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.

TAKING A RISK-BASED APPROACH TO SUSTAINABILITY AND OPERATIONAL EXCELLENCE IN MANUFACTURING

Manufacturing risks extend beyond
the boundaries of the site

Risk management tools are employed in some shape or form on most manufacturing sites. The two most common areas in which they are used are for safety and plant maintenance purposes, where risk- based approaches are used to identify potential failures and devise mitigation strategies to prevent these failures from occurring. In theory, the development of integrated quality management systems encompassing safety, food safety, product quality, environmental issues and more recently issues such as energy efficiency should involve some kind of risk assessment. In fact, a risk assessment should be the foundation of such systems. 

For reasons I refer to below, many organisations tend to gloss over this important step and their risk registers are not as comprehensive as they should be. They tend to identify risks through some kind of brainstorming process, which is not in itself a bad approach, but can lead to a “high-level” assessment of operational risks if the process is not sufficiently focused. The problem in manufacturing environments is that some very large risks tend to have very small origins – the v-belt that is not tensioned correctly and catches fire, burning down the facility; the valve that passes and contaminates large quantities of food products; the extraction fan that is never switched on and increases long-term occupational health problems for employees through solvent inhalation....I could go on. While management teams can and must identify strategic risks, operational excellence is largely about small details.

One of the major impediments to rigorous risk assessment is that it is resource-intensive. In the maintenance environment, Reliability Centered Maintenance (RCM) is an example of an approach that is shunned due to the amount of resources required to partition a manufacturing plant to the required level of detail, identify failure modes at the component level and then devise maintenance tasks to prevent failures using decision trees. However, without making this investment in time upfront, potentially catastrophic failures can go unidentified, and while the maintenance programme may be improved after such failures occur, the costs incurred in learning lessons in this way can be very high.  

A second challenge for those wishing to identify risks comprehensively is that this requires subject matter expertise. If a group of people spends a considerable amount of time conducting risk assessments but lacks this expertise, the chances are the risk assessment will have serious shortcomings. So resources will ultimately be committed but wasted, leading to disillusionment with the process when unforeseen incidents occur. It is therefore vital that significant investments are made in technical training and coaching, in order to equip employees to participate productively in risk assessment events.

By now you can see where I am going with this. Comprehensive, rigorous risk assessment is an essential element of any strategy aimed at sustainable, stable, continuously improving operational performance. Of course, risk assessment is only the first step, solutions still need to be developed and implemented to mitigate each individual risk in order to realise the benefits. The philosophy is simply that if we can identify and mitigate every risk, we will achieve excellence. This is in essence a lofty goal, since in practical terms, we will never be able to prevent every potential incident. However, if we try our best to eliminate every risk, those that remain will be small in number, and can be handled as they arise in line with PDCA. If we chose to deal with every risk after the fact with no risk identification upfront, we would be fire-fighting, and if your facility tends to operate in an unstable fashion, chances are your risk management practices need review.

The machinery through which risks are mitigated is comprised of the various management systems in place. Quality Management Systems specify overarching policies, how manufacturing process units should be operated, the parameters to be measured, reporting, corrective actions and the like – essentially everything that needs to be in place to ensure that excellent product quality, high levels of safety, responsible environmental performance and other key objectives are realised. These are however not the only systems through which risks may be managed. Preventive maintenance programmes are a vital component of risk management in the manufacturing environment. Human resource risks also require serious consideration, and require standardised and rigorous processes and standards for their management. Cost control procedures are a further example of tools employed to manage risk. Many of these supporting systems reside in IT platforms. The organisations that are best at developing and implementing quality management systems integrate these disparate systems into the overall quality management system through explicit linkages.  In general, the greater the number of unique systems you have to integrate, the more difficult the task, and if you could build an integrated system from the ground up you would have the ideal management system.

The complexity of quality management systems and the other programmes manufacturers may be implementing at any point in time (such as continuous improvement programmes for example) can lead to bureaucracy and confusion. In many cases a fixation with the system rather than its efficacy means that results are erratic and do not exhibit sustainable improvement. I am not knocking quality management systems or continuous improvement programmes, both of which are important vehicles for the achievement of operational excellence and sustainability. I do however believe that there is a need for manufacturers to “get back to the basics” insofar as obtaining and harnessing a fundamental knowledge of their operations is concerned. Risk assessment provides an ideal vehicle for doing so. It does however mean examining physical and business processes in minute detail, and yes, this is time consuming and requires a lot of skill and knowledge to do effectively. Once this has been done, the mitigation measures developed, if implemented rigorously, will however go most of the way towards solid, repeatable operational performance. Quality management systems provide the ideal vehicle for execution of these mitigation measures. Continuous improvement programmes require this sound foundation to be effective. Risk assessment therefore lies at the heart of the well-oiled manufacturing machine, particularly if the same approach used to identify risks is also used to unearth opportunities.  In a future post I will give you an example of a detailed, integrated, process-level risk assessment that will illustrate how powerful this approach can be as a platform for operational excellence and sustainability.

Copyright © Craig van Wyk, 2013. All rights reserved