Most organizations have a hard time minimizing the discrepancies between their capacity and the demands of their customers. As a result, most of them are under-utilizing resources or are unable to fulfill customer demand.
These inefficiencies can have a massive negative impact on an organization. In this competitive environment, organizations cannot afford the luxury of being inefficient when it comes to their capacity planning — the balancing act of ensuring available capacity meets required capacity while maximizing the return on an organization’s assets, minimizing costs, and effectively managing risks.
An organization’s supply chain must be flexible enough to accommodate fluctuations in demand, while also guarding against investments in capacity or production before it is needed. Most organizations receive mediocre results from their capacity planning because they fail create a plan that is feasible — or doable given their constraints — and to align the plan with their strategic objectives. In other words, organizations fail to create a plan that is both feasible and optimal — the former being more important that the latter.
With a capacity plan that is feasible and optimal, organizations can drive better performance, avoid costly mistakes, and boost confidence among an organization’s teams and shareholders. An organization will be able to meet financial and inventory objectives while being able to fulfill service level objectives, which most organizations believe is a trade-off.
In this article, we will look at how to ensure an organization’s capacity plan is both feasible and optimal by discussing the various constraints that cause organizations problems and how to align strategic objectives with the plan.
Is the Capacity Plan Feasible?
The biggest point of failure in most capacity plans is they are not feasible, as I mentioned earlier. Organizations have more problems ensuring their plans are feasible than ensuring they are optimal.
Feasible capacity plans can be executed within the realities of the business. They are not starry-eyed objectives an organization will never be able to execute given all of their important constraints. A feasible plan must account for the constraints, trade-offs, regulations, policies, and the financial requirements of a business.
- Constraints and Tradeoffs: A constraint is anything that hampers an organization from being able to produce more of what it strive for. Some constraint examples are labor, equipment, and third-party capacity limits.
- Regulations: Many manufacturing companies, especially those in the chemical industry are heavily regulated when it comes to their production. For example, factories are limited in production by the regulation of greenhouse gas emission the EPA enforces. So, while production may be possible from a physical standpoint, it is not possible from a regulation standpoint.
- Financials: It is possible for organizations to grow themselves out of business. Organizations are sometimes limited from a cashflow perspective. If an organization fails to convert their cash fast enough, they could find their survival being threatened.
So, how does an organization know if their plan is feasible given their constraints? Typically, if a complex organization (like CPG, chemicals or resource planning) is using rules for their planning, then it is likely the plan is infeasible.
For example, let’s say a firm is using Excel spreadsheets to plan their capacity. Their planning process would roughly follow this kind of structure
- Begin with demand and work backwards to find inventory needed, taking into account existing inventory.
- Adjust inventory requirements for lead time and use them to define production requirements.
- Assign production requirements using some convoluted ruleset. For example, assign production of product 1 to line A, B if it overflows. Assign production of product 2 to line C, B if it overflows, etc.
- Aggregate production requirements into overall hours, manually adjust if capacity 100% of capacity available, and work backwards to determine the number of shifts needed.
At this point, the rules are so complex that organizations fail to properly consider their resource allocation and labor rules. For example, an organization may physically be able to increase production by 20%, but not from a greenhouse gas emission standpoint.
An organization may be able to physically increase production by running over-time shifts, but this may not be practical from a financial standpoint. There are thousands of examples how an organization could be limited by their constraints, but tools like Excel, fail to allow organizations to create feasible capacity plans.
For a plan to be feasible, it must be able to meet the output given the constraints over a specified time period. A feasible plan represent reality, respects an organization’s constraints, and it the biggest point of failure when it comes to effective capacity planning.
Is the Capacity Plan Optimal?
The second factor in creating an effective capacity plan is optimization. Most organizations are able to create optimal capacity plans, they just fail to integrate it with feasibility. An optimal plan allows organizations to maximize or minimize the objectives they are trying to meet — whatever they may be. For example, an organization may wish to achieve better results in terms of profitability or a higher service level, so their plan should be organized around achieving these objectives.
So, how can an organization determine if their plans are the best plans or just some plan? Let’s look at an example. For now, assume that an optimal plan is one that delivers the best service levels at the lowest cost (subject to the relevant constraints, regulations and policies of the business).
Determining if a plan is optimal is a lot easier than trying to find out if it is feasible. Here are some questions, organizations should be asking their teams:
- Are we using linear programing (an “LP”) or are we just using simple rules?
- If we are using an LP, what is the objective function?
- What decisions is the LP helping us make? These can include sourcing, optimal shift configuration, inventory, build ahead, or capital investment — ideally it will use all of these.
Organizations who are running linear programs that are accounting for various constraints, typically are creating capacity plans that are optimal. Organizations need to evaluate optimization under a scope of feasibility to create the best capacity plan.
The organizations that are relying on heuristics or rules to find optimal solutions are often disappointed. Simple rules allowed organizations to make quick decisions at the cost of optimal decisions. This was needed twenty years ago, when finding an optimal solution may have taken hours.
However, innovative technology has allowed companies to run optimization calculations quickly and efficiently. So, there is no reason more organization should not be using linear programming. For a plan to be optimal, it must find the best solution that is aligned with an organization’s objectives given their constraints.
It is not enough to ask, “How can we balance available capacity and required capacity?” Organization now must ask the question, “Can we balance capacity?” Organizations that fail to balance capacity create inefficiencies in either under-utilized resources or customer demand.
However, the worst organization is the one that seeks to optimize their capacity plan without planning for feasibility. These organizations end up paying for costly mistakes and driving down the overall performance of an organization. Capacity planning that is both feasible and optimal is not easy, but is certainly becoming required for the success of an organization.
Editor’s Note: This post was originally published April 21st, 2017 and revised September 30th, 2018.