One of the predominant paradigms of managing shop floors is maximising work centre efficiencies. Many managers believe that it is the best way to reduce product costs and get the best out of the plant. The paradigm’s origin can be traced to the allocation rules of cost accounting. The rule of allocating overheads of a cost centre to products using machine or labour hours tends to create a scenario in which increased output results in overheads distributed over more output units and, consequently, a lower cost per unit. Hence, the need to maximise work centre efficiencies, particularly for high investment resources, is almost ingrained in every plant manager and guides their daily decisions.  <h2>This article tries to raise and answer the following questions:</h2><ol style="list-style-type:decimal;"><li>Is maximising local work centre efficiencies good for the plant as a whole?</li><li>Does the paradigm of maximising local efficiencies, at least, help the specific work centre?</li><li>If we establish that maximising work centre efficiencies is not in anyone's interest, should it be abolished? </li></ol> To understand more, let us analyse the environment of custom manufacturing plants. Typically, most custom manufacturing plants have two types of resources or operations - some operations (let us call them Type A) where people have a choice to not focus on orders, like machine shops producing parts for servicing many orders and some work centres (call them Type B) where there is no choice but to focus on order completions. Typically, they are assembly operations, site installation, packing or dispatch. In most plants, Type B resources are usually used in the last stage of operations, and Type A resources are used in the earlier stage. In such environments, the need to focus on utilising work centres forces managers to batch components across orders, and when many Type A work centres do so, the level of desynchronisation increases as the order progresses through the shop. However, when the order approaches Type B resources such as assembly, dispatch, or site installation, the desynchronisation is no longer tolerated. Type B resources trigger expediting signals (or flowbacks) to complete the remaining parts of an order. These flowbacks usually come in as urgent orders, and as a result, all centres that supply Type A work go to the other extreme - they leave everything and attend to supplying the remaining parts of a specific order - they break their queues and take unplanned extra set-ups to service a single order. So, from batching across many orders to minimising the set-up, they go to the other extreme of taking the set-up to service the remaining parts of one order. Typically, this happens close to month-end for plants that have to dispatch fully assembled units when the output of Type A resources or the feeding lines to assembly drops to complete orders. This correction period is payment for the sin of excess batching done in the initial part of the month. For organisations that supply independent parts to site installations, the capacity-killing flow-back requirement occurs after a few months, depending on the site installation lead time. It is not surprising that such companies have outputs that fluctuate widely month to month. If, over some months, the output goes up through cherry-picking for dispatches, then after some time the output drops as the plant tries to complete the mess of many open sites.  This painful experience is so ingrained in the minds of the plant people that they tend to believe that focusing on order completion causes plant output to decline. But what we just analysed is that the pressure to focus on one order (and the corresponding loss of capacity) is triggered by delays. The delays are primarily due to increased desynchronisation. The increased desynchronisation is due to actions aimed at maximising local efficiency. So, by trying to maximise efficiencies, we end up eventually reducing it!  We know for sure that trying to expedite one order wastes capacity, but at the same time, cherry-picking across orders to fully utilise the non-bottlenecks is the other extreme. If we operate at either extreme, we will soon be forced to go to the other extreme. So, the solution is very simple – do not operate at extremes, and you will be better off in terms of capacity utilisation and order completion. So, we can operate in the middle of these two extremes, which should be the stable zone. What is the middle zone?  Desynchronisation occurred because of ‘cherry-picking’ components across orders. Cherry-picking happened because there were too many orders visible to begin with. By reducing order visibility, we can find a balanced middle zone. If we cut the current buffer size (production lead time) in half and prevent material from being released before the revised material release date, the number of orders on the shop floor (WIP) will also be cut in half. With fewer orders available, cherry-picking opportunities are reduced. However, it is not reduced to a single order - there are still enough orders to allow batching, so the plant doesn’t have to process them one by one. This, combined with the elapsed-time-based colour-priority system, enables work centres to operate within a controlled zone while still focusing on efficiently moving multiple orders through their departments. The order completion rate from the work centre increases dramatically, and as a result, the flow-back from downstream operations (e.g., assembly/packing, installation site) diminishes because the entire order (in full