Capacity Planning | Management Optional Notes for UPSC PDF Download

Introduction

• Every organization strives to align demand and supply to produce products or services at the lowest total cost. Achieving this goal relies on maximizing the utilization of the organization's supply capacity, which underscores the importance of capacity planning. Capacity planning becomes essential not only during the design or expansion of a system, such as a factory or production unit, but also for shorter operating periods where changes in plant size are impractical. Decisions regarding capacity determination or expansion hold significance as they form the basis for both long-term strategic planning and short-term resource management and control.
• Productive capacity, typically measured in physical units, represents either the maximum output rate of products or services or the availability of key resources such as machine-hours or man-hours in each operating period. When the output of an operations system is relatively standardized, nominal physical capacity can be defined as the maximum sustainable output rate achievable with a full workforce during regular working hours. However, in systems producing diverse products or services, capacity must be expressed in terms of critical resource inputs, such as labor-hours or machine-hours, as these cannot be measured in common units.
• Given that changes in capacity are made in response to anticipated changes in demand, the dimensions of capacity align with those of demand. This relationship between the dimensions of demand and their impact on capacity is illustrated in Table.

Aspects of Capacity Planning

• Capacity-related challenges manifest in three primary forms. Firstly, there's the issue of significant increases in capacity required to accommodate changes in demand over the long term, typically spanning 5 to 10 years. With most technologies, capacity increments occur in substantial chunks, even if they cannot be fully utilized upon installation. For instance, adding another shift, steel mill, or aircraft when demand exceeds available capacity leads to step increases in fixed costs that cannot be immediately absorbed. Instead, these costs gradually offset the anticipated demand growth over an extended period. Such capacity changes establish the upper limit of a plant's productive capacity, also known as the system design capacity.
• Secondly, within the confines of the system design capacity, limited adjustments can be made for shorter periods, ranging from a year to a couple of years, to accommodate fluctuations in demand arising from seasonality and business cycles. This necessitates aggregate planning strategies involving inventory management, workforce adjustments through hiring and layoffs, utilization of overtime, and subcontracting orders to other firms.
• Lastly, finer adjustments in capacity may be required to address short-term, unpredictable fluctuations in demand. These adjustments occur on a weekly or even daily basis, with operational scheduling methods employed for refinement.
• However, it's essential to note that random fluctuations in demand, being both unpredictable and uncontrollable, can impact the effectiveness of these methods in any given situation.

Determining Capacity Requirements

A feasibility study is typically conducted to ascertain the required capacity and its timing. The phases involved in long-term capacity-related studies are depicted in Fig..

Capacity Planning for a Single-Stage System:

• For simplicity, let's consider an example for an entire operations system. However, in practice, such analysis must be conducted separately for each stage of the production process. Initial analysis should focus on production stages identified as bottlenecks.
• Consider a manufacturing firm experiencing an average annual demand increase of 200 units for one of its products. The current maximum capacity is 2,400 units per year. By analyzing past data, the trend line for annual demand is estimated as Y_t = 600 + 200t (with t = 0 in 1984). Management aims to add sufficient capacity to meet expected demand over the next 12 years, assuming the linear upward trend continues.

• The minimum planning horizon duration is determined by the lead time required for capacity additions, including activities like engineering design and equipment installation. Management's review frequency of such matters is also crucial. For instance, if the lead time for adding a new facility is 3 years and management reviews these issues every 5 years after the latest additions, the minimum planning horizon should be 3 + 5 = 8 years.
• In the last year of the planning horizon (2004), the value of t in the trend-line equation will be 20. Hence, the expected annual demand in 2004 will be Y_2004 = 600 + (20 * 200) = 4,600 units. Thus, if the current trend continues, sufficient capacity must be provided to produce at an annual output rate of 4,600 units in 2004.
• Considering the current capacity limit of 2,400 units, the projected increase in capacity requirements for 2004 amounts to 4,600 - 2,400 = 2,200 units. Whether the required capacity will be added all at once or in smaller increments depends on the process technology. While a typical manufacturing unit might opt to build facilities gradually as needed, a process plant is often constrained by technology, limiting large-capacity increments. The decision hinges on balancing lower variable costs for large-capacity increments against high fixed costs that may not be absorbed due to underutilization in the near future. Fig. illustrates the projected capacity requirements to handle the increase in demand.

