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Chapter 14
Sales and Capacity Planning
Russell and Taylor
Operations and Supply Chain Management, 8th Edition
© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Lecture Outline
• The Sales and Operations Planning Process• Strategies for Adjusting Capacity• Strategies for Managing Demand• Quantitative Techniques for Aggregate Planning• Hierarchical Nature of Planning• Aggregate Planning for Services
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Learning Objectives
• Appreciate the interface of marketing, finance, and operations in S&OP planning
• Describe the monthly S&OP process and the importance of reconciling differences
• Utilize various tools and techniques to adjust capacity and manage demand
• Evaluate a demand scenario and select an appropriate S&OP strategy
• Describe hierarchical planning and the process of determining available-to-promise
• Determine overbooking, single orders, and fare class strategies for revenue management in services
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Sales and Operations Planning
• Determines resource capacity to meet demand over an intermediate time horizon• Aggregate refers to sales and operations planning for
product lines or families• Sales and Operations planning (S&OP) matches supply
and demand• Objectives
• Establish a company wide plan for allocating resources• Develop an economic strategy for meeting demand
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Sales and Operations Planning Process
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Monthly S&OP Planning Process
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Meeting Demand Strategies
• Adjusting capacity• Resources to meet demand are acquired and
maintained over the time horizon of the plan• Minor variations in demand are handled with overtime
or under-time
• Managing demand• Proactive demand management
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Strategies for Adjusting Capacity
• Level production• Producing at a constant rate and using inventory to
absorb fluctuations in demand• Chase demand
• Hiring and firing workers to match demand• Peak demand
• Maintaining resources for high-demand levels
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Strategies for Adjusting Capacity
• Overtime and under-time• Increase or decrease working hours
• Subcontracting• Let outside companies complete the work
• Part-time workers• Hire part-time workers to complete the work
• Backordering• Provide the service or product at a later time period
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Level Production
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Chase Demand
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Strategies for Managing Demand
• Shifting demand into other time periods– Incentives– Sales promotions– Advertising campaigns
• Offering products or services with counter-cyclical demand patterns
• Partnering with suppliers to reduce information distortion along the supply chain
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Quantitative Techniques For AP
• Pure Strategies• Mixed Strategies• Linear Programming• Transportation Method• Other Quantitative Techniques
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Pure Strategies
Hiring cost = $100 per worker
Firing cost = $500 per worker
Inventory carrying cost = $0.50 pound per quarter
Regular production cost per pound = $2.00
Production per employee = 1,000 pounds per quarter
Beginning work force = 100 workers
QUARTER SALES FORECAST (LB)
Spring 80,000Summer 50,000Fall 120,000Winter 150,000
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Level Production Strategy
Level production
Spring 80,000Summer 50,000Fall 120,000Winter 150,000
Cost of Level Production Strategy
SALES PRODUCTIONQUARTER FORECAST PLAN INVENTORY
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Level Production Strategy
Level production
= 100,000 pounds(50,000 + 120,000 + 150,000 + 80,000)
4
Spring 80,000 100,000 20,000Summer 50,000 100,000 70,000Fall 120,000 100,000 50,000Winter 150,000 100,000 0
400,000 140,000Cost of Level Production Strategy
(400,000 X $2.00) + (140,00 X $.50) = $870,000
SALES PRODUCTIONQUARTER FORECAST PLAN INVENTORY
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Chase Demand Strategy
Spring 80,000Summer 50,000Fall 120,000Winter 150,000
SALES PRODUCTION WORKERS WORKERS WORKERSQUARTER FORECAST PLAN NEEDED HIRED FIRED
Cost of Chase Demand Strategy
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Chase Demand Strategy
Spring 80,000 80,000 80 0 20Summer 50,000 50,000 50 0 30Fall 120,000 120,000 120 70 0Winter 150,000 150,000 150 30 0
100 50
SALES PRODUCTION WORKERS WORKERS WORKERSQUARTER FORECAST PLAN NEEDED HIRED FIRED
Cost of Chase Demand Strategy
(400,000 X $2.00) + (100 x $100) + (50 x $500) = $835,000
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Level Production with Excel
Inventory atend of summer
Input by user=400,000/4
Cost of level production= inventory costs + production costs
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Chase Demand with Excel
Workforce requirementscalculated by system
No. of workershired in fall
Production input by user;production =demand
Cost of chasedemand = hiring +firing + production
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Mixed Strategy
