Actual capacity of the supply chain, or that of a manufacturing or service depart- ment, is the greatest throughput rate that can be achieved with the existing con- figuration of resources and the accepted product or service mix plans. Altering the product or service mix can (and usually will) change actual realizable capacity to produce output. Modifying the existing configuration of resources, equipment, and people in the supply chain workforce alters real capacity. The systems point of view includes cash as part of the resources because cash can be converted into new machines, which alter real deliverable throughput capacity. The systems view- point includes good ideas, which can increase supply chain capacity with minimum expenditures.
The formula for actual measured capacity of a manufacturing or service depart- ment is
C = T × E × U, where
C = actual measured capacity (in units converted to standard hours) T = real time available
E = efficiency U = utilization
T is determined by calculating the amount of time that is available when fully utilizing the resources that are already in place to make and deliver prod- uct throughput. Doubling the number of machines, trucks, etc., doubles T (the amount of available time). E is the efficiency with which time T can be utilized to make and deliver different kinds of products. Then, T × E is equivalent to standard hours available to make and deliver the products. U is how much of the available throughput capacity can be (or is) utilized. Lack of orders or breakdowns of pro- ductive systems diminish U. When T and E and U are multiplied, the product is C, the actual capacity that is being (or has been) utilized. Table 4.1 illustrates the calculations.
Assume that the rated maximum throughput capacity of the plant for the data given in Table 4.1 is 150 standard hours. It never achieves the maximum, but comes closest to doing so on Wednesday. As briefly explained before, there are a variety of reasons why the system does not achieve maximum throughput. Systems with bottlenecks and flow disruptions can never achieve maximum product throughput.
E, the efficiency, is a proportional factor used to convert units of throughput to standard times. Systems of machines and people that work slower have lower effi- ciency than those that have a higher productive output. Often, the best in the class is given an efficiency of 1. It is to be expected that variations in efficiency will occur.
Sometimes the source of the variation can be traced. If it is significant variation, then it should be corrected. Unexplained fluctuations in capacity are unpleasant and unprofitable.
If a supply chain is operating at 90% of the standard time because a supplier (somewhere along the supply chain line) has delivered a defective product, remedial action must be taken with supplies on hand and the problem must be corrected with future deliveries. Marketing may have assured deliveries, so customer noti- fication and promises of remedial action reflect the urgent nature of the systems problem so often involved in capacity management.
Table 4.1 Capacity Utilized (C)
Product Monday Tuesday Wednesday Thursday Friday
T101 25 20 65 18 30
T102 65 10 25 40 0
MW11 40 90 50 70 80
Total 130 120 140 128 110
Note: The numbers are product throughput.
U, the utilization, is applied as a proportional correction to standard time when there are supply chain disruptions. Even when everything is running “as planned”, the value of U is often less than 100%. If the system is operating even faster, the value of U can exceed 100%. There are pros and cons related to running opera- tional systems above the maximum rated capacity. How long maximum capacity is exceeded also counts.
U is a measure to be wary of when it becomes an objective of management to keep U as close to 100% as possible. There are sound economic reasons not to operate a production department above capacity. For example, it is economically sensible to stop production when the planned output quota has been met and safety stock is sufficient. Shutting down the production system for 2 h of an 8-h day means that the U measure goes to 75%. Actual measured operational capac- ity is going to be reduced by a fourth. For various reasons, that may be a good thing.
Management must establish the fact that supply chains are composed of com- plex sub-systems some of which cannot function above capacity for very long. The costs of unsold throughput must be analyzed. How long will stored output remain as inventory? Are there fluctuations in demand that they will buffer or is the buffer in place already? The costs of arbitrary utilization of supply chain capacity to elimi- nate less than 100% utilization should be recognized for what it is—waste of time and money due to fear of seeming to waste (unutilized) capacity. An appropriate decision model can be constructed to set proper supply chain throughput based on maximizing the whole systems performance.
As an example, assume that U is 0.963. This is likely to be viewed as a more rea- sonable utilization factor than 0.750. P/OM, in general, will not tolerate a perma- nent situation where utilization factors are below 0.900. Still, the target numbers will be dependent on the situation. For example, in service organizations, a high value of U might be required to keep queues short. Cyclical supply chain demand systems can be expected to cycle between utilization factors in the 70% range and then up to more than 100% capacity. Cyclical industry companies prefer having excess capacity in reserve and expect to operate effectively below the misleading ideal of 100% utilization.
Using data in Table 4.1, the value of 130 actual standard hours of throughput on Monday might have been obtained as follows:
C = T × E × U = 150 × 0.9 × 0.963 = 130 actual standard hours.
The ratio of actual standard hours to maximum standard hours is equal to 130/150 = 0.87 or 87%.
Suppose in a bank, there are six tellers’ windows. If the bank is open for 8 hours (h) per day, the maximum designed capacity is 48 h. This is a long-term decision because the number of windows cannot be changed every day. If there are only four tellers on a given day then the operating capacity is 32 h. The operating
capacity can be changed on a daily basis (even every hour) by changing the number of tellers. The operating capacity cannot be more than the designed capacity. The actual utilization of the four tellers may only be (say) 27 h—based on the number of customers that visit the bank.
The same situation applies to grocery stores with one cash register and super- markets with dozens of checkout counters. The number of operating rooms in a hospital is the maximum design capacity. The number of seats in a restaurant is the maximum design capacity but the service rate and the length of time that custom- ers stay in their seats determines throughput capacity.
In that regard, maximum operational capacity can also be defined as maxi- mum sustained throughput of goods or services. Operations capacity describes how many units can be supplied per unit of time. For services, a bank might compare the maximum number of people the bank teller can process per hour with the maximum number of people the ATM can process per hour. This is a supply chain service capacity comparison.
For a manufacturing supply chain example, compare the maximum number of hot dogs Oscar Meyer (OM) can make and ship per hour with the maximum num- ber of hot dogs Hebrew National (HN) can make and ship per hour. The maxi- mum number is likely to be much bigger than the breakeven number (see Appendix A). In this supply chain the ingredients required to make the products have a flow through rate that must match the producers’ rates. It is not possible to determine if OM is better off than HN without the entire picture of the system.
Still, this comparison is one that both companies would like to make to com- pare their PMCs (productivities at maximum capacity). PMC makes an excellent benchmarking measure. In supply chain terms, benchmarking is a systematic com- parison of fundamental measures with those of contestants performing similar sup- ply chain functions.
Many other aspects of capacity planning are discussed in Chapter 9, Supply Chain Management. In the following sections, we will discuss capacity planning for an operational department what is popularly known as aggregate production planning.