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 to 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 strives for. Some constraint examples are labour, 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 emissions. 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:
1. Begin with demand and
work backwards to find inventory needed, taking into account existing
inventory.
2. Adjust inventory
requirements for lead time and use them to define production requirements.
3. 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.
4. 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 labour 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 represents 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:
1. Are we using linear
programing (an “LP”) or are we just using simple rules?
2. If we are using an
LP, what is the objective function?
3. 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
organisations 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 organisations 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.
Takeaway
It
is not enough to ask, “How can we balance available capacity and required
capacity?” Organisations 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.
Contributed by: Rod Stout, Business Modelling Associates (BMA). BMA is the official distributor for River Logic’s Enterprise Optimizer® platform across Africa.