kit) is being delivered. This drastically reduces the number of set-ups due to flowbacks. Even though some set-ups may increase to a lower batch size (after cutting the current buffer size in half), the overall impact on the number of set-ups is much lower due to reduced flowbacks. So, if one looks at the number of set-ups completed in a given period (say, one month) before and after implementing the new rules, it will not be surprising to see that the total number of set-ups goes down. Therefore, the output will go up. Our experience across a wide variety of environments has shown that the output of the plant, as well as every work centre, always increases. In fact, the local efficiencies of all work centres also improve after the transition phase. In one of our implementations, there was a fear that the cost at one of the non-bottleneck work centres (the paint shop) would increase due to more set-ups (each set-up in the paint shop results in some waste of paint). However, after the solution was implemented, it was found that the cost went down. It was also found that the number of expediting requests from downstream work centres (i.e., assembly & installation sites) decreased drastically. So, the number of set-ups the paint shop took after implementation was far lower than before. This brought down the cost at the paint shop. In fact, all other local measures of the paint shop, such as carrier efficiency, actually improved. Moreover, on-time delivery at this plant increased from 30%-40% to 98%, coupled with a 25% increase in output. We have well established the fact that focusing on maximising local work centre efficiencies leads to too much WIP, incorrect prioritisation, reduced capacity utilisation, increased lead time and poor due-date performance. Does this mean the non-bottlenecks should be ignored? If you do not need to focus, you can ignore it as well! Do we have to fight the battle of removing the local efficiency measures? Let us try to understand the message ‘Do not focus’. It does not mean ‘ignore it or neglect it’. It is the availability of protective capacity in the non-bottlenecks that allows us the luxury of focusing on the bottleneck machine. What if the protective capacity drops? Don’t we want to know whether the protective capacity of another machine is eroding? Many times, these local efficiency measures provide us with feedback. So why discard it?  <h2>But we learnt that bad measures lead to bad behaviours!</h2>So, we have a conflict: On the one hand, local measures provide crucial information on protective capacity, but on the other hand, local efficiencies drive harmful behaviours.  To understand the conflict, we need to understand what we are calling measures. Are these the numbers that are incorporated into the formal end-of-period MIS reports, or are these questions that top management frequently asks in meetings? In most cases, what is captured in the MIS matters little unless it is backed by top management questioning. So, measures that impact behaviour are only those reflected in day-to-day top management questioning. The rest are just numbers in some papers.  So, if we teach top management to stop asking local efficiency questions, would we have removed the practice of local efficiency decisions? Is this the foolproof method to prevent damage to local efficiency decisions?  Well, measures or no, we, as human beings, are hard-wired to work efficiently. Give an exercise of creating 50 envelopes for any person without any performance measures. The immediate tendency is to cut all 50 envelopes, fold all 50 into the shape, and glue all 50. Setting up a reduction to minimise efforts comes naturally to us. This is almost the same as in a shop. Give any worker three orders: two similar and one dissimilar, each involving a set-up. The immediate instinct would be to batch the similar orders with or without a ‘measurement system’. So, it is not the measurement system which is causing the problem; it is our inherent instincts. Removing the old measurement system and replacing it with the new one will not benefit anyone. The best approach is just to choke the release of material - there will not be enough material to batch in the first place. For envelopes, assign first 25 to the person, let him batch them, then give the next 25. The person does not have 50 envelopes to batch in the first place - he had only 25 with him, so the first 25 are coming out much earlier.  The antidote to local efficiency measures is to choke the material release in the shop and to follow the colour priorities. These steps, when implemented, give significant results and remove the negatives of the local efficiency measures. So, if we have an enabling process that counters the negative impact of local measures and ensures that top management does not ask the wrong questions, we could live with formal measures and observe them from time to time to validate the availability of protective capacity and buffer signals.

Key Takeaways

Maximising work centre efficiencies has long been the bedrock of manufacturing management. Every plant manager strives to achieve them. Yet, hidden beneath this pursuit lies a paradox that has left many plant managers ensnared in a vicious cycle, undermining the very efficiencies they seek to enhance.

How bad are local efficiency measures?

<h4>How bad are local efficiency measures?</h4>

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Local plant measures create desynchronisation and month-end chaos. Discover how controlling WIP stabilises shop-floor operations and lowers total costs.