chapter 6: Planning capacity Capacity the maximum rate of output of a process or a system. Acquisition of new capacity requires extensive planning, and often involves significant expenditure of resources and time. Capacity decisions must be made in light of several long-term issues such as the firm’s economies and diseconomies of scale, capacity cushions, timing and sizing strategies, and trade-offs between customer service and capacity utilization. Planning capacity across the organization
Accounting provide cost information needed to evaluate capacity expansion Finance financial analysis of proposed capacity expansion investments and raises funds Marketing demand forecasts needed to identify capacity gaps. Operations selection of capacity strategies that can be implemented to effectively meet future demand. Human Resources hiring and training employees needed to support internal capacity plans. planning long-term capacity When choosing a capacity strategy: How much of a cushion is needed to handle variable or uncertain demand? Should we expand capacity ahead of demand, or wait until demand is more certain? easures of capacity and utilization Output Measures Are best utilized when applied to individual processes within the firm, or when the firm provides a relatively small number of standardized services and products. For example, a car manufacturing plant may measure capacity in terms of the number of cars produced per day. Inputs Measures Are used for low-volume, flexible processes (custom products). For example a custom furniture maker might measure capacity in terms of inputs such as number of workstations or number of workers. The problem of input measures is that demand is expressed as an output rate.
If the furniture maker wants to keep up with demand, he must convert the business’s annual demand for furniture into labor hours and number of employees required to fulfill those hours. Utilization Degree to which a resource (equipment, space, worker) is currently being used. Utilization= Average Output RateMaximum Capacityx 100% The numerator and the denominator should be measured in the same units. A process can be operated above the 100%, with overtime, extra shifts, overstaffing, subcontracting, etc, but this is not sustainable for long. Economies of scale
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Economies of scale The average unit cost of a service or good can be reduced by increasing its output rate. Why? * Spreading fixed costs same fixed costs divided by more units * Reducing construction costs doubling the size of the facility usually doesn’t double construction costs (building permits, architect’s fees, rental) * Cutting costs of purchased materials better bargaining position and quantity discounts * Finding process advantages speed up the learning effect, lowering inventory, improving process and job designs, and reducing the number of changeovers. diseconomies of scale
Diseconomies of scale The average cost per unit increases as the facility’s size increases. The reason is that excessive size can bring complexity, loss of focus, and inefficiencies. capacity timing and sizing strategies sizing capacity cushions Capacity cushion=100%-Average Utilization rate (%) When the average utilization rate approaches 100% for long periods, it’s a signal to increase capacity or decrease order acceptance to avoid declining productivity. The optimal capacity cushion depends on the industry. Particularly, in front-office processes where customers expect fast service times, large cushions are vital (more variable demand).
For capital-intensive firms, minimizing the capacity cushion is vital (unused capacity costs money). timing and sizing expansion Two strategies: * Expansionist strategy large, infrequent jumps in capacity. Is ahead of demand, and minimizes the chance of sales lost to insufficient capacity * Wait-and-see strategy smaller, more frequent jumps. It lags behind demand. To meet any shortfalls, it relies on short-term operations (overtime, temporary workers, subcontractors, postponement of preventive maintenance on equipment).
