Ensuring Profitable Product: Costing And Optimisation

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The economic parameters explained here give us indication of the profitability and risk of our product development work. Ideas that give more profit with less risk are always preferable

Business economics plays an important part in the product development process. There is no fun in developing a product that is not profitable. As product architects, we need to ensure the technical, environmental as well as financial viability of our products. The product must be socially acceptable and environmentally friendly.

A well-designed product must ensure the viability of the three Ps of sustainability—planet, people, and profit. Ensuring these 3Ps is the basic requirement to remain in the market for a long time.

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In the formative stages of product development, we start with estimates and incomplete ideas. During the development process, there are many changes in our product idea as we get and process more information.

Sometimes the design changes are competitor driven, sometimes by our innovation. During the idea formation phase, it is important to quickly evaluate many alternatives and select the better ones. As we progress with our development, we add more details and get more accurate information to check the viability of our product ideas.

In the QFD (quality function deployment) analysis itself, we should include the requirements of environmentally friendly products as well as social requirements. Many of the environmental requirements, like the conservation of energy and other resources, also directly impact profitability. Even in cases where such direct relation is not present, consumers prefer a product that is less harmful to the planet.

Analysis of the profitability often poses a problem as different expenses happen at different times and may not be apparent right at the beginning. One needs to account for all the revenues and expenses to get an idea of the profitability.

It is easy to get an idea of the material that goes directly into the product, which is normally available as bill of material during the design process. We can estimate labour requirement by comparing it with similar processes.

There are many published time standards, like the MTM time standards for manual processes, or the Boothroyd and Dewhurst Assembly Time Estimation for automated lines, that help us to estimate these numbers.

From our estimation of machine and labour capacity, we get the number of machines and labour required for our target production volume. Here, the process planners must take make-or-buy decisions.

For manufacturing our designed product, we need to purchase or lease machines and equipment. In addition to processing equipment, we also need equipment for storing and handling raw materials, work-in-process materials, and finished goods. We also need land for all these materials and equipment.

Money comes with a cost. When we keep some money in the bank, we earn interest. Similarly, when we take a loan, we need to pay interest. In business, the entrepreneur must invest in all the land, machines, and materials. He earns his revenue after making the items, marketing them, and receiving payment from the dealers. There is a gap between the entrepreneur investing his money and earning the revenue.

Business needs working capital to run its operation and this money comes with its cost of capital. We need to estimate the amount of money needed as well as the time of its requirement, and when we get back the money as revenue.

Profitability performance indicators

There are a few standard performance indicators that help us to understand the economic viability of our product idea and compare it with other businesses. Let us discuss a few popular indicators and compute to see how our product idea compares.

Net present value. Net present value (NPV) starts with a presumed cost of capital. The investor has the option of keeping his money in a bank, or some bonds, and earning interest. He needs to earn something better from the business than the return he will get from his safe investment in a bank’s fixed deposit or bonds to justify the effort and risk he takes with his money.

The notional minimum acceptable interest rate that the entrepreneur will accept is the cost of capital. We estimate the time and amount of various investments and earnings and convert those into the monetary value at the present date with interest charges using the cost of capital. The total of all these present values gives us the net present value.

Mathematically,
NPV=Sum [Rt/(1+r)t] – Sum [It/(1+r)t]

where, Rt is the revenue earned in period t, It is the investment made in period t, r is the cost of capital.

In the initial phase, we start our analysis with estimates and get an idea of the economic viability of our idea. Let us take the case of our charge controller. As a rough estimate, the raw material of the product will cost around ₹ 600.

We need to invest approximately ₹1,000,000 in machines and equipment. Investment in land and building will be around ₹1,500,000 Lakhs. We estimate that factory building will take one year time.We hope to sell around 100 pieces per day at ₹1,000, excluding taxes and commissions. We estimate our business cycle from raw material procurement to getting the money from the dealer will take approximately 30 days. This gives our working capital requirement of ₹3,000,000, as calculated below.

30 days×100 pcs/day×Rs1,000/pc=₹3,000,000

Let us say we expect to make a profit of ₹400/pc. Assuming 25 days working in a month, our investment will generate an income of ₹400/pc×100 pcs×25 days=₹1,000,000 every month. For ease of calculation, we convert all recurring earnings or expenditures into a one-time amount by dividing it by the interest rate.Details of NPV calculation are given in Table 1. If we assume our cost of capital to be 10% per year, we find that our project has a robust NPV of over ₹5.7 million. Hence, we can proceed with this product with confidence.

Table 1 NPV Calculation
Year Description Amount (`) NPV (`)
0 Purchase of land and building -1,500,000 -1,500,000
1 Purchase of machines and equipment -1,000,000 -909,091
1 Working capital -3,000,000 -2,727,273
1.08 Earning (every month) 1,000,000
Eq. one-time earning 12,048,193 10,866,599
Net NPV 5,730,235


Return on investment (RoI)

In our previous NPV calculation, we assumed a cost of capital. RoI calculation does away with that assumption and instead finds out the interest rate that makes NPV equal to zero. The RoI calculation is quite complex and is best done using an inbuilt function in spreadsheets or a special calculator that has such functions. Using the values from Table 1, we can find the RoI. This comes out as 582%. This value looks too good to be true. We shall revise our analysis once we gather more details about our product.

