Traditional methods of maximizing profitability focus on maximizing margins through the levers of market price and product cost. These methods ignore the significant impact of difference in throughput between different products. A product with high margins but low throughput may fetch a lower gross profit than a product with low margins but high throughput. The impact on profits in such scenarios is illustrated in Figure 1.
Consider a capacity-constrained situation where market demand is greater than what we can service. For the sake of simplicity, let us assume that the market demand for both products are equal and each demand by itself is greater than capacity. How do we select the demand that we should service? Traditional decision making methods favour the demand that nets the highest margin. In our example, traditional decision making would choose to service demand for product B. Now our capacity to supply product B is 400 tons per day or 12000 tons per month. This translates to a profit of Rs.18 Cr in a month.
Fig.1a. Traditional decision based on margin maximization
Now consider what we would have made had we serviced product A. Our capacity to supply product A is 500 tons per day or 15000 tons per month, which will fetch us a total profit of Rs.20.5 Cr per month.
Fig.1b. Decision that accounts for throughput differences leads to higher profit
It is indeed very simple and intuitive to make a back of the envelope calculation and arrive at the profit-optimal choice when we are dealing with a limited number of products, simplistic market demand situations and a single source of supply. Suppose we are a medium-sized organization with 5000 different product variants and 800 customers in 15 countries. Our prices and costs would vary across different customer segments and geographies. How would we make the decisions to achieve the maximum profit?
To support decision making in such complex scenarios, there is a need for a simple measure that captures the impact of price, cost as well as throughput on profits. This new measure is Contribution per Operating Hour (CPOH). The simplistic definition of CPOH is
CPOH = Margin (Rs. per unit) x Throughput (units per hour)
The concept of CPOH can be understood better if we forget for a moment that we are selling products. We are not selling products. We are selling time. You have 24 hours every day. How would you encash these 24 hours to generate the maximum total profit? You would sell your first hour to the customer who pays the highest for that one hour. Then you would sell your second hour to the next highest payer. And so on until you run out of time.
CPOH allows you to compute exactly how much we would earn for each hour of our time by servicing a given demand. At a tactical level, CPOH should therefore be used to select which portion of the demand basket should be serviced from the limited supply. A simple way to do this is to plot a scatter of the demand as shown in figure 2. Against this scatter, plot isoclines (similar curves representing the tradeoff between margin and throughput* while maintaining the same CPOH) representing different CPOH levels. These isoclines are represented by the equation xy = c, where x is throughput, y is margin and c is CPOH. The gaps between isoclines give us CPOH bands that slice the demand basket into multiple segments.
* Computation of throughput in a real manufacturing environment can itself be a difficult exercise. A detailed discussion of how to compute throughput will be presented in later posts. A great concept-teacher on this subject is a bestseller named “The Goal: A Process of Ongoing Improvement” by Eliyahu M. Goldratt. For now, let us assume that we know the throughput of different products.
Fig.2a. Scatter of Demand Plotted Against CPOH Levels
We plan to service the demands in the highest CPOH band first (Ref. figure 2b). We then plan to service demands in the next highest CPOH band, and so on until we run out of supply capacity (in terms of hours of resource availability). This method will lead us to select the most profitable sales mix.
Fig.2b. Operational and Tactical Demand Selection using CPOH Bands
At a strategic level, you should use CPOH in conjunction with a cost-benefit analysis model to shape your customer-product portfolio for higher profitability. You can do this by plotting a bubble chart of your demand against CPOH isoclines. The size of each bubble represents the sales volume and market potential in each segment. This chart provides visual cues to suggest shifting of the customer-product portfolio towards a segment. The example in figure 3 shows options for shift in portfolio that can be considered for a cost-benefit analysis.
Fig.3. Strategic Decision Making using CPOH Bands
This decision making tool can be applied to a variety of business problems across different industries. Examples of application of this tool to some common problems are cited below:
- A steel company may use CPOH to decide the minimum additional charge for supplying a non-standard grade or a difficult-to-make sheet size when entering into a contract with a customer.
- A chemicals company may distribute capital expenditure to increase sales volume of those products for which the marginal increase in CPOH per dollar of cap-ex is highest.
- A consumer goods company may use CPOH to rationalize product segments, by discontinuing those low-volume products that net low CPOH.
The problem definitions presented in this discussion are still simplistic compared with the real business challenges that most companies face. Practical considerations such as multiple sources of supply, cyclicity of demand, multiple bottlenecks, shifting bottlenecks, customer relationships etc. present greater complexity. These complex scenarios will be discussed in the next few posts. Watch this space.
