Capacity: Matching Productive Resource to Demand Chapter 14 Capacity: Matching Productive Resource to Demand

Learning Objectives Define capacity and distinguish it from capability. Describe how capacity relates to value and profitability for B2C and B2B transactions. Describe how demand that varies from design capacity influences profitability. Describe the role individual resources and constraints play in determining system capacity. Make the calculations necessary to create demand chase and level production aggregate plans Describe the information inputs and logic necessary for rough-cut capacity planning. Calculate required and available capacity for a rough-cut capacity plan. Describe the information inputs and logic necessary for capacity requirements planning. Calculate required and available capacity for a capacity requirements plan. Describe how yield management aids services in maximizing revenues. Perform the calculations to determine the number of customers to overbook.

Introduction: Matching Resource Availability to Market Demand Capacity The ability to produce at a given volume in a specified amount of time. Being able to do “enough” of something to meet demand. When making products, capacity can be stored in the form of work-in-process and finished-goods inventory. For services, capacity usually can’t be stored.

Matching Resource Availability to Market Demand: Capacity Defined Capacity is defined in terms of a level of output per unit of time. There must be enough capacity in each resource to meet demand.

Matching Resource Availability to Market Demand: Capacity Defined Capacity can be provided in various ways Mixing production and storage capacity Demand of 1,000 units is to be met in ten days

Capacity and Value Capacity affects value. What value attributes do customers seek? Cost Style/fashion Quality Ethical issues Response time Technology Dependability of delivery Flexibility Convenience Personalization When capacity can’t meet demand, customers may choose to go elsewhere rather than wait. A capacity shortage creates waiting lines and impacts response time, dependability of delivery, and flexibility. A backlog is a queue of orders waiting to be processed (another term for a waiting line).

Capacity and Value Excess (protective) capacity can serve the same purpose as inventory Protect against surges in demand. More flexible than buffering with finished goods Excess capacity is often in short supply Viewed as an investment in resources that aren’t providing a financial return. Yet it provides a long-term benefit because it improves various time-related value attributes

The Financial Impact of Capacity Decisions Design capacity The capacity a facility is designed to accommodate on an ongoing basis. Best operating level The level of demand or “load” on a system that results in the lowest cost per unit produced or processed. Going either above or below the best operating level for long periods has a negative financial impact

The Financial Impact of Capacity Decisions Below best operating level (A and E): Lower revenue Underutilized capacity drives up unit costs and/or forces layoffs Exhibit 14.3 Demand and Design Capacity Relationships

The Financial Impact of Capacity Decisions Above best operating level (C): High revenue and lower unit cost (both good) Equipment could wear out as maintenance is postponed Have to pay overtime and/or hire and train temps Possible decline in quality, increase in safety problems Exhibit 14.3 Demand and Design Capacity Relationships

Individual Resource Influence on System Capacity The overall capacity of a system is dependent on all of the resources used to create it. Constraint Anything that inhibits a system’s progress toward its goals. In a production system, a constraint is called a bottleneck. Constraint management An integrative framework that attempts to maximize the system’s accomplishment of its goals by managing the system’s constraint.

Individual Resource Influence on System Capacity Step 3 is slower than the rest. It is the constraint in the system. Characteristics of constrained systems Resources don’t set their own utilization rates Time lost by the constraint is lost (and can’t be made up) to the entire system Constraints must be utilized 100% of the time to maximize output If a constraint breaks down, the result is the same as shutting down the entire system. Exhibit 14.4 Example of a Constrained System

Individual Resource Influence on System Capacity: Constraints Increasing output of a non-bottleneck doesn’t help the system. It just builds up inventory at the bottleneck. A system can be thought of as analogous to a chain. A chain is only as strong as its weakest link.

A Broader View: Supply Chain Capacity A business can constrain an entire supply chain. The supply chain can be thought of as a “chain of chains”. Exhibit 14.7 Weakest Link in a Supply Chain Constraints don’t have to be in production resources or machines. They can occur in transportation, distribution, or storage.

The Demand-Capacity Match in Manufacturing Aggregate demand The total demand for all products and services Often utilizes a “pseudoproduct” or fictional “representative” product Two Alternatives for Aggregate Planning: Demand Chase: Adjusting capacity to meet varying demand; hiring and firing workers Primary cost is hiring and firing costs Loss of labor quality Level Production: Building up and using from inventory, while keeping workforce level Primary cost is inventory carrying cost

The Demand-Capacity Match in Manufacturing Step-by-Step: Demand Chase Aggregate Planning Convert units required for each time period to labor hours by multiplying number of units by labor hours required per unit. Compute number of workers required per period by dividing number of labor hours by the number of hours worked by each worker (rounding up). Hires are required if the number of workers needed in a time period is greater than the number of workers needed in the previous period. The difference between the two is the number of hires. Fires or layoffs are required if the number of workers needed in a time period is less than the number needed in the previous time period. The difference between the two is the number of fires. Calculate hiring costs by multiplying number of workers hired by cost of hiring a worker. Calculate firing costs the same way. Calculate total plan costs by summing hiring and firing costs.

