Strategies for reclaiming your trapped capacity
The more chaotic the planning environment, the more capacity gets lost during execution. The disorder results in an effect called trapped capacity. Trapped capacity is the amount of time consumed on a production line to correct for variability on both the supply and demand sides. Even the most meticulously constructed capacity plan can be foiled by trapped capacity. Rather than blaming random events and bad forecasting, trapped capacity needs to be understood, quantified, and included in capacity planning.
Consider, for example, a line qualified to produce 1,000 cases a week. Can you schedule it make to make 1,000 cases per week in your annual plan? The short answer is no. This line can indeed make 52,000 cases in a year, but it will not always produce the right stockkeeping unit (SKU) at the right place and right time to satisfy demand.
Are evil gnomes stealing time from your lines in the dead of night? No; this issue is that rough-cut capacity planning—our standard means of capacity planning—is too rough. Portfolios are complex, capacity is limited, and everyone wants to run lean. Planning to average run rates and average demand levels gives false results.
Just because you meticulously measured the average output of a line to be 1,000 cases per week does not allow for the fact that it might be swinging from 800 to 1,200 cases per week. Additionally, average demand might be anywhere from 500 to 1,500 cases per week.
Furthermore, enterprise resources planning applications are not predictive tools and will not save you from production variability. They provide valuable information, but if relied upon, everyone eventually will be attending weekly meetings to recover fill rates. Figure 1 shows a system that has more average capacity than average demand. Yet, the probability that on any given week there will be more items sold than can be produced is very high. Even with a perfect forecast, this cannot be stopped.
A lean system cannot rely on inventory to absorb variability.There are two secrets you should know about statistical safety stock: First, it can only be calculated one way. No matter how sophisticated the inventory optimization program, safety stock is subject to a single statistical equation and curve. Second, this equation makes the dangerous assumption after production cycles of expecting that the system will replenish everything consumed and that all backlogs will be cleared. But what if you sell more than you can make in two or three successive periods? In such a case, you would dig into your safety stocks too deeply.
Figure 2 shows a real-life production line for frozen dinners. This line was loaded to 100 percent (average demand = average capacity). And, according to rough-cut capacity planning, this should have worked. Yet, there were many instances with one, two, or even more weeks of sequential capacity shortage. One week of short age is all right, but two weeks means this system tapped its inventories too much. The result at this business was fill rate shortages. This effect often is compounded by a planning department’s tendency to react to a single SKU’s shortage.
“Planning harder” to fix one SKU’s shortage in a lean environment often sets off a ripple effect on every other SKU manufactured on the line. The leading cause of variability in the supply chain is not demand fluctuations; it is human decision making. Schedule changes, unanticipated changeovers, reassigned inventory, and general confusion caused by broken production run strategies lead to two counterproductive results. First, they decrease the effective capacity of a manufacturing line when output is required most. And second, they upset the balance and harmony of a well-planned production run strategy and invalidate safety stock calculations.
This is a planning problem, not an execution problem. Annual and quarterly rough-cut capacity planning needs to be replaced by effective capacity planning, wherein the fluctuations in supply and demand are absorbed by an economic combination of safety stock and capacity. Relying on only safety stock and a heroic operations staff will not solve the problem.
There is a three-way relationship 0 between system variation, capacity loading level, and safety stocks that satisfy a given fill rate target. Knowing the first two points gives you the third: safety stock. For most plans, variability is a given—demand variation or supply performance will not change overnight. The primary choice to make is regarding line loading. The key question here is this: Do you expect 100 percent output of a line, or do you reserve a little to run lean? Remember, even though rough-cut capacity planning says you can operate to 100 percent loading, you will encounter a trapped capacity threshold when the requirements for safety stock become exponentially high. It is very easy to find yourself in this area of operational chaos. As an approximation of the trapped capacity threshold, you can load a manufacturing line up to demand less one standard deviation of system variation. After that, you enter an area that becomes rapidly unstable. Yes, enough safety stock can cover this variability— maybe a half-year’s worth. (How big is your warehouse?) But this will not be acceptable to any company interested in running lean, and it certainly embarrasses six sigma efforts.
The risk is clear: Only a system that has 100 percent supply and planning reliability, a perfect forecast, and no demand variability should be loaded to 100 percent of its effective capacity. Most systems run at about 80 percent schedule adherence and more than 20 percent demand variation, so some lines should be loaded to only 70 to 90 percent of average capacity.
This figure is unpalatable to most executives unless properly explained and quantified. But the good news is this explains why planners are struggling against persistent stockouts. Additionally, it provides a quantitative means to measure trapped capacity and determine the degree to which it can be controlled.
No amount of executive encouragement will enable an organization to break the laws of physics and probability. You either run fat and reactionary with excess inventory and capacity or get lean and proactive with effective capacity management. There are three simple steps to achieve the latter goal:
Optimally, you will view your capacity plan in the “whack-a-mole” format shown in Figure 3. This predictive modeling approach shows the impact of decisions about sourcing. You can see that filler 1 is overloaded in this model. Moving demand from filler 1 to filler 2 will have several effects. First, it will move changeover and gross production time from one to the other; second, it will change the trapped capacity profile, enabling it to go up or down. Of course, you need to know what it is before you try to execute this plan.
- Model your environment in statistical terms. Know the net variation of demand and supply on your lines.
- Build quarterly run strategies that everyone agrees to follow. Specifically, load lines to their most economic level, calculate safety stocks to match the environment, and establish your run strategy.
- Be disciplined and adhere to your run strategy.
When you are stable in your capacity planning, you can begin to recover your trapped capacity. The following strategies— in order of increasing difficulty— will help you get there.
Variability elimination. Combine products that are countercyclical so that the net demand of the two products is much smoother than the individual demands.
Variability concentration. Use an asset or product as the focus of flexibility in your schedule. Have it absorb the surges and troughs of demand. Pick a SKU that is a fast runner and, in times of surplus, build that inventory. In times of shortage, draw on its inventory. All the other SKUs can run at their proper cycles and inventory profiles. Weekly planning is simplified to asking the question of what should be done with the flexible SKU. Variability concentration makes weekly planning focused and lean.
Complexity reduction. This is standard SKU reduction with a twist. Add to the dialogue the cost of trapped capacity, and use it as a leverage point to kill C- and D-class SKUs. Follow the previous strategies, and you will find a collection of products that represent bad revenue. You even can adjust your standard costs so they are essentially activity-based, rewarding profitability. The results are simplified problems and eliminated bad revenue.
Flexible manufacturing. Use any staffing or scheduling technique that enables you to vary your plant’s output from week to week. Put a temporary labor plan in place to allow for an extra shift on weeks with capacity shortages. A line that can add eight hours of capacity any given week can reclaim exactly eight hours of capacity. This practice quantifies the value of flexibility. Demand shaping.
Demand shaping. Demand shaping is any technique that “sells” capacity or smooths demand. Operations works with brand and sales professionals to coordinate supply and demand streams. For instance, find a commodity- based product and make it your flexible SKU. If a surplus occurs, sell it. If a shortage occurs, buy it, or at least reduce its demand. Another example is working with marketing to promote products when there are forecast periods of surplus capacity. These efforts take an esoteric concept—demand shaping—and enable operations managers to lead in quantifying its benefits.
This may seem like a lot of work and data management—and it is. But the rewards of increased cash flow and stable fill rates are more than worth it. And if these are not enough reasons to look at better managing your capacity, imagine what you could achieve with all the time and energy your organization currently spends “planning harder.”
Steve Johanson is the chief executive officer of Supply Chain Toolworks and a founding partner of GTM Consulting. He may be contacted at (415) 533-9275 or