Metrics don’t tell the whole story
Metrics are a huge part of our lives. From the lowest level process to the highest level business initiative, all our processes have a complete set of metrics. Today almost nothing goes unmeasured.
In manufacturing, metrics are applied to four main areas: cost, quality, speed and profit. In each area, some metrics are forward-looking and some are rearward-looking.
Metrics are as powerful as they are ubiquitous. When metrics don’t meet a set target, they can block a product launch, prevent the purchase of a company, or keep you from getting a raise. When the metrics aren’t right, things stop in their tracks. Sometimes, however, we give our metrics too much power.
The primary function of metrics is to improve the effectiveness of resources. Whether it’s people or machines, the first question metrics are supposed to answer is, are we doing the right work? Said another way, are we working on the right projects? In general, if the metrics meet the targets, the answer is yes. But what if you’ve chosen the wrong metrics? How do you choose the right metrics to help you decide if you’re working on the right projects?
After it’s decided that you’re working on the right projects, the next question is, can we use our resources more effectively? A common pitfall is to confuse efficiency for effectiveness. Most metrics measure efficiency, so be careful. If the metrics don’t meet the target, the answer is yes. A shortfall is usually powerful enough for the company to allocate precious resources, launch an improvement plan and deploy countermeasures.
But what if the target was set inappropriately? Doesn’t that mean resources were allocated inappropriately? If you asked Deming, he’d argue that, since all numerical targets are arbitrary, you allocated resources in an arbitrary way. A shortfall in a metric is not justification to allocate resources. That’s giving too much power to the metric.
Metrics are used appropriately when they aid decision making. But with the best intentions, sometimes standard-work thinking is misapplied to decision making, and human judgment is displaced by automatic triggers when a metric crosses a threshold. The intention with metrics is to improve judgement, not displace it. Metrics don’t make decisions, people do.
Good decision support systems make for good decisions. The three pillars of a good decision support system are data (metrics), people and discussion. No set of metrics is complete, so people are needed to fill in the gaps and reconcile the contradictions. No one person can see the complete picture, so discussion among team members is needed to see things from multiple perspectives.
The good teams know that the most valuable discussions start with multiple perspectives. If everyone agrees right from the start, that’s not a discussion, that’s group think, and it’s analogous to an automatic decision made when a metric trips a threshold. Discussions should be part analysis, part disagreement and part open-mindedness, all wrapped in a warm blanket of mutual respect.
Judgment and disagreement are foundations of good decision making, and good decision making is the foundation of good business. But today there’s a strong negative bias against both. As a result, people are afraid to use their judgment and disagree.
In this time of great change, rearward-looking metrics tell an old story that no longer applies and forward-looking metrics are nothing more than speculation. All you really have is your people and their judgment. I urge you to make it safe for them to use it.