Profit Optimization Planning

Make the most of your Black Belts

Regression equations,try fitted lines, and sampling are familiar terms to people in the quality field. There are tools that we use (i.e., planning matrices, tree diagrams, and flowcharts) to help our organizations optimize processes. These tools aren’t new, and their use isn’t limited to the quality profession. They’re being employed more frequently as the popularity of Six Sigma increases.

However, organizations are not exploiting their talent pool by utilizing the increased knowledge and skills of their trained Six Sigma professionals in other areas, e.g., profit optimization planning. As organizations are improving their operational processes, assessing their key performance indicators (KPI) and other factors, they should also consider using these same tools for profit optimization planning.

Amber, the general manager at an electronics manufacturer, decided to do just that. Her product manager, Matt, was responsible for a small handheld scanner selling for $46. For years he’d been using a low-price, low-promotion strategy. The prior year, Matt spent $20,000 on advertising and another $20,000 on sales promotion. Annual sales were 24,000 units for a profit of $28,000. Amber thought that a higher-percentage profit could be made on this unit and was anxious to find a better strategy to make this happen.

As she discussed this with Matt, their first step was to visualize some alternative marketing-mix strategies, with price, advertising, and promotion being the three factors that they identified as most influencing sales. Together, they laid out eight strategies for the marketing mix, as seen in table 1.

These strategies were formed by assuming a high level and a low level of values for each of the three marketing-mix variables (i.e., price, advertising, and promotion) and listing all of the combinations (for the two levels and three variables/factors: 23 = 8).

Joel, one of the company’s Black Belts, came looking for Amber, and saw the table of numbers on the whiteboard. With interest, he asked what they were working on, because the format looked familiar: three factors and two levels, although the factors listed in the top row were alien to his manufacturing mindset. He quickly visualized what had been ingrained in his head during his Black Belt training: Y = f(x1, x2, x3, … xn).

Amber, with her sharp mind, was quick to recognize Joel as an internal resource and decided to tap into his knowledge. Once Joel was apprised of the problem scenario, and was feeling reasonably comfortable regarding the relationship of the factors, he stated his concerns. He would normally approach this issue with a design of experiments methodology, varying the factors in a random manner and measuring the outcomes. However, with this marketing-mix case, a substantial amount of money would be involved and he had no idea about the time frame required to attain the desired outcome (sales) with each marketing mix.

Amber agreed that these sales figures were unlikely to be found through fitting historical data, and that conducting experiments would take too long. Given these  constraints, she decided to ask the sales director, Jane, for her estimates, because Jane had shown an uncanny ability to be on target.

Within a couple of weeks Jane provided the following sales estimates, seen in table 2.

2-2k15 ProfitOptimizeTable2

Joel was satisfied that now they could use the marketing mix to maximize sales using these “reliable” estimates. But at the same time, he had a nagging feeling that they were looking at the wrong outcome. Sales could be high but so could costs; shouldn’t they be looking at profits rather than sales? He’d heard of many companies that had gone bankrupt despite high sales.

Working together, Amber, Matt, and Joel realized they had to introduce a profit equation and then insert the different marketing mixes into this equation to see which maximized profits. Amber wrote the basic profit equation on the board:
Profit Z = revenue R – costs C

Revenue R = sales quantity × price/unit
Costs C = fixed costs + variable costs + marketing costs

Fixed costs = sum of salaries, rent, electricity, etc.
Variable costs = sales quantity × cost/unit (production and distribution variable costs)
Marketing costs = advertising and promotion costs

It was evident that profits are a function of the chosen price and the associated advertising and sales promotion budgets, and could now be determined for each of the eight strategies noted in tables 1 and 2.

Given the sales numbers and the scenarios above, it should be relatively simple to determine the best marketing-mix strategy for the highest profits.

At this point Joel, now feeling a bit more comfortable with this scenario, observed that there must be some marketing mix not shown that just might yield a still-higher profit. After all, these strategies were based on only two levels. What if they had chosen three levels?

As he delved into this further, Joel learned that the sales budget was often fixed, and was divided into different marketing mixes of advertising and promotion. Because each mix yielded different sales results, to gain further understanding he represented this as the model shown in figure 1.

MktgMixFigure 1: Relation of sales to different marketing mixes of advertising and promotion


Although this was still a model, the organization could use their historical data to explore further to find the optimal marketing mix for a given marketing budget. They might even be able to use this insight to optimally allocate a given marketing budget to their various products.

Amber had known that larger organizations, especially those in brand management, had used techniques like this over the years, but she’d never thought it would be within her means at her relatively small organization to have such capabilities. She decided to use this resource as a competitive advantage to not only optimize production operations and improve quality but also for profit optimization planning.

Are you making the most of your Black Belts?

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