolate the curve itself far into the future. Correlation coefficient values, on the other hand, mean little. This is a measure of the amount of variation accounted for by the regression fit. And if monthly unit costs vary above and below the regression line as they usually do , the correlation coefficient may be low.

A simple measure of fit that is relevant to prediction is a time-series plot of the residuals, or the difference between the data points and the regression line. The residuals should be equally spaced above and below the line and should not exhibit a trend, particularly in the most recent segments. Remember above all that fit and prediction are two very different things—and fit is less important.

After all. Thus, make sure to be on the lookout for situations that might lead to a change in slope, such as the example in Figure 2. A true understanding of cost Assuming that cost and output data are readily available, it is possible to create a simple experience curve in minutes.

Complex production arrangements or changing circumstances may require more refined models, but the most striking aspect of experience curves is how quickly they begin to generate useful information. Like everything else in the world of continuous improvement, creating experience curves is more journey than destination. The sooner you begin your journey with this simple but powerful tool, the sooner you will gain new knowledge, insight, and predictability for your product costs.

Alex J. Shrom is a financial analyst and certified six sigma black belt working as principal financial analyst at a major medical device manufacturer in St. Paul, Minnesota. He may be contacted at alexander. To comment on this article, send a message to feedback apics. The apics. Please review the Terms of Use and Privacy Policy and indicate your acceptance by clicking "agree" below.

Bryn Mawr Ave. Review our author guidelines. Are you a supply chain superhero? Share your story in "Supply Chain Matters. Submit it to our "Lessons Learned" department. Is your company doing big things worthy of recognition? Multimedia Upcoming Webinars. Research Reports ASCM research cuts through the clutter and brings you critical ideas and innovations in supply chain management, best practices, how-to steps, and practical advice that give you and your organization a competitive advantage.

Women in Manufacturing APICS, through a partnership with The Manufacturing Institute, explores how manufacturing and supply chain can attract, retain and advance women. Experience Counts Learning curves lead to production know-how and savings. Opening existing Close existing Most popular apics authors Click to select:.

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Join ASCM today. Subscribe to SCM Now. Subscribe to this weekly e-newsletter. ASCM research cuts through the clutter and brings you critical ideas and innovations in supply chain management, best practices, how-to steps, and practical advice that give you and your organization a competitive advantage. APICS, through a partnership with The Manufacturing Institute, explores how manufacturing and supply chain can attract, retain and advance women. Most popular apics authors.

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APICS is the association for supply chain management and a not-for-profit international education organization, offering certification programs, training tools and networking opportunities to increase workplace performance. In , 20 production control managers formed the American Production and Inventory Control Society. APICS offers several professional designations. The program was founded in

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