agreed, “The answer could be one, any, or even all of them. I see how a compound metric might make it hard to compare one company to another, because they could be getting a similar result for very different reasons!”
“Yes,” I said. “For example, we found that the most common driver of cash-to-cash improvements was lengthening the days of payables and paying suppliers later.”
Joe rubbed his hands and smiled, and said, “That sounds familiar. It worked for one quarter before it caught up with us. This is a difficult discussion to have with our financial team. When the push for cash is on, it sounds so simple to increase payables; but, I know that we end up eating it when our operating margin rises a couple of quarters after the change.”
“Another compound metric is the ‘perfect order.’ Do you use this for customer service?” I asked.
“We tried it a couple of years ago, but dropped it because it was too hard,” Joe said.
I continued, “I understand. This metric lacks an industry-standard definition, and varies from company to company, but many companies try to use it. The most common definition is based on an equation using three metrics.”
I wrote on his whiteboard:
I spun around and continued, “Similar to the cash-to-cash discussion, if there is a change in the perfect order, the answer is not obvious. Instead, companies have to ask:
• What drove the change?
• How have these three elements changed over time?
• What affected the performance in the three components of this metric over time?
As a result, companies should use caution in using compound metrics and absolute numbers. Compound numbers can drive the wrong conclusions and absolute numbers do not allow the level of comparison needed for benchmarking between companies.”
Joe was now pacing. “So much to learn. So much to do. How do we get started?” he asked.
Benchmarking Companies over Time
“There are many ways that we could work together,” I said. “The methodology doesn't just apply to the benchmarking the whole company. It can also yield valuable insights at a finer, more granular level by benchmarking divisions. In our work with clients, we find that segmentation of the business by division, and by geographies within the company, yields valuable insights. When we do this more detailed analysis – analyzing divisional and geographic data – the concepts are more quickly grasped by the team.”
Then, I showed him an example of this type of analysis as illustrated in Figure 1.6.
Figure 1.6 Example of an Orbit Chart Comparing Two Businesses within a Corporation on Inventory Turns versus Operating Margin
Source: Supply Chain Insights LLC, Corporate Annual Reports 2001–2012 from One Source.
“See, Joe, in this example, the patterns of the two divisions of this company are very clear. Division 1 is operating at a higher potential and making year-over-year improvement, while Division 2 is struggling to make clear headway. The use of root-cause analysis to discover the ‘why’ can help the organization drive continuous improvement and maximize the potential of Division 2 on the Effective Frontier,” I said.
Joe then said, “I love it. I would like to talk to you about doing this type of analysis for all our divisions and geographies. I think that it could help our team. Let's talk about how we could do this. We've put so many systems in place already to try to see patterns in our data, yet we've still not got any insight.”
Rethinking Metrics: Using Technology to Manage the Organization in the Information Age
I looked at my watch and said, “I know we only have another hour together, but let me give you a short answer of why I think this has happened, and maybe we can pick it up when we meet again. The ability to drive data-driven decisions has improved through the use of technology. Dashboards, scorecards, in-memory reporting, and visibility technologies make it easier to manage metrics within a company, but companies have to be clear on the metrics strategies. This is the challenge for every Joe like you.”
With that, Joe laughed. “So, I am not unique? Do other organizations have the same problem?”
I nodded, saying, “Many times companies will leap to improve metrics through technology without doing the hard work of figuring out which metrics matter and how to align the key performance indicators into a metrics strategy.
“There is also an issue of functional myopia. The views of operations and finance do not easily align. One of the problems is that financial metrics are backward-looking and transactional, while operational metrics are forward-looking based on flows. But the metrics you get from technology are based only on historical data. It's like trying to drive a car on a winding road at 60 miles an hour while looking in the rear-view mirror to steer. Closing this gap requires descriptive, predictive, and prescriptive analytics. While descriptive analytics enable reporting and data analysis, predictive and prescriptive analytics enable the management of operational flows. In contrast, predictive analytics enable operational alerting while prescriptive technologies recommend actions to take. Robust analytics are essential to ensure metrics alignment and are an important step in driving success on a metrics journey.”
“This has certainly been one of our issues,” said Joe. “We have a guy on the sales team who's really smart and can put together spreadsheets so we can analyze all kinds of things, but they're all about what's already happened, and the sales forecasts… Well, you know, they're really only good about three or four months out, and the sales team always inflates the numbers. It's an ongoing problem.”
“When embarking on a project to improve metrics, the average Joe, like you, will need to work with the information technology (IT) department to build measurement systems. This includes self-service reporting, dashboards and scorecards, and alerting systems. Analytics technologies are closely woven into a metrics project to make progress possible. It should be easy, but it is not. I would like to tell you more, but right now I really must be going. As much as I have enjoyed the discussion, I am late.” I then suggested that Joe read the article that I had just completed, “Managing Metrics in the Information Age.” We agreed to discuss it at our next meeting.
With that, we shook hands and I left my article on Joe's desk. Some excerpts are offered in the following feature.
Managing Metrics in the Information Age
Today, business leaders live in the Information Age. Technologies make new ideas possible. Data flows quicker and computational power enables quicker assessment of complex problems. Decisions can be more data-driven and real-time information enables new capabilities. More and more, metrics can be measured. Targets can be assessed more quickly. However, this only adds value if the technological advancements can be successfully aligned with business outcomes. This is the challenge.
Why is there a problem? Simply put, companies are new at it. We are only 40 years into the Information Age. The adoption of technology in the Information Age followed the Industrial Revolution. The Industrial Revolution was all about mechanization. There was a shift from making things by hand to the mechanization and adoption of manufacturing processes. The focus was on the management of physical assets. It was all about the control of financial assets and liabilities.
