A McKinsey survey into the American consumer shows that while consumers have more disposable income, they are choosy about where they spend their money, looking for ways to spend less. In addition, Deloitte's 2020 Manufacturing Industry Outlook highlights that trade tensions, supply chain volatility and a possible global economic slowdown will risk disrupting manufacturing.
Capacity planning, the periodic exercise to determine productive capacity to meet anticipated demand, needs to evolve to meet the challenges of 2020 and beyond. It needs to be:
Capacity planning is the process used to determine the capacity needed to meet demand. Almost without exception, all businesses and organizations are limited in terms of capacity. A capacity plan focuses on strategies aimed at utilizing existing capacity as efficiently as possible as well as the identification of additional capacity required to meet short-, medium- and long-term demand.
Capacity planning originated in manufacturing, and we will primarily focus on capacity and demand planning in this industry. However, the same principles can be applied in other fields such as the provision of services in call centers, IT and hospitals as well as in project management, engineering design and sales.
When considering capacity planning and how often you should do it, it's important to appreciate the difference between design capacity and effective capacity. A system's design capacity represents its theoretical maximum output but does not take into account limitations such as:
The effective capacity of a system represents the capacity available after taking into account all known limitations. In most instances, its actual capacity, what is actually produced, will be somewhat lower because of unplanned events, breakdowns and inefficiencies.
While the primary goal of a capacity plan is to determine capacity to meet demand, a secondary requirement is to minimize costs. Even better, instead of minimizing costs, companies might want to plan for the most profit. This requirement is often overlooked as production management is naturally incentivized to meet demand and achieve manufacturing targets. The consequence is that manufacturing costs may increase due to:
For these reasons, capacity planning processes should always place high emphasis on production cost as well as on production capacity.
Although actual processes vary, a capacity plan process usually follows a hierarchical top-down approach that starts with the strategic long-term plan and works down through several phases to a detailed daily production plan.
Performed as part of an organization's strategic planning, the strategic capacity plan determines the best estimate of the company's sales demand and required production capacity for the next three to five years. It provides direction to planners and determines capital investment requirements for future production capacity.
During annual budget preparation, sales and marketing departments forecast demand for the company's products and determine sales forecasts for the year. Production takes that information and determines available capacity, while taking into account known and planned production capacity. It also factors in capacity constraints, manning requirements and the number of shifts. Based on quarterly or monthly buckets, this broad-brush plan could change during the year. However, it's crucial the plan is feasible; that it can be achieved. Other aspects to consider are revenue and cost targets, as these determine annual profits, a metric closely watched by investors. During annual budget preparation, different scenarios are evaluated to determine the optimal capacity plan to maximize profitability.
The rough-cut capacity plan considers capacity for the next quarter, although in some industries this could extend to 12 or 18 months and may be part of the annual capacity plan. Production capacity is determined using information such as:
Depending on the organization, overall capacity may be calculated manually or using MRP and ERP software. The rough-cut plan should be reasonably accurate because long delivery raw materials need to be ordered, although minor changes are possible.
At this stage, the broad-brush approach is dropped and a detailed, day-by-day, step-by-step production plan is prepared. The detailed production plan is fixed and can't be changed except through a force majeure situation such as an unplanned break-down, unexpected component shortage or another unusual event.
Capacity planning is used in three distinct spheres:
Most medium- to large-scale manufacturing companies use capacity planning to determine their production capacity. It's used by automotive companies, appliance manufacturers, process industries, pharmaceuticals and in semiconductor manufacturing, to name a few.
The service industry is unique in that all services are direct; they can't be stored. The service is either available or not, so it's vital management use capacity planning to match supply and demand to provide an appropriate level of service. Examples include cloud computing services, airline seat capacity and fast-food restaurants
Human capacity refers to organizations that sell the skills of their team. Examples of human skills include project management, technical service technicians and call centers. Companies using human capacity planning include auditing companies, legal firms and engineering project companies.
Of all industries, manufacturing is by far the most complex when it comes to capacity planning. A typical manufacturing process requires the procurement and manufacture of numerous components, parts and sub-assemblies that go into a finished product such as an automobile, smartphone or refrigerator.
The capacity of each step of the manufacturing process has to be meticulously studied so it's possible to determine the actual capacity of each sub-operation on the production line as well as for off-line operations. This is a complicated process because equipment such as laser cutting machines and machining centers routinely make components for different production lines, each with their own demand and priority.