The above procedure does not account for the degree of uncertainty in future demand.

• This assessment can be done subjectively by top management planners or objectively by calculating a measure of dispersion of actual demand points from the trend line in the past. The projected estimate for net capacity requirements can be further adjusted to accommodate planned shutdowns for preventive maintenance or to account for unexpected growth or decline.
• For instance, if the firm decides to construct a new plant overseas with a capacity of 2,500 tonnes/year, management may wish to increase this by 20% for planned maintenance and an additional 10% for further growth. The required capacity adjustment would then be as follows:
  • Normal plant capacity = 2,500 * 1.20 = 3,000
  • Adjusted plant capacity = 3,000 * 1.10 = 3,300 units.
• This adjusted plant capacity represents the average annual output rate for the overseas plant. If there are no seasonal fluctuations, the monthly rate would be 3,300/12 = 275 tonnes. However, in the presence of a strong seasonal cycle typical of plants, actual requirements will exceed this monthly average during the peak season and be less than the average during slack periods.
• If management can rely on seasonal inventories, overtime, or subcontracting, the annual capacity requirements can be met based on the monthly rate calculated above. If inventories cannot be used, the production rate must be continually adjusted to align with actual demand over the time period.
• Let's assume the plant can handle demand with a monthly maximum capacity of 300 tonnes, which corresponds to an annual capacity increase to 3,600 tonnes. Between the extremes of 3,300 tonnes needed for producing at a constant rate of 275 tonnes/month and 3,600 tonnes to absorb peaks of up to 300 tonnes per month, a compromise plan can often be adopted. For example, an amount of (290 * 12) = 3,400 tonnes/year may be needed, influenced by the feasible scale of production and the technology of the process. Therefore, when determining long-term capacity, it's crucial to consider the feasibility of using short-term alternatives such as inventories, overtime, additional work shifts, or subcontracting.

Capacity Planning for a Multiple-Stage System:

• When the production process involves multiple stages, the determination of capacity requirements by previous methods applies to the output rate for the entire new system directly. However, capacity planning for multi-stage processes often becomes necessary. Different equipment configurations at each stage make it virtually impossible for all stages to operate with the same maximum capacity requirements as done for bottleneck operations or production stages. This can lead to higher operating costs due to underutilization of facilities at non-bottleneck operations. However, in some situations, this may be the only feasible alternative available.

Assessing Alternative Plant Sizes

• Determining the required capacity for a future planning period can involve considering various plant sizes, each with its own maximum capacity limit. This decision entails critical strategic considerations not only regarding the technology to be employed but also whether capacity will be concentrated in one centralized location or dispersed across several geographic locations. 
• In making this choice, management must evaluate not only production and distribution costs but also the impact of such decisions on competition, organizational structure, managerial approach, and the adaptability required for future environmental changes. At times, these strategic considerations carry more weight in the final selection process than quantifiable aspects related to technology and costs.

Traditional Economic Analysis

• While subjective factors are important, a proposal for capacity expansion should meet specific economic criteria. If the investment is projected to yield a satisfactory return within an acceptable level of risk over a given timeframe, the expansion project may be funded from the capital budget. Financial-performance metrics derived from cash-flow analysis, such as net present value (NPV) or rate of return on investment, can help evaluate the economic viability of the capacity expansion. Once the revenues and costs for the project are estimated, calculating these metrics becomes routine and can be computerized for analysis under various assumptions.
• For example, let's assume that the new capacity requirement of 3,300 units per year, as determined in the previous example, can be met by three different plant sizes with the cost characteristics outlined in Table 7.2.

Table 7.2: Data for three alternative plant sizes

From the data presented in Table 7.2, several key points emerge regarding capacity studies:

• Larger plant sizes necessitate substantial investments but can yield significant economies of scale, particularly near full-capacity production volumes. This often results in savings in construction and equipment costs per unit of capacity.
• Fixed costs per unit decrease with larger plant sizes, as expenses such as utilities, supervision, and insurance remain relatively constant over a wide range of capacities.
• Certain variable costs are spread over more units in larger plants, leading to a reduction in unit variable costs.
• Variable costs tend to be lower in larger plants due to economies in raw material procurement and shipping, as well as reduced processing costs from specialized equipment feasible only for large production volumes. Additionally, larger plants often employ more advanced technology, substituting capital for labor and resulting in higher organizational efficiencies through the application of advanced management techniques such as computer-aided scheduling, maintenance, and inventory control.
• The concept of economies of scale significantly influences economic activities across various sectors, including industries such as oil refining, steel production, communications, transportation, and service sectors like supermarkets, department stores, education, and government. When considering plant size, the level of expected demand must be considered alongside cost performance, often necessitating cost-volume-profit analysis.

Cost-Volume-Profit Analysis

• Optimizing plant size based on capacity requirements requires thorough analysis of cash flows for each potential plant size. This involves estimating total costs (TC) and total revenues (TR) at different production volumes for each plant size. Total costs are determined by fixed and variable costs associated with each plant size, while total revenues are determined by expected demand levels and the firm's pricing strategy. 
• By forecasting demand for each year within the planning horizon, the plant size that maximizes return on investment in new capacity can be selected, informed by a cost-volume-profit analysis for each plant size.

Determination of Equipment Requirements

Once the design capacity for the entire system and each individual production stage has been estimated, it is necessary to translate this capacity into specific equipment and workforce needs. The process for determining these requirements for a single stage, which may encompass a workstation or an entire department, is straightforward and can be outlined in the following steps. This same process can also be applied to determine requirements for multi-stage production systems.

Equipment Requirements For A Single Production Stage

To convert capacity measures into equipment requirements, two key pieces of information are needed:

• An estimate of demand for each period within the planning horizon, represented by the number of acceptable or conforming units needed per period, obtained from a detailed demand forecast.
• An estimate of processing time for the workstation where the equipment will be utilized. This is typically determined using work-measurement methods.

Let:

• P = Production rate of the workstation, measured in units of output per period
• T = Processing time per unit, expressed in minutes
• D = Duration of an operating period, measured in hours (e.g., D = 8 for one shift, D = 16 for two shifts, and D = 24 for three shifts)
• E = Efficiency of the equipment, represented as a percentage of running time per period, accounting for downtime due to setups, breakdown repair, or other idle periods
• N = Number of machines required by the workstation

The calculation of equipment requirements is based on the formula:

• To illustrate this relationship with an example, suppose that a fabrication section of a department must supply 4000 good parts daily to another department for assembly.
• Processing time is 3.00 min/unit, and the equipment efficiency for two-shift daily is estimated at 80 per cent. The equipment requirements for this case will be 

In practical application, it is crucial to approach the proposed method for determining equipment requirements with caution, particularly when examining the variables P and D in more detail. To accurately estimate P, it is essential to consider that the total number of units processed at a workstation may comprise both conforming (non-defective) and non-conforming (defective) units in a traditional production management system. Therefore, the total number of units (P) can be expressed as:
P = P_g + P_d

Where Pg represents the number of conforming units and Pd represents the number of non-conforming units. For a given operation, the number of defective units can be calculated as a percentage of defective units (p) over the total number of units processed. Thus, we have:

where p is the percentage defective output of the workstation. For our example_ if the number of required conforming parts is 4000 daily, and for the work station under study, the defective output amounts to 5 per cent, the output rate must be revised as follows:

 For the estimation of D. the use of 8 hours per shift is appropriate when the processing time T refers to an average required time per unit. If T represents a standard time, it includes not only the strictly productive time, but also allowances for operator fatigue, personal needs, and uncontrollable delays due to existing level of interference and other disturbances in the existing system. Therefore, when using standard times for T, the duration of an operating period must also be expressed in standard working hours. 

