Chapter 5: Week Three: Benchmarks, Competitive Requirements, and Steering Team Review Number One

Start to put data to work.

The objectives of the third week are to review the results of the data collected during Week Two, including industry comparisons, metric queries, and other information required for the PMG benchmark survey (Chapter 4). Also on the schedule is steering team review number one, conducted by the project manager and chosen members of the design team.

Data Review

The first agenda item on Day One is to review results of the detailed query data collection (far-right column of the SCORcard metric template, Tables 4-1a-c) assigned during Week Two. The owner of each metric should lead the review of data, making adjustments to the type or form of information being sought, Table 4-3). By now, all actual data, plus large portions of the internal and shareholder benchmark sections of the SCORcard, should be complete. After the data has been reviewed, submitting the completed PMG benchmark survey is the last step in completing the customer-facing and supply chain-specific benchmark data of the SCORcard.

When the Fowlers team reached this point, the corporate controller, the vice president of sales and marketing—food products group, and the vice president of operations—technology products group volunteered to present their findings. (See Table 5-1.)

Table 5-1: Fowlers' industry comparison spreadsheet and raw data.

They had assembled company data and industry summary data for conglomerates, but also added summary data for the "food/meat products" industry and "media/movie, television, and music production services and products" industries. These provided meaningful comparisons for the company's food and technology product groups, respectively. They used the most recent actual data and didn't bother with current-year data that was reported as preliminary. The team filled out the appropriate sections in the Fowlers enterprise SCORcard but had little time for analysis. (See Figure 5-1.)

Source: Copyright 2001 Supply-Chain Council, Inc. Used with permission.

Figure 5-1: Fowlers' enterprise SCORcard.

Even on the first examination of the data, several things jumped out. First, the wide range of figures for cost of goods and SGA costs made it clear that there is no standard for reporting these numbers from one company to another. Operating income seemed to be a good comparison point for expenses. "But there's still no way to compare supply chain costs using the data we have so far," the coach pointed out. "You can't add cost of goods and SGA and supply chain costs to create a working SCORcard metric. Supply chain costs are activity based, and they borrow from the other two categories, so you'd be double-counting certain costs if you just added them. We'll have to wait for the results of the PMG survey to come back."

Second, the metrics of 197 days for the cash-to-cash cycle and 1.5 asset turns confirmed what many in the finance community seemed to feel about Fowlers: It utilized physical assets well and cash assets poorly.

Third, the 7 percent operating income in the food products group compared well against the food/meat products industry. It was a similar story for technology products. But sales were declining in each business, and profits were nearly half of what they had been the previous year. The strategy of charging a premium price for a premium product wasn't holding, and in fact was causing some customers to go elsewhere.

But as the team looked at the "parity opportunity" portion of the chart, their eyes got wide. As a conglomerate with a $1 billion in revenue, Fowlers' 7 percent operating income ($70 million) was only half the level of the conglomerate industry benchmark. To achieve parity in operating income, they would need to find another $70 million of additional supply chain performance.

Next, the corporate controller, director of logistics, and director of customer service took their turn. In addition to the review of enterprise supply chain and warranty/returns processing costs, they reviewed some data not included on the enterprise SCORcard. They learned that combined food and technology products delivery performance was 22 percent—meaning that just twenty-two orders out of one hundred were delivered on time and complete. Line-item fill rate was 80 percent, perfect order fill rate 5 percent, order fulfillment lead time 4.1 days, supply chain response time was 122 days, and production flexibility was sixty days. By the next week, they said they'd be ready to provide data for each product group SCORcard; enterprise delivery data was out of scope.

By this time, everyone was nearly speechless. Each measure in the customer-facing section was new, and it was the first time that the team had really thought about overall delivery performance through the customer's eyes. Especially disturbing to the team was the big picture about delivery performance.

The ensuing discussion sounded a bit like a classic session with a grief counselor; there was denial, bargaining, anger, and eventually acceptance of the data. Every member of the team wanted to bolt from the room and jump right into fixing the problem—like they had all done so many times before. Fortunately, it was the end of the day. Tomorrow's agenda would focus the team on something else. And a good night's sleep would put this information in perspective: The team had found an opportunity for the kind of improvement it needed to make.