Isn’t it just fantastic what we people have managed to do in the field of measurement and control technology. The nicest gadgets ensure our production equipment and processes remain in control. Nothing can halt technology, so it seems. And who knows what the Internet of Things (IoT) will have in store for all of us in the future. A future that most probably is nearer than we think.
But let’s stay somewhat closer to home for now. What about all these business processes that aren’t fully mechanized or automated? How do we keep those in control? What can we learn from trying to apply the concepts of measurement and control technology to these type of processes?
After all, a value stream can very easily be seen as a dynamic, controllable system with certain output variables (performance criteria) and associated objectives. And there also are sufficient input variables to be controlled in order to achieve the objectives set for the value stream. These input variables are typically grouped under the so called 4M’s of man, machine, material and method (sometimes extended to the 6M’s, including measurement and mother nature).
Feedback as Core Concept of Control
Feedback probably is the core concept of measurement and control. It means we measure the actual results of the process, compare these to the set objectives and provide feedback to the controllable system. And here the practices I regularly encounter already start to derail. For starters, we do not even always measure our process outcomes or the ones that are relevant to what we are trying to achieve. And if we do, reference values to compare our outcomes with are often lacking. Partially this is because we do not always translate our organizational objectives all the way down to our operational teams and team members. And if we provide feedback on the outcomes based upon objectives, we also do not always provide this feedback to the system itself, but often to some kind of managerial layer higher up the hierarchy or a functional support department. Strange, don’t you think?
Moreover, when feedback is provided, it often strikes me that the feedback frequency is very low compared to the cycle time of the process. This means feedback is late and this typically leads to significant losses as problems can continue to persist before action is being taken.
In order to organize an effective measurement and control cycle, we need to provide instant feedback to the operational team members actually working on the gemba, where value is added. And in order to allow these team members to react immediately, we need to provide them with standard reactions – predefined actions in case of deviations from the standard. These standard reactions allow the team to be autonomous in reacting both quickly and effectively in case of problems.
Another negative effect of the delay in feedback is the so called “learning loss”: the decreasing ability to learn how problems actually come about and therefore how the system that the organization is trying to control actually functions under certain circumstances. And in order to enable an operational team to become autonomous and to provide standard reactions, this is exactly the kind of knowledge organizations need to develop in order to setup an effective, operational measurement and control cycle.
Measurement and control in a strict sense concerns the measurement, monitoring and subsequently correcting or repairing of the system. But in order to know how we can correct and repair the system we of course first need to have proven knowledge about the functioning of the system. Based upon detected deviations from standard, followed by thorough and structured problem solving, organizations need to develop their knowledge of the relationships between the 6M’s and the process outcomes and performance criteria. Because otherwise, “measurement and control” will quickly become like ineffective “shooting from the hip”. Measurement and control in a broader sense therefore implies that we need to become a learning organization.
When we increase our knowledge of our system – through this approach of intentional learning – we can also start thinking of a shift from feedback to feed forward. Our attention thereby shifts from output to input variables, from product to process quality and from corrective to preventive and planned. This is where standard work, visual management on the 6M’s, error prevention (poka yoke) but also autonomous/instant as well as preventive maintenance play an important role. But in order to get there, organizations should first start learning intentionally! And that implies openness about problems, short-cyclic feedback at each level, high reactivity, time for self-reflection and criticism, structured and disciplined problem solving and continual adjustment of our standards based upon the latest insights.
Well, wouldn’t that be nice… But back to the harsh reality. We are reporting tons of data, our two-pipe pellet shotgun is doing overtime, but whether we are actually hitting something I’m not sure. What are your thoughts? Personally I think we still have a lot of potential applying the lessons and concepts from measurement and control to our business processes. Until then, I wish you “happy hunting”!