Find the hidden capacity in manufacturing plants
Manufacturing companies tend to be unaware of the 'Real Capacity' of their plants. In this podcast, Achal Saran Pande, Senior Partner at Vector Consulting Group, discusses common misconceptions in calculating the 'Real Capacity' of a plant and sheds light on how to unearth hidden capacity in almost all plants.
Welcome to Counterpoint Podcast.
Many of our implementations in Operations release capacity in the plant surprise the client with anywhere between 20 to 50% more Output while improving delivery. This raises a fundamental question – why are our clients surprised? Why is this inherent capacity not intuitively understood? What makes capacity opaque and hidden in many organizations? To talk about it, we have Achal Pande, Senior Partner with Vector Consulting Group.
The world has become highly competitive, so everyone wants to achieve more with whatever they have to win. Increasing efficiency utilization of available resources and improving productivity are critical for organizations to survive. Asset Sweating, exploring innovative ways to exploit the available resource capacity, is the new norm for survival and growth.
But do you know companies, in most cases, do not even know they have ~20-25% hidden capacity, which they are oblivious to?
And we can only improve productivity if we can,
- Establish the real capacity of the manufacturing system and
- Exposing ‘Hidden Capacity’ and extracting impairing the manufacturing system visible to us may not be the ‘Real Capacity’ of the system.
Therefore we need to ask two key questions which are essential for capacity management
- What is the ‘Real Capacity’ of the manufacturing System/Set-up?
- Is there a ‘Hidden Capacity’ in the system, and where is it hidden?
These two broad challenges have to be addressed to arrive at an effective way of managing the capacity.
How do we do this? What is this ‘Real Capacity’? And how do we unleash it? All these and more in today’s discussion with Achal Saran Pande, a Senior Partner at Vector Consulting Group.
| Shubham Agarwal : | So, let us seek the answer to the first question. What is the problem with how capacity is defined/monitored/assessed in a typical manufacturing system, and What is ‘Real Capacity’? |
| Achal Pande : | Capacity is defined as the ‘Maximum Acceptable Output’ of a manufacturing system in a certain defined time horizon. This is the standard way of defining capacity. The ‘Acceptable Output’ is commonly defined as units produced, denoted as Units/hr, pieces /hr, etc.
The world is highly competitive, and the demand for customized products is increasing each day. This is leading to product proliferation and a significant increase in the variety of products being manufactured. When operating with a large variety of products, the wide variation in Output provides a ‘Contrarian View’ of available capacity. For instance, If we take a lean product mix (a mix consisting of products with shorter cycle times), the Output increases significantly, giving an impression that we have huge available capacity. In contrast, in a not-so-lean mix (mix skewed towards products with larger cycle times), we get the impression of a lower available capacity. Many organizations and equipment suppliers define capacity in terms of the median product mix to establish a “Reference Capacity” that would serve as a benchmark for the manufacturing system. But such a benchmark still needs to establish the real capacity in a varied product mix environment. The ‘Mix of Operations’ further aggravates this. Operational Mix is defined in terms of various operations in a manufacturing system. Let’s take an example. Typical Sheet Metal Operation has Press Shop, Weld Shop, Finishing Operations and Assembly operations. It is an amalgamation of various operations, and each operation is characteristically different from the others. The capacity of the press shop is defined in terms of the number of stock per/hr; the weld shop capacity is defined as the number of welds per/hr; the finishing operation’s capacity is defined as finished pieces/per hr, and assembly capacity is defined as assemblies/hr. Therefore, in such a mixed environment, defining the capacity at an overall system/plant level is challenging. In such a scenario, the maximum ‘Experienced output (credit to the plant warehouse)’ or ‘Experienced dispatches to the customer’ often becomes the ‘Reference Capacity’/’Benchmark’ for operations. Due to the ‘Mix of Products’ and ‘Mix of Operations’, the real capacity of the manufacturing system is not ‘Exposed’. In the absence of visibility of ‘the real system capacity’, the only way to ensure that the overall manufacturing facility maximizes Output is to maximize Output (therefore utilization) of individual work centres/work set-ups/job shops constituting the manufacturing set-up. |
| Shubham Agarwal : | But maximization of Output at individual work centres could cause new concerns, like every work centre would try to produce whatever they can produce the maximum, regardless of the priority. This could convert into a high inventory accumulation, impact OTIF poorly, and there will be chaos across the shop floor. Isn’t it? |
| Achal Pande : | This is a vicious loop – Real System Capacity is not visible – maximize Output at work centres to maximize system output – Delays and Poor OTIF-Impression of lack of capacity- Invest in more capacity.