It reduces the risk of overexpansion based on overly optimistic demand forecasts, obsolete technology, or inaccurate assumptions regarding the competition. This strategy fits the short-term outlook but can erode market share over the long run. Timing and sizing of expansion are related: if demand is increasing and the time between increments increases, the size of the increments must also increase. An intermediate strategy can be “follow the leader”, so nobody gains a competitive advantage for being ahead of demand, and everyone shares the agony of overcapacity in the other case. inking capacity and other decisions Capacity cushions in the long run buffer the organization against uncertainty, as do resource flexibility, inventory, and longer customer lead times. If a change is made in any one decision area, the capacity cushion may also need to be changed to compensate. For example: Lower volume of production (more capacity cushion) to raise prices or vice versa. a systematic approach to long-term capacity decisions 4 steps: 1. Estimate future capacity requirements 2. Identify gaps by comparing requirements with available capacity 3. Develop alternative plans for reducing the gaps . Evaluate each alternative, both qualitatively and quantitatively, and make a final choice step 1: estimate capacity requirements A process’s capacity requirement is what its capacity should be for some future time period to meet the demand of the firm’s customers (external or internal), given the firm’s desired capacity cushion. Larger requirements are practical for processes or workstations that could potentially be bottlenecks in the future, and management may even plan for longer cushions than normal. Capacity requirements can be expressed in: * Output measure * Input measure
Either way, the foundation for the estimate is forecasts of demand, productivity, competition, and technological change. The further ahead you look, the more chance you have of making an inaccurate forecast. Using output measures Demand forecasts for future years are used as a basis for extrapolating capacity requirements into the future. If demand is expected to double in the next 5 years, then the capacity requirements also double. For example: Actual demand 50 customers per day; expected demand = 100 customers per day; desirable cushion = 20%. So capacity should be (100)/(1-0. )=125 customers per day. Using input measures Output measures may be insufficient in these situations: * Product variety and process divergence is high (customized products) * The product or service mix is changing * Productivity rates are expected to change * Significant learning effects are expected In these cases, an input measure should be used (number of employees, machines, trucks, etc) One product processed When just one service or product is processed at an operation and the time period is a particular year, the capacity requirement (M) is: M=DpN[1-C100]
D=demand forecast for the year (number of customers served or units produced) p=processing time (in hours per costumer served or unit produced) N=Total number of hours per year during which the process operates C=desired capacity cushion (expressed as a percent) M=number of input units required and should be calculated for each year in the time horizon Many products processed Setup time time required to change a process or an operation from making one service or product to making another. To calculate the total setup time D/Q*s Where D=demand forecast for the year
Q= number of units processed between setups s= time per setup For example, if the demand is 1200 units, and the average lot size is 100, there are 1200/100=12 setups per year. Accounting for both processing and setup times for multiple products, we get: M=[Dp+DQs]product 1+[Dp+DQs]product 2+…+[Dp+DQs]product nN[1-C100] When “M” is not an integer and we are talking about number of machines, you can round up the fractional part, unless it is cost efficient to use short-term options, such as overtime or stockouts.
But if we are talking about number of employees and we get 23. 6, we can use 23 employees and use a little overtime (in this case, 60% of a full-time person). step 2: identify gaps A capacity gap is any difference (positive or negative) between projected capacity requirements (M) and current capacity. step 3: develop alternatives Develop alternative plans to cope with projected gaps. One alternative is the base case do nothing and simply lose orders from any demand that exceeds current capacity or incur costs because capacity is too large.
Other alternatives: various timing and sizing options (expansionist or wait-and-see strategies); expanding at a different location; and using short term options. For reducing capacity, the alternatives include closing plants, laying off employees, reducing days or hours of operations. step 4: evaluate the alternatives Evaluate qualitatively and quantitatively. Qualitative concerns The manager looks at how each alternative fits the overall capacity strategy and other aspects of the business not covered by the financial analysis (uncertainties about demand, competitive reaction, technological change, and cost estimates).
Some of these factors can’t be quantified and must be assessed on the basis of judgment and experience. Quantitative concerns The manager estimates the change in cash flows for each alternative over the forecast time horizon compared to the base case. tools for capacity planning waiting-line models Are useful in high customer-contact processes. Waiting-line models use probability distributions to provide estimates of average customer wait time, average length of waiting lines, and utilization of the work center.
Managers can use this information to choose the most cost-effective capacity, balancing customer service and the cost of adding capacity. This topic will be treated more deeply in the appendix (siguiente resumen) simulation Simulations can identify the process’s bottlenecks and appropriate capacity cushions, even for complex processes with random demand patterns and predictable flows in demand during a typical day. decision trees A decision tree can be particularly valuable for evaluating different capacity extension alternatives when demand is uncertain and sequential decisions are involved.
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