Strategic Capacity Sizing & Investment
Capacity Strategy
The level at which we choose to operate that resource at any given time is the resource capacity utilisation, typically a fraction of the resource capacity.
A capacity strategy thus involves decisions on sizing, timing, type, and location of real assets or resources. Structuring the portfolio of real assets is a key part of operations strategy. Capacity planning hierarchy shows three layers of capacity decisions:
Strategic capacity planning addresses overall capacity for the long term
Tactical capacity planning considers the coming 1 to 18 months.
Executional capacity planning considers the coming 0 to 30 days
Decisions and Trade-offs
When firms decide on capacity investment, they face a rather simple trade-off: the higher the capacity, the more likely it can meet market demand, yet, the more costly and risky it is.
The capacity measure the maximal sustainable throughput of the organisation’s bundle of real assets. Typically, the firm incurs a one-time investment cost to acquire a real asset, and an operating cost every time the asset is utilised. The investment cost is a capital expenditure (CapEx), while the operating cost is and operating expense (OpEx).
Investment decision share three important characteristics:-
The investment is partially or completely irreversible meaning that one cannot recover its full cost should one have a change in mind
There is uncertainty over the future rewards from the investment
There is some leeway in multiple types of resources that have different financial and operational properties.
Sizing means deciding on the capacity level.
The second decision in a capacity strategy is the timing of capacity adjustments.
The third and fourth decisions in a capacity strategy involve the types of the resources we want and their locations. In practice, many types of real assets are deployed in the activity network for the production of several products and services.
Challenges for Capacity Strategy
Complexity - When making capacity decisions, the firm has to take into account many different factors, from the cost of capital, technology roadmaps, to access and maturity of the supply base and the stickiness of the customer base
Lead times: The lead time is the lag time between the investment decision epoch and the availability of the new capacity.
Lumpiness: when capacity is not endlessly divisible, it is called lumpy and comes in discrete chunks which prevent us from precisely matching capacity with some demands.
Fixed costs: Installing capacity often incurs a fixed cost which introduces economies of scale that make incremental investment suboptimal.
Measuring and valuing capacity shortages is not obvious. Depending on the patience of customers, unsatisfied demand due to capacity shortage may result in one of three short-term effects:
A delay of sales results when a customer signs on to a waiting list or backlog
A substitution occurs when a customer chooses another product or service of the same firm.
A lost sale results when a customer no longer wants the good or service, or goes to a rival
Forecasting and modeling uncertainty
The goal of such analysis is often to find the most likely outcome and create a strategy based on it.
The uncertainty that remains after the best possible analysis has been done is called residual uncertainty. Courtney, Kirland, and Viguerie partition that residual uncertainty in four levels:
Level 1 is a clear-enough future modeled by deterministic forecast
Level 2 represents alternative futures or a discrete set of scenarios
Level 3 is a range of futures or a continuous distribution of outcomes
Level 4 represents true ambiguity, which is rather rare and often transitory
To estimate the value, we need a model that predicts all contingencies that can impact the future value of capacity utilisation. This model should include a forecast of the evolution of the following factors:
Demand
Supply
Technology
Competition and environment
Useful forecasts therefore incorporate a measure of this uncertainty, which we will call forecast error. The forecast error can be quantified by the spread in scenarios or the standard deviation for range forecasts.
A forecast should contain all available information on the future evolution of X. It must reflect a good understanding of all possible outcomes with their likelihoods.
In theory, a forecast should contain all the statistical information that describes the future evolution of X, which is a time-sequence of random variables.
The second key property of a forecast is that is should specify at least two numbers.
Forecasts should contain all the available information the future evolution of demand, supply, technology, competition and environment. Forecasts must include forecast uncertainty. Aggregate forecasts have higher relative precision.
It is important to distinguish between outcomes that leaders can influence and those they cannot.