An important aspect of preparing a manufacturing capacity plan is determining bottlenecks that limit production. As a result, planners often turn to a technique known as the Theory of Constraints for help. Sophisticated production control software is needed to calculate production capacity and determine the best combinations of machinery and work centers to meet demand while minimizing overall costs.
The underlying concept behind capacity and demand planning is the provision of sufficient capacity to meet demand. Companies usually adopt one of three alternative capacity strategies.
An aggressive strategy is to provide additional capacity in anticipation of increased demand. It ensures there's always sufficient capacity but comes at the risk of installing more capacity than needed, especially if demand doesn't materialize or is less than expected. It's more expensive in terms of capital expenditure, but when correctly anticipated, it provides a competitive edge. It's best suited to long lead-time investments requiring significant capital expenditure.
The opposite of a lead strategy, capacity isn't increased until there's demand. This conservative approach avoids excess inventory and may work well when overall demand is high. On the other hand, the lack of capacity means its possibly frustrated customers will move to the competition.
Taking the middle road, a match strategy adds capacity in incremental amounts as demand changes. This low-risk strategy is viable if additional capacity can be added quickly and at a reasonable cost.
The choice of the right strategy depends on many factors and the steps the organization can take to mitigate risk. This in turn depends on the quality of the organization's business intelligence and the tools available to determine the best strategy in any circumstance.
While the US economy remains strong, there are concerns regarding lower growth, higher inflation and an economic slowdown. This, together with unpredictable factors such as the trade wars with China and other countries, turmoil in the Middle East and the rise of popularism, means that it's difficult to predict what will happen in 2020 and beyond.
This uncertainty also means it's difficult to predict future demand and to plan capacity. While some organizations may be tempted to adopt a lag strategy in case of a downturn, others are more confident in their direction.
The differences are not due to misplaced confidence, but rather to the intelligent use of business analytics, including prescriptive analytics, to determine the right strategies going forward. These tools allow companies to develop a greater understanding of their organization and its environment, as well as to make capacity and demand planning decisions supported by data instead of by intuition and hunches.
However, the degree of uncertainty means capacity planning cycles should be shorter, more frequent and flexible. This is to accommodate short-term changes and avoid being locked into long-term strategies that are no longer valid.
While MRP and ERP allow you to determine a feasible capacity plan for the next period of time, the software design precludes effective scenario analysis to determine which of several alternatives is best.
For this reason, most planners turn to Excel because of its ability to download data and to perform scenario analysis. Excel is a useful tool for rough-cut capacity planning thanks to flexibility and ease of programming.
While there's a place for Excel spreadsheets, their efficacy is undermined by data complexities and coding errors. Anyone who has worked with large spreadsheets knows that checking for the inevitable errors is difficult and tedious. Even one minor error can dramatically affect calculation accuracy.
Numerous capacity planning tools exist, although a great many focus primarily on resource planning instead of production or service capacity planning. MRP II and ERP software generally include capacity planning modules, although not all have the ability to consider constraints and few have optimization capabilities. Because of this, solutions proposed may not be realistic or feasible, and this is why many planners turn to Excel to complete their plans.
Prescriptive analytics provides one answer. Available as a package or application, this type of software allows users to accurately model their business, taking constraints into account and to produce a feasible capacity plan. Not only that, but it's possible to solve for different criteria, such as the lowest cost, highest production capacity and, most importantly, maximum profit.
Because the plan considers the entire business, it's possible to evaluate capacity requirements in various scenarios, such as for promotions, new product launches and increased demand. Such an approach also overcomes one of the major limitations of a conventional capacity plan, and that is the sequential top-down process that takes time and inhibits flexible planning. Instead, a prescriptive analytics approach allows planners to proactively change assumptions, input new information and run planning cycles repeatedly and as often as necessary.
The underlying principles of capacity planning have not changed, and that is to:
What has changed, though, is the degree of complexity together with the need to perform capacity planning more frequently. It's essential capacity plans are feasible but also accurate. Decisions need to be based on data and not instinct. Businesses need to understand the financial implications of capacity planning decisions and to know which of several alternatives is best.
This can only be achieved through the use of optimization software that's able to use the organization's data to model and evaluate the impacts of planning decisions. In this way, companies can: