Aggregate Planning 2 2

Planning Horizon Aggregate planning: Intermediate-range capacity planning, usually covering 6 to 18 months. Short range Intermediate range Long range Now 6 months 18 months

Long range Intermediate Manufacturing Services Short Master scheduling Material requirements planning Order scheduling Weekly workforce and customer scheduling Daily workforce and customer scheduling Process planning Strategic capacity planning Sales and operations (aggregate) planning Long range Intermediate Short Manufacturing Services Sales plan Aggregate operations plan Forecasting & demand management

Operations Planning Activities Long-range planning Greater than one year planning horizon Usually performed in annual increments Medium-range planning Six to eighteen months Usually with monthly or quarterly increments Short-range planning One day to less than six months Usually with weekly increments 3 3

Planning Tasks and Responsibilities

Aggregate Production Planning (APP) Matches market demand to company resources Plans production 6 months to 18 months in advance Expresses demand, resources, and capacity in general terms Develops a strategy for economically meeting demand Establishes a company-wide game plan for allocating resources

Balancing Aggregate Demand and Aggregate Production Capacity 10000 Suppose the figure to the right represents forecast demand in units 10000 8000 8000 7000 6000 5500 6000 4500 4000 Now suppose this lower figure represents the aggregate capacity of the company to meet demand 2000 Jan Feb Mar Apr May Jun 10000 9900 9500 9500 9000 9000 8800 8000 What we want to do is balance out the production rate, workforce levels, and inventory to make these figures match up 6000 4000 2000 Jan Feb Mar Apr May Jun

Aggregate Plan: Relationships Plan for Production Demand Forecasts, orders Master Schedule, and MRP systems Detailed Work Schedules External Capacity Subcontractors Inventory On Hand Raw Materials Available Work Force Marketplace and Demand Research and Technology Product Decisions Process Planning & Capacity

Inputs and Outputs to APP Company Policies Strategic Objectives Capacity Constraints Units or dollars subcontracted, backordered, or lost Size of Workforce Production per month (in units or $) Inventory Levels Financial Demand Forecasts Aggregate Planning

Aggregate Planning Inputs Resources Workforce Facilities Demand forecast Policies Subcontracting Overtime Inventory levels Back orders Common unit for measuring outputs Costs Inventory carrying Back orders Hiring/firing Overtime Inventory changes Subcontracting

Aggregate Planning Outputs A plan that specifies the optimal combination of production rate (units completed per unit of time) workforce level (number of workers) inventory on hand (inventory carried from previous period Subcontracting levels (if any) Backordering levels (if any) 6 6

Aggregate Planning Goals Meet demand Use capacity efficiently Meet inventory policy Minimize total cost

Aggregate Planning Strategies Proactive Alter demand to match capacity Reactive Alter capacity to match demand Mixed Some of each

Demand Management Shifting demand into other periods by incentives, promotions, advertising campaigns, pricing, etc. Offering product or services with counterseasonal demand patterns (counterseasonal product mixing) Backordering Creation of new demand Partnering with suppliers to reduce information distortion along the supply chain

Options of Adjusting Capacity to Meet Demand (1 of 2) Producing at a constant rate and using inventories to absorb fluctuations in demand ie. changing inventory levels Varying work force size (hiring and firing workers) so that production matches demand Varying production capacity by increasing or decreasing working hours (overtime or idle time)

Options of Adjusting Capacity to Meet Demand (2 of 2) Using part-time workers to change production rate Subcontracting work to other firms Providing the service or product at a later time period (backordering)

Strategy Details Overtime & undertime - common when demand fluctuations are not extreme Subcontracting - useful if supplier meets quality & time requirements Part-time workers - feasible for unskilled jobs or if labor pool exists Backordering - only works if customer is willing to wait for product/services

Capacity Options - Advantages and Disadvantages (1 of 4) Some Comments Changing inventory levels Changes in human resources are gradual, not abrupt production changes Inventory holding costs; Shortages may result in lost sales Applies mainly to production, not service, operations Varying workforce size by hiring or layoffs Avoids use of other alternatives Hiring, layoff, and training costs Used where size of labor pool is large

Advantages/Disadvantages (2 of 4) Option Advantage Disadvantage Some Comments Varying production rates through overtime or idle time Matches seasonal fluctuations without hiring/training costs Overtime premiums, tired workers, may not meet demand Allows flexibility within the aggregate plan Subcontracting Permits flexibility and smoothing of the firm's output Loss of quality control; reduced profits; loss of future business Applies mainly in production settings

Advantages/Disadvantages (3 of 4) Option Advantage Disadvantage Some Comments Using part-time Less costly and High Good for workers more flexible turnover/training unskilled jobs in than full-time costs; quality areas with large workers suffers; temporary labor scheduling pools difficult Influencing Tries to use Uncertainty in Creates demand excess capacity. demand. Hard to marketing ideas. Discounts draw match demand to Overbooking new customers. supply exactly. used in some businesses.

Advantages/Disadvantages (4 of 4) Option Advantage Disadvantage Some Comments Back ordering during high- demand periods May avoid overtime. Keeps capacity constant Customer must be willing to wait, but goodwill is lost. Many companies backorder. Counterseasonal products and service mixing Fully utilizes resources; allows stable workforce. May require skills or equipment outside a firm's areas of expertise. Risky finding products or services with opposite demand patterns.

The Extremes Level Strategy Chase Strategy Production equals demand Production rate is constant

Basic Aggregate Planning Strategies for Meeting Demand Level capacity strategy: Keeping work force constant and maintaining a steady rate of regular-time output while meeting variations in demand by a combination of options (such as using inventories + subcontracting) Chase demand strategy: Changing workforce levels so that production matches demand (the planned output for a period is set at the expected demand for that period.) Maintaining resources for high demand levels Ensures high levels of customer service

Level Production Production Demand Units Time

Chase Demand Production Demand Units Time

Level Strategy: Forecast and Average Forecast Demand 22 18 21 21 22 20

Level Strategy: Cumulative Demand Graph Jan Feb Mar Apr May Jun Cumulative forecast requirements Cumulative level production using average monthly forecast requirements Reduction of inventory Excess inventory Cumulative Demand (Units) 7,000 6,000 5,000 4,000 3,000 2,000 1,000

Level Approach Advantages Disadvantages Stable output rates and workforce Disadvantages Greater inventory costs Increased overtime and idle time Resource utilizations vary over time

Chase Approach Advantages Disadvantages Investment in inventory is low Labor utilization in high Disadvantages The cost of adjusting output rates and/or workforce levels

Aggregate Planning Methods Graphical & charting techniques Popular & easy-to-understand Trial & error approach Mathematical approaches Linear programming Transportation method Linear decision rule (LDR) Search decision rule (SDR) Management coefficients model Simulation

Summary of Planning Techniques

Steps of Trial & Error Method Forecast demand for each period Determine capacities (for regular time, overtime, subcontracting) for each period Identify policies that are pertinent Determine costs (labor, hiring/firing, holding etc.) Develop alternative plans and costs Select the best plan that satisfies objectives. Otherwise return to step 5.

Aggregate Planning Using Pure Strategies (Example 1) QUARTER SALES FORECAST (LB) Spring 80,000 Summer 50,000 Fall 120,000 Winter 150,000 Hiring cost = $100 per worker Firing cost = $500 per worker Inventory carrying cost = $0.50 pound per quarter Production per employee = 1,000 pounds per quarter Beginning work force = 100 workers

Level Production Strategy (1 of 2) QUARTER SALES FORECAST (LB) Spring 80,000 Summer 50,000 Fall 120,000 Winter 150,000 Level production = 100,000 pounds (50,000 + 120,000 + 150,000 + 80,000) 4

Level Production Strategy (2 of 2) Spring 80,000 100,000 20,000 Summer 50,000 100,000 70,000 Fall 120,000 100,000 50,000 Winter 150,000 100,000 0 Total 400,000 140,000 Cost = 140,000 pounds x 0.50 per pound = $70,000 SALES PRODUCTION QUARTER FORECAST PLAN INVENTORY

Chase Demand Strategy Spring 80,000 80,000 80 0 20 Summer 50,000 50,000 50 0 30 Fall 120,000 120,000 120 70 0 Winter 150,000 150,000 150 30 0 100 50 SALES PRODUCTION WORKERS WORKERS WORKERS QUARTER FORECAST PLAN NEEDED HIRED FIRED Cost = (100 workers hired x $100) + (50 workers fired x $500) = $10,000 + 25,000 = $35,000

APP Using Mixed Strategies January 1000 July 500 February 400 August 500 March 400 September 1000 April 400 October 1500 May 400 November 2500 June 400 December 3000 MONTH DEMAND (CASES) MONTH DEMAND (CASES) Production per employee = 100 cases per month Wage rate = $10 per case for regular production = $15 per case for overtime = $25 for subcontracting Hiring cost = $1000 per worker Firing cost = $500 per worker Inventory carrying cost = $1.00 case per month Beginning work force = 10 workers

Aggregate Planning (Example 2) Suppose we have the following unit demand and cost information: Demand/mo Jan Feb Mar Apr May Jun 4500 5500 7000 10000 8000 6000 Materials $5/unit Holding costs $1/unit per mo. Marginal cost of stockout $1.25/unit per mo. Hiring and training cost $200/worker Layoff costs $250/worker Labor hours required 0.15 hrs/unit Straight time labor cost $8/hour Beginning inventory 250 units Productive hours/worker/day 7.25 Paid straight hrs/day 8

Cut-and-Try Example: Determining Straight Labor Costs and Output Demand/mo Jan Feb Mar Apr May Jun 4500 5500 7000 10000 8000 6000 Given the demand and cost information below, what are the aggregate hours/worker/month, units/worker, and dollars/worker? 7.25x22 7.25/0.15=48.33 & 48.33x22=1063.33 22x8hrsx$8=$1408

Chase Strategy (Hiring & Firing to meet demand) Lets assume our current workforce is 7 workers. First, calculate net requirements for production, or 4500-250=4250 units Then, calculate number of workers needed to produce the net requirements, or 4250/1063.33=3.997 or 4 workers Finally, determine the number of workers to hire/fire. In this case we only need 4 workers, we have 7, so 3 can be fired.

Below are the complete calculations for the remaining months in the six month planning horizon

Below are the complete calculations for the remaining months in the six month planning horizon with the other costs included

Level Workforce Strategy (Surplus and Shortage Allowed) Lets take the same problem as before but this time use the Level Workforce strategy This time we will seek to use a workforce level of 6 workers

Below are the complete calculations for the remaining months in the six month planning horizon Note, if we recalculate this sheet with 7 workers we would have a surplus

Below are the complete calculations for the remaining months in the six month planning horizon with the other costs included Note, total costs under this strategy are less than Chase at $260.408.62 Labor Material Storage Stockout

APP by Linear Programming Minimize Z = $100 (H1 + H2 + H3 + H4) + $500 (F1 + F2 + F3 + F4) + $0.50 (I1 + I2 + I3 + I4) Subject to P1 - I1 = 80,000 (1) Demand I1 + P2 - I2 = 50,000 (2) constraints I2 + P3 - I3 = 120,000 (3) I3 + P4 - I4 = 150,000 (4) Production 1000 W1 = P1 (5) constraints 1000 W2 = P2 (6) 1000 W3 = P3 (7) 1000 W4 = P4 (8) 100 + H1 - F1 = W1 (9) Work force W1 + H2 - F2 = W2 (10) constraints W2 + H3 - F3 = W3 (11) W3 + H4 - F4 = W4 (12) where Ht = # hired for period t Ft = # fired for period t It = inventory at end of period t Pt = units produced in period t Wt = workforce size for period t

APP by the Transportation Method 1 900 1000 100 500 2 1500 1200 150 500 3 1600 1300 200 500 4 3000 1300 200 500 Regular production cost per unit $20 Overtime production cost per unit $25 Subcontracting cost per unit $28 Inventory holding cost per unit per period $3 Beginning inventory 300 units EXPECTED REGULAR OVERTIME SUBCONTRACT QUARTER DEMAND CAPACITY CAPACITY CAPACITY

The Transportation Tableau Unused PERIOD OF PRODUCTION 1 2 3 4 Capacity Capacity Beginning 0 3 6 9 Inventory 300 — — — 300 Regular 600 300 100 — 1000 Overtime 100 100 Subcontract 500 Regular 1200 — — 1200 Overtime 150 150 Subcontract 250 250 500 Regular 1300 — 1300 Overtime 200 — 200 Subcontract 500 500 Regular 1300 1300 Overtime 200 200 Demand 900 1500 1600 3000 250 1 2 3 4 PERIOD OF USE 20 23 26 29 25 28 31 34 28 31 34 37 20 23 26 25 28 31 28 31 34 20 23 25 28 28 31 20 25 28

Burruss’ Production Plan 1 900 1000 100 0 500 2 1500 1200 150 250 600 3 1600 1300 200 500 1000 4 3000 1300 200 500 0 Total 7000 4800 650 1250 2100 REGULAR SUB- ENDING PERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY

Other Quantitative Techniques Linear decision rule (LDR) Search decision rule (SDR) Management coefficients model Linear decision rule (LDR) payroll, staffing, over/undertime, inventory costs Search decision rule (SDR) find minimum cost combination of labor levels & production rates Management coefficients model uses regression analysis to improve consistency of planning 20

Hierarchical Planning Process Items Product lines or families Individual products Components Manufacturing operations Resource Level Plants Individual machines Critical work centers Production Planning Capacity Planning Resource requirements plan Rough-cut capacity plan Capacity requirements plan Input/ output control Aggregate production plan Master production schedule Material requirements plan Shop floor schedule All work centers

Aggregate Plan to Master Schedule Aggregate Planning Disaggregation Master Schedule

Disaggregating the Aggregate Plan Master schedule: The result of disaggregating an aggregate plan; shows quantity and timing of specific end items needed to meet demand for a scheduled horizon. Rough-cut capacity planning: Approximate balancing of capacity and demand to test the feasibility of a master schedule.

Master Scheduling Process Figure 13.6 Master Scheduling Process Master Scheduling Beginning inventory Forecast Customer orders Inputs Outputs Projected inventory Master production schedule Uncommitted inventory

Projected On-hand Inventory Inventory from previous week Current week’s requirements - =

Projected On-hand Inventory Figure 13.8 Beginning Inventory Customer orders are larger than forecast in week 1 Forecast is larger than Customer orders in week 2 Forecast is larger than Customer orders in week 3

Time Fences Time Fences – points in time that separate phases of a master schedule planning horizon.

“slushy” somewhat firm Time Fences in MPS Figure 13.12 Period 1 2 3 4 5 6 7 8 9 “frozen” (firm or fixed) “slushy” somewhat firm “liquid” (open)

Available-to-Promise PERIOD ON-HAND = 50 1 2 3 4 5 6 Forecast 100 100 100 100 100 100 Customer orders Master production schedule 200 200 200 Available to promise PERIOD ON-HAND = 50 1 2 3 4 5 6 Forecast 100 100 100 100 100 100 Customer orders 90 120 130 70 20 10 Master production schedule 200 200 200 Available to promise 40 0 170 ATP in period 1 = (50 + 200) - (90 + 120) = 40 ATP in period 3 = 200 - (130 + 70) = 0 ATP in period 5 = 200 - (20 + 10) = 170

Available-to-Promise Product Request Is the product available at this location? Is an alternative product available at an alternate location? Is an alternative product available at this location? Is this product available at a different location? Available-to-promise Allocate inventory Capable-to-promise date Is the customer willing to wait for the product? Revise master schedule Trigger production Lose sale Yes No

Aggregate Planning for Services Most services can’t be inventoried Demand for services is difficult to predict Capacity availability is also difficult to predict Service capacity must be provided at the appropriate place and time Labor is usually the most constraining resource for services Labor flexibility can be an advantage in services

Characteristics That Make Yield Management Work Service or product can be sold in advance of consumption Demand fluctuates Capacity is relatively fixed Demand can be segmented Variable costs are low and fixed costs are high

Hotel: Single Price Level Sales Demand Curve Potential customers exist who are willing to pay more than the $15 variable cost Passed up profit contributions Some customers who paid $150 for the room were actually willing to pay more $sales = Net price * 50 rooms =150*50 =$7500 Money left on the table $15 variable cost of room Price $150 Price charged for room $ Sales = $ 6,750

Hotel: Two Price Levels Demand Sales $100 Price #1 $200 Price #2 Total sales = 1st net price *30 + 2nd net price *30 = $8100 Net prices are: Price #1 => $85 Price #2 => $175 $15 variable cost of room $Sales = $ 8,100

Yield Management Cu Cu + Co P(n < x)  where n = number of no-shows x = number of rooms or seats overbooked Cu = cost of underbooking; i.e., lost sale Co = cost of overbooking; i.e., replacement cost P = probability

Yield Management NO-SHOWS PROBABILITY 0 .15 1 .25 2 .30 3 .30

Yield Management NO-SHOWS PROBABILITY P(N < X) 0 .15 .00 1 .25 .15 0 .15 .00 1 .25 .15 2 .30 .40 3 .30 .70 .517 Expected number of no shows 0(.15) + 1(.25) + 2(.30) + 3(.30) = 1.75 Optimal probability of no-shows P(n < x)  = = .517 Cu Cu + Co 75 75 + 70

Yield Management NO-SHOWS PROBABILITY P(N < X) Cost of overbooking [2(.15) + 1(.25)]$70 = $38.50 Cost of bumping customers (.30)$75 = $22.50 Lost revenue from no-shows $61.00 Total cost of overbooking by 2 rooms Expected savings = ($131.225 - $61) = $70.25 a night NO-SHOWS PROBABILITY P(N < X) 0 .15 .00 1 .25 .15 2 .30 .40 3 .30 .70 Expected number of no shows 0(.15) + 1(.25) + 2(.30) + 3(.30) = 1.75 Optimal probability of no-shows P(n < x)  = = .517 Cu Cu + Co 75 75 + 70 .517