The Demand-Capacity Match in Manufacturing: Demand Chase Example Labor per unit = 1.2 hours 40 hour week (160 hours per month) Hiring cost = $475 Firing cost = $400 Initial staffing = 11 employees

The Demand-Capacity Match in Manufacturing: Demand Chase Example Labor per unit = 1.2 hours 40 hour week (160 hours per month) Hiring cost = $475 Firing cost = $400 Initial staffing = 11 employees 1,400 * 1.2 = 1,680 hours required

The Demand-Capacity Match in Manufacturing: Demand Chase Example 1,680 / 160 = 10.5 workers required Round to 11 workers in order to meet demand

The Demand-Capacity Match in Manufacturing: Demand Chase Example Total hiring cost = $6,650 Total layoff cost = $4,400 Total plan cost = $11,050

The Demand-Capacity Match in Manufacturing Step-by-Step: Level production aggregate planning. Divide total expected demand for the planning horizon by the number of days to get number of units to produce each day Multiply the number of units produced each day by the number of labor hours per unit to get labor hours required per day Divide labor hours required per day by number of hours each worker works in a day to get number of workers required Get average level of inventory for each month by averaging beginning and ending inventory. Get inventory carrying cost by multiplying monthly average inventory by monthly carrying cost per unit Total cost is the sum of the monthly inventory carrying costs plus any initial hiring and firing costs

The Demand-Capacity Match in Manufacturing: Level Production Example Labor = 1.2 hours per unit 40 hour week (8 hours per day) Hiring cost = $475 Layoff cost = $400 Initial staffing = 11 employees 253 working days per year Total forecast production = 21,320 units Carrying costs = $12 per unit per month 85 units per day (85 units * 1.2 hours)/8 hours per day 12.75  13 workers on staff

The Demand-Capacity Match in Manufacturing: Level Production Example Hire two workers for $950 Carrying cost = $91,890 Plan cost = $92,840

The Demand-Capacity Match in Manufacturing It is unlikely that either the demand chase or level production aggregate plan will be totally acceptable. Best solution is some hybrid of using inventory and adjusting the level of the workforce. Adjust workforce levels periodically and level the production between adjustments

Detailed Capacity Planning in Manufacturing Capacity-demand relationships must be addressed on a detailed level in the short term. Goal is to determine the load on firm resources Capacity planning processes are typically integrated with dependent demand inventory- with material requirements planning (MRP) Bill of capacity: A statement of the time required on each resource needed to produce a product. Rough-cut capacity planning: An approach used in manufacturing that uses the master production schedule to provide the quantity of units that must be produced.

Detailed Capacity Planning in Manufacturing Step-by-Step: Rough-Cut Capacity Planning Calculate capacity required (in hours) for each work center for each week by multiplying the quantity of items to be produced in each week (from the master production schedule) by the time it takes on that particular work station to produce them Calculate available capacity (in standard hours) at each work center by multiplying the actual hours available in a week by the historical utilization and by the historical efficiency On a weekly basis, compare required capacity to available capacity, for each work station

Rough-Cut Capacity Planning Example A manufacturer of crates wants to create a rough-cut capacity plan for one of their lines (model 2440). The utilization rate for all machines averages 93%. Efficiency is at 95%. Develop a rough-cut capacity plan using data from master production schedule and the bill of capacity.

Rough-Cut Capacity Planning Example First, multiply the number of units you will produce in each week (from the MPS) by the time one unit takes at each work center (from the bill of capacity). Exhibit 14.14 Master Production Schedule 210 * 0.08 = 16.8 Do this for all the numbers to create a table Exhibit 14.15 Bill of Capacity

Rough-Cut Capacity Planning Example The table below shows capacity required at each work center, each week. Exhibit 14.16 Capacity Required

Rough-Cut Capacity Planning Example Multiply actual hours available by the historical utilization rate for the work center and also by historical efficiency to get capacity available, in “standard hours”. Exhibit 14.17 Capacity Available

Rough-Cut Capacity Planning Example Combine the capacity required and capacity available tables for easy comparison. Exhibit 14.18 Rough-cut Capacity Plan

