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Using Process-Level Management Systems to Drive Resource Efficiency

Technology is an important tool for maximising the resource efficiency of a manufacturing plant, but even the most efficient technologies require maintenance and management. The optimisation of resource efficiency, like any continuous improvement initiative, requires feedback.

Process-level management systems are the key to continuous improvement. There are two major components to a good process-level management system. The first is that it should provide information on the quantum of resource usage at short-intervals. For example, if one is trying to minimise raw material usage but is relying on a monthly report from the Finance Department, the chances are that this information, while useful, is not suitable for process optimisation, since it is just too infrequent. What is needed is information close to the source of the waste, and accessible to the plant operator. Rather than being independent of accounting information, it is best linked to the accounting information in a formal manner. The best way to do this is to make this process-level information the source of the accounting information. The resolution of this data should be as fine as possible. Hence if you are in a batch manufacturing environment, the raw material usage should be known for each individual batch, and also compared to a standard for the product being produced. The cost of the waste in financial terms should also be made available at this level, so that plant operators understand the full financial impact of the waste. This drives urgency in its resolution.

The second major characteristic of a good process-level management system is that it should provide good information on the drivers of resource efficiency. This is a more challenging matter, and requires sound process knowledge to set up. It is however where the real value of process-level management systems lies. For it is only in measuring and controlling the drivers that the outcome can be reliably achieved. Ensuring that the drivers stay in control ensures that the process operates at its efficient best. So what exactly are these drivers that I am on about? These are the inputs to the process that impact on its resource efficiency performance.

As a simple example, say I am operating a process that produces sugar solutions and I am trying to manage the usage of sugar. In terms of overall material management, I would want to know, on a batch basis, the quantity of water used, and the quantity of sugar added (as measured by a scale), and these quantities would be compared to standard quantities as per a “recipe” for each batch size produced. As a check, I would reconcile the quantity of sugar issued to the process (e.g. the number of bags x the mass of each bag) to the quantity weighed and added to the process – variances here would provide me with insights as to the accuracy of the scale used. Of course, I could also have a work practice in place whereby the scale is checked each shift with a standard weight. I now have the information needed to measure how much sugar is being used at short intervals and whether I am meeting my usage targets. Next I need to understand what drives the usage of sugar. Clearly, any sugar spills would translate to a loss of sugar, since it would be unhygienic to use soiled sugar in a food product. Plant operators could be instructed to sweep up and weigh all sugar spills once per shift.In addition, it is important that the concentration of sugar achieved is not excessive, since this would translate into an economic loss. Hence the concentration of sugar in each batch, together with a comparison to the target concentration, would be an important aspect of the process-level management system. This could be used to calculate the amount of sugar actually added, and compared to what was measured on the scale and issued from storage. The dissolution of sugar is an important issue, since it affects the concentration data and un-dissolved sugar could lead to misleading results in terms of sugar usage. Hence I could include water temperature and mixing time as information that is recorded and monitored. Finally, the volume of sugar solution transferred out of the batch tank is also important, since some losses are expected between the addition of water and sugar and the final volume of solution delivered from the batching tank. For example, a small quantity of solution will adhere to the walls of the tank, and a significant volume may end up in “dead legs” in the piping – we will not be able to recover every drop. We could calculate the magnitude of this loss for each batch, since we know the inputs (volume of water and mass of sugar added) and would measure the outputs (volume of solution and concentration of solution). In this way we would now have a system which tracks and reconciles sugar usage, and also points us in the direction of root causes when this usage deviates from specified standards.

Every system is different, and most industrial processes are more complex than in this simple example. Regardless of complexity, measuring performance close to the source of waste and monitoring and controlling the drivers of that performance are the best ways to manage resource efficiency on a routine basis. Both of these approaches can easily be built into process-level management systems.

Tip: a great way to identify resource efficiency drivers is to use a formal root cause analysis tool such as why-why networks.

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