For example, if we have 25 working days per month and use two shifts, we have (2) (25) (8) = 400 actual hours. However, the average output rate of the workforce is 120, per cent of that based on standard times, possibly due to wage incentives or other performance enhancement schemes, the 400 hours are equivalent to (400) (1.20) = 480 standard hours per month. The revised equipment requirements for our example, assuming that the standard processing time is 3.50 minutes will be 

where T_s = standard processing time per unit

D_s = duration of operating period in standard hours

Determination of the Stage Efficiency, E

Equally important, but more difficult to assess, is the efficiency of a given work-station. Efficiency, E is normally defined as:

where E = work-station efficiency
H = expected running, time per period, in hours
D = duration of an operating period, in hours
DT = Downtime, in hours
ST = setup time for processing different orders per period, in hours.

Despite advancements in production technologies, a portion of the operating period is inevitably spent in forced idleness. This downtime may be due to various factors such as the need for machine repairs or adjustments during operation, power outages, or delays in the delivery of raw materials and components. 

• The efficiency of a given stage depends primarily on three factors: the type of equipment utilized, how the equipment is operated (including speeds, feeds, adjustments, etc.), and the maintenance policy implemented for the equipment. The increasing trend towards extensive mechanization and automation has introduced complex challenges in achieving consistent performance.
• For all types of equipment, preventive maintenance stands out as a highly effective approach for minimizing downtime from breakdowns, thereby directly enhancing efficiency. This necessitates close coordination with operations scheduling to minimize the overall costs associated with repairs, downtime, and preventive maintenance while ensuring timely deliveries. Another approach involves focusing on training and motivating operators to identify and address the root causes of breakdowns before they escalate into costly and time-consuming failures.
• Regarding Activity E in the Fabrication unit of your department, where 5000 good parts need to be supplied to another department for assembly, with a processing time of 4 minutes per unit and an equipment efficiency of 80% over three daily shifts, the equipment requirement for the department can be calculated. Additionally, the revised output should be calculated if the defective output amounts to 5%.

 Conclusion

• Choosing the most optimal combination of products and/or services stands as a crucial concern for any organization. Equally significant is the determination of the required productive capacity. Within specific time frames, capacity defines the maximum sustainable output rate if the produced items can be quantified in common units; otherwise, it refers to the maximum available critical resources, such as labor-hours or machine-hours. Determining productive capacity serves as the common thread for long-term, medium-term, and short-term planning.
• Long-term capacity decisions must consider how capacity will be utilized in the medium term, where the maximum capacity is fixed but demand experiences significant seasonal fluctuations. Depending on the company's strategy and available production alternatives, the design capacity may fall short of covering peak demand. This shortfall can be addressed through inventory management, workforce adjustments, subcontracting, or other feasible short-term alternatives. Available capacity must also adapt to the random fluctuations in demand as estimated in forecasts.
• The selection of an optimal plant size relies on economic analysis using traditional cost-volume-profit (or break-even) analysis. The aim is to balance the potential savings from economies of scale against the increased average unit costs due to underutilization of capacity in the initial years of operation. This problem can also be addressed under conditions of uncertainty using a decision-tree approach.
• Once the design capacity is established, equipment requirements for each period in the planning horizon can be determined using a straightforward method that balances the demand for capacity against equipment output rates. It's essential to recognize that a production stage typically needs to process more units than estimated for final demand due to losses from defective or non-conforming output. Capacity requirements must also consider limited efficiency attributed to setup time and downtime for repairs or delays.
• The methodologies used to determine productive capacity over time for a manufacturing organization can also be applicable in service organizations for capacity determination. Service systems are characterized by considerable randomness in both the pattern of units arriving for service and the time required to receive it, often referred to as waiting-time or queuing systems. Queueing models can be utilized to determine capacity requirements for a service facility.