Profit margin

While making a product the money we spend is called cost. The cost of the product is a result of our decisions. We cannot change it. While we sell the product, we decide to sell it at a price. Price is our decision, and we can change it quickly. The difference between price and cost is our profit.

Profit margin is the ratio of profit over price. We use it to compare how our product compares with others in the market. Innovative products tend to have a higher profit margin, whereas products that have competition have a lower profit margin.

In our case, our target price is ₹1,000 whereas our product cost is estimated to be ₹600. So our profit margin is (1,000-600)/1,000=0.40. For our product, which we want to make very innovative, 40% profit margin may be justified for the initial period.

Breakeven quantity

There are many unpredictable things in product development. One must invest a fixed amount in developing the product and purchasing machinery and equipment. Once we start selling the product, we start recovering our investment.

In the development expenses, there are two parts. One part is the fixed cost. These are the one-time investments in machines, project costs, etc. Another part is variable, which varies with the number of products we make. Costs of raw materials and wages of workers fall in this category.

Breakeven quantity is the number/quantity of the product we must sell to recover our investment. Numerically, it is Fixed Cost/Profit per item. For our charge controller, we need to invest ₹5.5 million. Our profit per item is ₹400. So, we need to sell 13,750 pcs of the charge controller to recover our investment.

The breakeven quantity gives us an indication of the risk of our investment. If the breakeven quantity is very big then we run a bigger risk.

Payback period

While breakeven quantity gives the number of items to be sold to make a profit, we also need to know how much time it will take before we see profit from our work. The payback period gives us that number. Payback period is the ratio of breakeven quantity and per day production.

We plan to make 100 pieces per day. Hence our payback period is 13,750/100=137.5 days.

These economic parameters give us an indication of the profitability and risk of our product development work. Ideas that give more profit with less risk are always preferable.

Risk assessment

Apart from economic analysis, we should also do a risk assessment of our new product development. Risks for a new product come from changes in the sales volume, different prices, and periods. Changes in government policies can also cause changes in our original plan. Another risk is competition.

As we develop new products, our competition may also come up with similar or better products. During product development, we need to maintain confidentiality to protect our competitive lead. Our sales projection should consider the effects of future competition.

During product viability analysis one should see the effects of both optimistic as well as conservative estimates and decide if the product development efforts look viable.

Selecting alternatives

Product design involves selecting the right design from different alternatives. At every stage of the design, we have multiple choices. A good product architect must evaluate the impact of his design decisions in totality.

In our earlier article, we discussed concurrent engineering. Making design decisions with a big and diverse team is challenging work. This coupled with the analysing effect of our design in totality create some big challenge.

To understand the problem let us consider we have five modules in our product and our manufacturing has four steps. If we generate even a modest three alternatives for each of these nine steps, we land up with a total of 39 or, 19,683 alternate scenarios. Evaluating such a large number of scenarios would be very difficult.

Dynamic programming

There is a branch of mathematics called operations research that deals with efficient ways to solve business problems. Decision problems that involve finding the best solution requiring a series of decision steps are known as dynamic programming problems.

In dynamic programming we view the entire process as a few distinct steps or stages (see Fig. 1). Each stage receives an input. We control the outcome of each step by changing a decision variable. A combination of input and decision variable produces the output. In addition to output, the process also gives a certain return. The output of each stage becomes the input of the next stage. Our dynamic programming problem is to find out the best return from all the stages to produce the final output.

Fig. 1: Dynamic programming problem
Fig. 1: Dynamic programming problem

There are many standard approaches to solving such problems. One of the simplest and most popular methods is the branch and bound algorithm. This method helps us to cleverly reduce the number of evaluations required to find the best solution.

For instance, if we know that out of our 19,683 combinations, some of the combinations are infeasible, then we can eliminate the entire branch that has the infeasible combination of options. Similarly, if we have reached an option that is costlier than a combination we had found earlier, then we can drop that branch as well.

In the branch and bound method, we find these costlier or infeasible branches and eliminate them from further search. This way we get the best possible combination with fewer number of evaluations.

I prefer to decide on the product architecture manually. There are many powerful computer software for solving these kinds of problems. However, internal workings are not evident when we solve these problems on a computer. Knowledge of the intermediate steps often helps in triggering further innovation.

Inside-out tabular process

The manual process that I use calls for charting the alternatives in a series of tables, I call it an inside-out tabular process. In the table, we put the inputs in rows and the outputs in columns. The options go into the cells. If we have more than one option for input resulting in the same output, we select the best input option for each output. In the next stage, the output of the previous stage becomes input. We evaluate all the stages to find out the best combination of choices.