The key thing to understand is that the ‘Real capacity is invariant’. Therefore ‘Mix of Products’ and ‘Mix of Operations’ should not have any impact on the same. The idea is to establish the Real capacity and monitor the throughput against the same. |
| Shubham Agarwal : | Let’s assume that we have established the ‘Real Capacity’ of the manufacturing system. How do we extract the additional throughput from the system? |
| Achal Pande : | This will be interesting. The ‘Real Capacity’ established is with the assumption of product cycle time (for different products) at work centres. There are two scenarios/cases often observed 1. Case 1: The operating cycle time is more than the established cycle time of the product (during NPD and commercial production). Past operational experiences (increase in Output deteriorated a certain operational parameter and, therefore, an optimal point of operation is arrived at). 2. Case 2: The potential to reduce cycle time is much more than the established cycle time of the product (during NPD). At times all possibilities, permutations and combinations of parameters are not evaluated fully to arrive at the right combination of operational parameters.In both cases, the potential to operate at lower cycle times are available, but due to certain reason, the full potential is not realized. This happens when a specific operational parameter is in direct conflict with the Output of the system. When the Output is increased, the other parameter deteriorates and vice versa. This we term ‘Capacity Conflict.’ For instance – Quality vs Output, Machine Performance (Breakdowns etc.) vs Output (elaborate with example), Organizational/labour Policy vs Output etc. Let us try to elaborate with an example. Case 1: Aged/old machinery is associated with wear and tear over a period of time. Wear and Tear of parts may cause repetitive breakdowns such as misalignment, part/component damages/breakages, hydraulic failures etc. (elaborate). At higher operating speeds and feed rates, such breakdowns may increase, bringing down productivity significantly. Therefore, a ‘Sweet Spot’ is arrived at, a compromise between operating machine speed and breakdowns. Such a compromise increases the operating cycle time and reduces the effective capacity of the work centre. Such operational conflicts reduce the capacity of the system, and the effective capacity is much lower than the real capacity of the system. Many times these conflicts are accepted as an operational norm and never questioned. Therefore, many such conflicts remain hidden and unresolved until they are questioned. Each conflict hides a certain capacity; therefore, the number of hidden conflicts in a manufacturing system is ‘indicative of the hidden capacity’. The key is identifying and resolving such conflicts to obtain substantial gains in Output. Hidden capacities have been observed to be anywhere from 30% to 60% within the manufacturing system. |
| Shubham Agarwal : | That is interesting to see how capacity conflicts hide capacities. Is there hidden capacity somewhere else in the system as well? |
| Achal Pande : | Yes. There is one more area where we have hidden capacities. We can term that area as ‘Capacity De-Rating’. |
| Shubham Agarwal : | What is capacity De-Rating? |
| Achal Pande : | In a typical manufacturing environment, capacity is de-rated to accommodate certain categories of losses. These could be Set-up Losses, Breakdown Losses, Admin Losses and many other category heads. For instance, if installed capacity is 100, then effective working capacity could be 75%, assuming that certain losses will always happen and cannot be reduced. Therefore 70% is taken as the operating norm instead of 100 for the manufacturing system. The losses are accepted as a norm and are rarely questioned/investigated. Such losses can de-rate a capacity by 20%-25%. This is huge. The significant potential is available to reduce such losses by deploying efficient working methods. |
| Shubham Agarwal : | Once we have established the ‘Real Capacity’ and exposed the ‘Hidden Capacity’ of the system, the two challenges that we opened the discussion with, is there any other challenge we need to be cautious of? |
| Achal Pande : | Great discussion. We shall come back in the next episode to discuss in detail the process and tactics for calculating the ‘Real Capacity’ in the system unleashing the system’s ‘Hidden Capacity’. Thank you, Achal.Great. • Not knowing the bottleneck due to more than critical inventory • Trying to define capacity by average Output • Or de-rating capacity • Accepting work expansions as a new norm And the way out is to use the available theoretical time to define capacity and find wastages of work expansion and interruptions against that. |
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