Lovelock (1992) defines the capacity of a service as the highest possible amount of output that may be obtained in a specific period with a predefined level of staff, installations and equipment. “Capacity management is the ability to balance demand from customers and the capability of the service delivery system to satisfy the demand”, (Armisted and Clark, 1993). This requires first to understand the nature of demand by forecasting and second to manage capacity to meet that demand. In simple terms, the aim is to minimise the customer waiting time and to avoid unused capacity but at the same time without affecting the quality of service provided. As said above, the number of service organisations is growing in many countries. This leads to increased competition and the firms are forced to increase their efficiency and productivity that requires adequate management of available capacity.
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There are many writers and literature on how to cope with the demand and supply imbalances (Lee, 1989; Lovelock, 1988; Sasser, 1976; Shemwell and Cronin, 1994). In general, there are three ways to deal with such variances as identified by Sasser (1976), the level capacity, chase demand and manage demands. Slack et al., (2009) calls them as ‘pure’ plans but suggests that in practise most organisations will use a mixture of them rather than sticking with a single plan.
Level capacity plan
The first method is to have a fixed capacity irrespective of the demand. This is a very simple strategy and if the demand is lower than the capacity, the extra idle capacity is wasted. When the demand is more than the capacity, it cannot meet the higher demand. “Level strategies are applicable when demand is more visible before the time of use and the service organizations can effectively tell customers to wait when demand cannot be satisfied, i.e. the service is valued by the customers and they are willing to wait” (Armisted and Clark, 1993).
In goods manufacturing or in logistics when the demand is low, the firms could continue its production to make and keep an inventory level for future demand. This will help to meet the high customer demand when capacity is lower than demand. However, in service delivery there is not the possibility of producing the complete service package in advance of demand and holding it as an inventory since the services are perishable by nature (Armistead and Clark, 1993). In addition, services tend to keep additional capacity in anticipation of additional business. Most service sectors have strict capacity constraints. “Once the capacities are established, the cost of making any adjustment-renting new gates and airplanes or building new hotels -are quite high” (Kim et al., 2004). Moreover, if the customer demand is not met, then there is a high risk of losing customer base. So to serve as many customers as possible and to have competitive advantage companies prefer to keep the capacity at the maximum anticipated level (Irene et al., 1998). This also helps them to avoid implementation of the complicated capacity management techniques.
The second strategy is to manage supply to demand. Irene et al. (1998) says that
Problems in Capacity management of Services Industries
However, Adenso-Diaz et al. (2002) says that capacity management in service sector presents additional problems to those of manufacturing industries. He says that on the one hand the service firms are faced with a strong seasonality in demand. Kim et al., (2004) also notes that many service industries face considerable demand uncertainty and seasonal variations. “For instance, market demands typically are much higher during summer holidays and Christmas than during the rest of year” (Kim et al, 2004). On the other, the need for the customer to be actually present when the service is given is fundamental to many sectors. This personalised demand directly affects the quality of service offered.
In addition, services are perishable by nature and hence for each day those services are not put to profitable use, they cannot be saved (Bateson, 1977; Thomas, 1978). For example if an airline passenger seat is vacant on a particular journey, the airline company cannot use that on the next journey as an additional seat. This is applicable to most services. In addition, most services have strict capacity constraints. For example, an airline company or a hotel when reached its capacity it is very costly to make adjustments. To add another hotel or a flight requires huge investment.
Capacity management – minimum staff – model
As seen before, the capacity management is a very complex and difficult task in services. The failure to synchronize supply and demand, leads to a loss in opportunity to attend certain customers when demand is higher and to high costs due to the loss in income when demand is insufficient (Sasser, 1976). Another of the barriers in services is the problem of seasonal demand. Adenso-Diaz et al. (2002) says that one of the important aspect is the human resource planning. This deals with assigning the right number of people at the right place and time. They propose a model to determine the staff numbers to provide minimum coverage.
Duder and Rosenwein (2001) show that by using simple formulas to rearrange the number of staff in a ‘call centre’, it is possible to reduce the percentage of abandoned calls and increase the number of calls answered without waiting.
Coping capacity management
As we saw before, there is a relation between capacity management, quality management and efficiency management. A number of authors have identified problems confronting when managing supply and demand, which affect the quality of services (Lovelock, 1984; Rhyme, 1988). In services as capacity is managed more efficiently, there is a possibility that the quality of services might get affected. Armistead and Clark (1993), says that it is inevitable that at times service organisations run out of capacity to meet demand. They call it the coping zone. In coping zone, there is more demand than can be managed with the available capacity. This inability of managing the demand leads to a fall in the quality of services offered to the customers.
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The queuing theory by Maister (1985) recognises that to maintain a consistency in waiting time and queue length, the average utilization of resources may be relatively low. Another study (Heskett, 1986) done in a restaurant showed that the perception of service quality was increasing up to a maximum utilization of 75% and fell away from then. This fall in the quality was partly due to a move into the coping zone. This shows that the operations managers should be aware of the level of utilization above or below the service quality will be affected unless some actions are taken.
In a coping zone, the operational manager could either allow the service quality standards to fall in an uncontrollable way or try to control the service standards and with that protect the service standards for the core service. Also another scenario is when the capacity is in excess of demand and this underutilization leads to a fall in service quality.
An action strategy
Armistead and Clark (1993), proposes a model to deal with the fall in the quality in ‘coping’ zone. There are three main areas in service operations. The customer, Front office and back office. The relation and interaction between these three units provide the basis of the strategy. For different services
All these facts make managing capacity is a big challenge in service sector.
Goodale, J.C. and Tunc, E. (1998), “Tour scheduling with dynamic service rates”, International journal of service industry management, Vol.9 No.3, pp. 226-47
Johnston, R. (1999), “Service operations management: return to roots” International journal of operations and production management, Vol.19 No.2, pp-104-24
Adenso-Diaz, B., Gonzalez-Torre, Pilar and Garcia, Virginia (2002), “A capacity management model in service industries”, International Journal of Service Industry Management, Vol.13 No.3, pp.286-302
Lovelock, H.C. (1992), “Seeking synergy in service operations: seven things marketers need to know about operations”, European Management Journal, Vol.10 No.1, March, pp. 22-9
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