Detailed Capacity Planning in Manufacturing Rough-cut capacity planning is a quick but inaccurate analysis. It is frequently used to check if the MPS is feasible Ignores on-hand inventory Ignores possibility of production occurring in weeks before the MPS shows a product to be due Capacity Requirements Planning is more accurate. Uses the planned releases from MRP- timing of production is more accurate Accounts for inventory

Detailed Capacity Planning in Manufacturing Step-by-Step: Capacity Requirements Planning Using MRP logic, compute planned order releases for all components For each department or work station, identify the components that will utilize that work station Compute required capacity for each work station by multiplying quantity of a component (from planned order releases) by the time required per unit on that work station. Do this for each week Compute total capacity required on a work station for a given week by summing the time required for each of the components using it

Capacity Requirements Planning Example A fly rod manufacturer wants to use planned order releases from MRP to help plan for capacity. The manufacturer has three departments: Handle assembly, wrapping department, and finishing. The product structure of a fly rod is given below: Exhibit 14.19 Product Structure for 9-foot 5-Weight Fly Rod

Capacity Requirements Planning Example MRP logic generates the planned order releases: All the bottom-level components are purchased, so assembling these components into the butt and tip sections is what needs capacity. Exhibit 14.20 Planned Order Releases

Capacity Requirements Planning Example Multiply the time each section takes at each department by the planned order releases from MRP. 48 * 0.08 = 3.84 Exhibit 14.21 Routings for Components Exhibit 14.20 Planned Order Releases

Capacity Requirements Planning Example Add butt and tip section capacities to get total required capacity per week per department. Exhibit 14.22 Capacity Requirements Plan

Detailed Capacity Planning in Manufacturing Relationships between material and capacity planning within a generic production planning and control system: Exhibit 14.23 Generic Manufacturing Planning and Control System

The Demand-Capacity Match in Services For services, the load on capacity can’t be leveled by inventory. We must often smooth demand to smooth the load on capacity. We use appointments, reservations, or pricing strategies to level demand. Services that have high fixed costs and little marginal cost for additional customers have developed more sophisticated approaches, such as yield management.

The Demand-Capacity Match in Services: Yield Management Yield Management: An approach used in capital-intensive services that attempts to obtain maximum revenues through differential pricing, reservation systems, and overbooking. Used in services with high fixed costs, low variable costs. Requires segmenting the customer base, using multiple price levels

The Demand-Capacity Match in Services: Overbooking Overbooking: Taking more reservations than you have capacity. Minimizing costs associated with “no-shows” when reservations are used Balance these costs with costs of not being able to serve a customer (or having to serve them differently) when too many show up Common issue in high fixed cost service businesses and where “no-shows” are a problem Airlines Hotels

The Demand-Capacity Match in Services: Overbooking Step-by-Step: Determining the Low-Cost Overbooking Policy. Calculate percentage of the time each no-show condition occurs Determine the cost of walking or bumping a customer and the opportunity cost of a vacant room or seat Identify an overbooking policy to evaluate. For each scenario under the policy, determine the cost of the no-shows or bumping. If there are empty rooms, calculate the expected cost of that condition by multiplying the number of empty rooms by the cost per empty room by the probability of this condition occurring. Similarly, multiply number of customers bumped by the cost of bumping, by the probability of it happening. The cost of the plan is the sum of all bumping and empty room costs Repeat last step for any overbooking policies being evaluated, and pick the one with the lowest cost

The Demand-Capacity Match in Services: Overbooking Example A hotel wants to use overbooking to minimize the cost of no-shows. Over the past year it has had an average of 1.56 no-shows per day, at a cost of $89 each. It has calculated the cost of bumping a customer to be $110. No-show probabilities are given below. Find the best overbooking policy. Exhibit 14.24 No-show History

The Demand-Capacity Match in Services: Overbooking Example Overbooking by one results in a total cost of $101.38 Exhibit 14.25 Expected Costs of Overbooking by One

The Demand-Capacity Match in Services: Overbooking Example Overbooking by two results in a total cost of $143.72 Exhibit 14.26 Expected Costs of Overbooking by Two

The Demand-Capacity Match in Services: Overbooking Example Overbooking by one is the best policy: Expected cost is less than no overbooking, and less than overbooking by two. There is no need to try policies where you overbook by three or more, as costs will only increase

Current Trends in Capacity Management The goal is flexibility Temporary workers Contract manufacturers Outsourced components All of these have negative aspects, as far as quality, reliability, and increased transportation time, but there are tradeoffs in all capacity decisions.