We use this process to get a first draft version of the charge controller parts. Our first requirement is to divert power from the wind turbine to a dummy load. We can use a solid-state relay or a solenoid relay for this purpose. From the websites of bulk electronic parts suppliers, we can get prices for various options, which are listed in Table 2.

Table 2
Stage 1-Power Switching Options
Options
Input 48V, 64A Power Switching ASR-SI240D60ZW-M (₹2,783) A31ACQ24VDC1 (₹150) 3-1414937-3 (₹741) CB1AH-P-12V (₹298) A3F1ACQ12VDC1D (₹155)
A31UCQ12VDC1R (₹160)
Output (Switching Current) 7.5mA 75mA 83mA 117mA 150mA
Return (₹) 2,783 150 741 298 155

We club options that require a similar output current in the same column. For our design requirements, we have six options and five different current requirements. We can drop the option that has the same current requirement but is costlier from further analysis.

In our next stage, we investigate the design options for driving the relay. Most microcontrollers can supply about 15mA continuously. Solid-state relays can be driven directly from the microcontroller, relays that need about 100mA can be driven by a transistor, and for higher current requirements we can use a MOSFET switching transistor.

We add the input cost with the cost of the device and list it with the option. For example, we need 7.5mA current for a solid-state relay. We got the cost of the relay as ₹2,783. So, the total cost till Stage 2 for this option works out to be ₹2,783. For relays that require 75mA current, we need a transistor, resistor, and a flyback diode. The total cost of these is ₹20, and the net cost till Stage 2 comes out to be ₹170.

We evaluate the other options in a similar manner (Table 3). Here we find that relay A31ACQ24VDC1 with BC148 transistor and flyback diode works out to be the cheapest option that fulfills our requirements.

Table 3
Stage 2-Control Current
Switching Current Best Return (₹)
Inputs 7.5mA 2,783 Direct `2,783
75mA 150 Transistor BC148,
flyback diode
₹170
83mA 741 Transistor BC148,
flyback diode
₹761
117mA 298 IRFZ44N,
flyback diode
₹328
150mA 155 IRFZ44N,
flyback diode
₹185
Control current 15mA

In the next stage, we look for the analogue voltage sense options. We need to sense three voltages, battery, current in, and current out. We have the option of using a microcontroller based ADC, or we can use an ADC chip. Some microcontrollers have only one or two ADC. We have a third option of using an analogue multiplexer to extend the ADC port of such microcontrollers. Table 4 gives these options and the resulting cost till Stage 3.

Table 4
Stage 3-Analogue Voltage Sense
Analogue Sense 3-Channel Best Return Options
Input ₹170 MCP3204/3208
ADC – SPI
₹365
Arduino Nano
₹450
CD4051
8-channel analogue multiplexer
₹189
Output SPI CPU M.plex
Return ₹365 ₹450 ₹189

Let us look into CPU options in Stage 4. Our expected production volume is low. For such low-volume requirements, it is better to use off-the-shelf CPU modules. We have options of using Arduino Nano, NodeMCU, ESP8266, ESP32, or Raspberry Pi Zero W. Cumulative best cost of these options are listed in Table 5.

Table 5
Stage 4-CPU Options
CPU Options
Inputs
SPI ₹365 ESP32 ₹745 NODE MCU
₹575
R.Pi Zero W
₹1,715
CPU ₹450 Arduino Nano
₹450
M.Plex ₹189 ESP32
`569
Node MCU
`399
Outputs
Arduino Nano ESP32 Node MCU R.Pi Zero W
₹450 ₹569 ₹399 ₹1,715

Raspberry Pi is a single-board computer. It comes with its operating system and ports for a keyboard and monitor. We evaluate this option for future requirements.

We look into the option to add Wi-Fi connectivity in the next stage. Arduino needs a Wi-Fi module. Other CPU options have built-in Wi-Fi. Table 6 gives the options and cumulative cost till Stage 5.

Next, we need to add an SD card for data storage. With Raspberry Pi, the SD card is in-built. Other options will need to add an SD card reader module and an SD card. The cumulative cost of these is listed in Table 6.

Table 6
Stage 5-Memory Option
SD Options
Inputs Arduino Nano ₹557 SD module
₹868
ESP32 ₹569 SD module ₹880
Node MCU ₹399 SD module ₹ 710
R.Pi ₹1,715 In-built ₹1,715
Output Cloud client Stand-alone server
₹710 ₹1,715

Conclusion

This analysis helps us to find the most cost-effective design solution for our charge controller. For our product, the most cost-effective configuration works out to be the A31ACQ24VDC1 relay, BC148 transistor, and flyback diode, NODE MCU module with a CD 4051 analogue multiplexer, and SD card with a reader.

You might have noted that after the analysis we landed up with cost figures that are different from our original assumptions. We need to add other miscellaneous details and re-validate our business case. In addition, we also need to make our design robust. We shall discuss technical risk analysis and tricks to make our product robust in the next article.


Soumyanath Chatterjee is former TVS Motors Chair Professor at Industrial and Systems Engineering Department, IIT Kharagpur. His expertise is in Product Development and Supply Chain Management

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