LEVERAGING SHORT-INTERVAL CONTROLS TO DRIVE OPERATIONAL EXCELLENCE

Operations-orientated environments such as manufacturing are mastered by those who pay strict attention to detail. The organisations that do this tend to be the most successful, but the real champions in manufacturing are those who understand which details are most important. These organisations know immediately when an out-of-control situation (such as a quality or cost problem) arises, and also immediately understand why, and what to do about it. They then react quickly, rapidly restoring desired performance levels and maintaining high levels of delivery against a multitude of performance indicators. How is this possible? The answer lies in the short-interval control systems implemented on the shop floor.

 

As the name implies, "short-interval controls" refers to systems that deal with operational matters over short time-frames. Hence while senior management may be interested in tracking process yields for raw materials at monthly intervals or longer, the frequency for assessing these yields should be far higher on the shop floor. In a batch manufacturing environment, process yields may be calculated for every batch, for example. The principle is essentially that the monthly process yields are simply a result of the process yields for individual batches, and that by maximising the yields for each batch, monthly process yields will be maximised.  This approach can be followed for all indicators of operational performance, including product quality, throughput, safety, reliability and whatever indicators a business may choose.

 

It is one thing to measure performance at short intervals, but while this is a step in the right direction, and in my experience is itself often lacking, the best-performing organisations take it one step further. Instead of only measuring outcomes at short intervals, these organisations also track the drivers of performance at short intervals. These are tracked in one system so that the relationships between the drivers and the outcomes are readily apparent and can immediately be acted upon. The principle behind this approach is that by keeping the drivers of performance in control, performance will be maintained or where possible, improved. If the drivers have been comprehensively identified, then when performance deteriorates, it should be a simple process to scan the measured drivers, identify the one that is out of control, and then take steps to restore this driver to desired levels. Trends in the driver data can also be used to draw conclusions with regards to trends in outcomes, so that process owners can be proactive about performance management. If the reasons for poor performance do not show up in any of the drivers, this means you need to identify more drivers. You can use root cause analysis techniques to do this (WHY-WHY analysis is my personal favourite) or even do this by trial and error. I can tell you from experience in manufacturing management that this process works, and in my career has been one of my most powerful "secrets" to enhanced performance.

 

To make this less abstract, let me illustrate with a concrete (but hypothetical)  example. Say you are dissolving a solid in a liquid e.g. making a sugar solution in a beverage facility. The process needs to be completed in a given time period in order to meet the throughput requirements of the factory in which it is operated. The outcomes for this process would be the volume produced for each batch, the time taken to prepare each batch and the final concentration or density of the solution being prepared. Each of these would need to be monitored for each batch. The process is carried out in a heated, agitated vessel, into which the liquid is pumped. The solid is manually added after being weighed by the process operator. Typical inputs that would also be monitored using the short-interval control system would be:

• The volume of liquid added (affects turnaround time, throughput and yield)
• The time taken to pump the liquid into the vessel (affects turnaround time)
• The time taken to heat the liquid to dissolution temperature (affects turnaround time and possibly also product quality e.g. burn-on if rate is too high)
• The actual initial and final temperatures of the liquid (affects turnaround time and dissolution rate)
• The mass of solid material added (affects density of the solution and process yield)
• The speed of the agitator - this is equipped with a variable speed drive (affects turnaround time and dissolution rate)
• The time taken to pump the solution into the holding tank (affects turnaround time)
• The mass of solution produced (affects throughput and yield)

By monitoring these variables on a continuous basis, this process can be kept in control and this data can immediately be consulted should there be a problem with throughput, yield or product quality. Without it, management and the plant operator is pretty much in the dark with regards to the reasons for performance problems, and the time to correct these would be much longer, impacting negatively on the performance not just of this process, but of the wider processes in the facility impacted upon by it. If this process is the bottleneck in the facility, for example, turnaround problems would affect the throughput of the entire factory, with potentially disastrous consequences. We see therefore that something as seemingly small as the time taken to pump a liquid into a vessel can impact on the performance of en entire business. This is what I meant when at the start of this post I spoke about "details".

Copyright © 2016 Craig van Wyk, reproduction only with written permission.

Craig is the founder and Managing Consultant at VWG Consulting, a Johannesburg-based productivity and resource efficiency consulting and services company serving the industrial and commercial sectors.