Background

The example for this case study will be the fictional Edison Furniture company. Edison Furniture is a 75-year-old, highly respected office furniture supplier. It is the market leader in terms of quality, volume, and revenue. While there is a fashion veneer to office furniture design that changes with the times, at its core it is a utility, almost commoditized business. High-end, niche players have carved out space for businesses desiring an “exciting, innovative” edge for their employee workspace. However, Edison Furniture is the undisputed supplier of choice for Fortune 500 companies that office 17 percent of the workplaces in the United States.

In order to defend its leading market position, Edison Furniture offers configurable options for its core product items, such as color, size, and accent styles like desk-drawer handles. For this case study we will focus on a standard business desk with options for color and desk-drawer handles.

Furniture wood or laminate color is a slowly changing design preference. In the late 1900s, deep-wood colors such as dark cherry or mahogany dominated the market. In the 2000s, lighter colors such as ash, maple, or white on steel have become more fashionable. In the late 1900s, drawer handles were thicker and more angular. In the 2000s, slimmer, rounder handles are preferred. Style preferences change gradually as new generations climb the ladder and take their seats at the executive desk. Accordingly, as the market leader, Edison cannot pick and choose its clients' tastes; it must be able to serve new, upcoming executive tastes as well as the seasoned leader.

Cost volume is a well-established relationship in the business field; the higher the volume, the lower the cost per unit. Edison Furniture's challenge is to navigate the changing of styles and preferences, satisfying customer preferences while managing price and costs to maintain profitability.

Edison Furniture's primary channel for product orders is an online order form. The client purchasing agent can order items directly, or the sales agent may enter the order on behalf of the client. When choosing desks, clients are presented with a desk style to choose from, then a selection of colors and accent styles for the desk-drawer handles. Upon completion of the online order form and payment confirmation, a work order is sent to the distribution center to ship the selected item.

Edison Furniture's executive management has noted that although revenues have been increasing with the rate of increase of business spending, gross margins and inventory turns have been decreasing to alarming levels. Gross margin measures the profitability of items sold after transactional costs, such as cost of goods sold and inbound shipping, have been deducted from revenue. Inventory turns are calculated by dividing cost of goods sold by average inventory value. With revenues increasing and margins and inventory turns decreasing, Edison Furniture has an operations management problem.

The Executive Team commissions a cross-functional team, drawing from Finance, Sales, Marketing, Product Development, and Operations, to study the issue and recommend solutions. We structure our presentation of this case study using our Monetization Strategy Framework.

Business Levers

In order to confirm that the business objectives, hypothesis, and actions are in line and will deliver the expected business performance, the team develops their Business Levers (see Figure 7.1). The levers articulate what will be impacted to drive revenue or reduce costs.

Discovery

During the discovery phase, the team finalizes the Business Objective and Hypothesis. Following several working sessions and interviews with key departmental stakeholders, the team determines they have a “long-tail” product complexity problem. That is, on average, 70 percent of business profits are derived from 40 percent of 100,000 product configurations ordered each month. The top movers have a higher gross margin of 40 percent, whereas the long tail has a gross margin of 25 percent, implying that the long-tail products account for 40 percent of revenue.

Operations would love to chop off the tail and concentrate on the high-moving products but 40 percent of revenue is too much to sacrifice. The team determines they need to dive into the long tail to determine how they can improve productivity without sacrificing sales.

Decide

Category Tree

The team determines the Category Tree they will use to navigate the Decision Architecture. As the market leader, maintaining top-line revenue share is a key objective for Edison Furniture. While the Executive Team objectives are focused on profitability and productivity, the team does not want to lose sight of the revenue impact resulting from decisions and actions they consider. In Figure 7.2 we show the Category Tree the team develops.

Data Science

In order to identify the specific options or combination of options that give rise to unprofitable configurations, the team will rely on data mining to analyze the bill of materials for the 60,000 configuration and then sort the key component parts by cost and frequency of use. They will also use market-basket analysis to determine if there are patterns of order packages that might trigger an unprofitable configuration. There may be instances where the loss of profitability on the configuration is justified by the value of the package.

For each product configuration, the team will produce a profitability number along with an impact metric. The impact number will let us know how many other profitable configurations this configuration impacts. Lastly, the Data Science team will produce a velocity metric that will provide insight into sales volume for the configuration. A number over 1.0 lets us know that the sales volume for that product has been on the increase. Anything below 1.0, and the sales volume has been on the decrease.

Monetization Framework Requirements

The final step in completing the requirements is to review the guiding principles to see which ones the team plans to utilize.

The team now has a complete Monetization Strategy that they can use to communicate to leaders and stakeholders getting support for their plan of action and the changes to be implemented in business practices and processes.

Decision Matrix

With our requirements completed, let's put together our Monetization Strategy Decision Matrix. We are assembling the matrix to answer our two decision questions:

1. D1 Should we discontinue low-profit or unprofitable configurations?
2. D2 Should we raise prices for these configurations?

To answer our decisions, we will add our success metrics along with the new metrics the Data Science team has developed in the Decision Matrix based on the four building blocks of a decision matrix: acts, events, outcomes, and payoffs.

In our case, the acts are the choice to raise prices or retire a product configuration. The events are the individual product configurations or the Configuration ID. The outcome is the Total Units Sold per Configuration metric. Finally, the payoff is the Gross Margin Profitability metric. Table 7.1 shows the Decision Matrix with a sample of the product configurations.

In our sample, we can see some clear candidates to retire and others we would want to raise the prices on. For any product configuration with an impact number of 0 and low velocity and units sold, we would want to retire immediately. For any product configuration that has a high-to-medium volume and a velocity metric over 1.0, and is priced under the competitor price, we would want to raise prices immediately.

From our work, we can take immediate actions with the decision matrix on several of the configurations. For other configurations that are not so black-and-white, we would need to perform additional analysis.