• Combination of Level Production and Chase Demand strategies
• Example policies• no more than x% of workforce can be laid off in one
quarter• inventory levels cannot exceed x dollars
• Some industries may shut down manufacturing during the low demand season and schedule employee vacations during that time
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
General Linear Programming (LP) Model
• LP gives an optimal solution, but demand and costs must be linear
• Let• Wt = workforce size for period t• Pt =units produced in period t• It =units in inventory at the end of period t• Ft =number of workers fired for period t• Ht = number of workers hired for period t
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LP MODELMinimize Z = $100 (H1 + H2 + H3 + H4)
+ $500 (F1 + F2 + F3 + F4)
+ $0.50 (I1 + I2 + I3 + I4)
+ $2 (P1 + P2 + P3 + P4)
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) 14-23
© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Setting up the Spreadsheet
Target cell;cost of solutiongoes here
Solver will put thesolution here
When model is complete, Solve
Click here nextDemand Constraint
Workforce Constraint
Production Constraint
Cells where solution appears
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Setting up the Spreadsheet
Click these boxes
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The LP Solution
Optimal production plan
Cost of optimal solution
Solution is a mixof inventory, hiringand firing
Extra report options
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Level Production for Quantum
Level = 12,000/12 = 1,000
Input by user
Excel calculates
these
Cost of level production
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Chase Demand for Quantum
Input by user
Excel calculates
these
Cost of chase demandNo. workershired in Feb.
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LP Solution for Quantum
Solver foundthis solution
Constraintequationsin these
cells
Optimal solution
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Transportation Method
1 900 1000 100 5002 1500 1200 150 5003 1600 1300 200 5004 3000 1300 200 500
Regular production cost $20/unitOvertime production cost $25/unitSubcontracting cost $28/unitInventory holding cost $3/unit-periodBeginning inventory 300 units
EXPECTED REGULAR OVERTIME SUBCONTRACTQUARTER DEMAND CAPACITY CAPACITY CAPACITY
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Transportation Tableau
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Transportation Tableau
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Burruss’ Production Plan
1 900 1000 100 0 5002 1500 1200 150 250 6003 1600 1300 200 500 10004 3000 1300 200 500 0
Total 7000 4800 650 1250 2100
REGULAR SUB- ENDINGPERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY
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Period 2’s endinginventory
Regular productionfor period 1
Cost of solution
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Excel and Transportation Method
© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Other Quantitative Techniques
• Linear decision rule (LDR)• Search decision rule (SDR)• Management coefficients model
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Hierarchical Nature of Planning
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Disaggregation
• Breaking an aggregate plan into more detailed plans
• Create Master Production Schedule for Material Requirements Planning
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Collaborative Planning
• Sharing information and synchronizing production across supply chain
• Part of CPFR (collaborative planning, forecasting, and replenishment)• involves selecting products to be jointly managed,
creating a single forecast of customer demand, and synchronizing production across supply chain
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Available-to-Promise (ATP)• Quantity of items that can be promised to customer• Difference between planned production and customer
orders already received
AT in period 1 = (On-hand quantity + MPS in period 1) –
(CO until the next period of planned production)
ATP in period n = (MPS in period n) –
(CO until the next period of planned production) • Capable-to-promise
• quantity of items that can be produced and made available at a later date
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
ATP
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ATP
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
ATP
ATP in April =
ATP in May =
ATP in June =
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ATP
ATP in April = (10+100) – 70 = 40
ATP in May = 100 – 110 = -10
ATP in June = 100 – 50 = 50
= 30
= 0
Take excess units from April
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Rule Based ATP
© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Aggregate Planning for Services
• Most services cannot be inventoried• Demand for services is difficult to predict• Capacity 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
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Yield Management
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Yield Management
NO-SHOWS PROBABILITY P(N < X)
0 .15 .001 .25 .152 .30 .403 .30 .70
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P(n < x) =
Cu
Cu + Co
Revenue = $100/night
Maintenance = $25/night
Overflow = $70/night
Co = $70
Cu = $100 - $25 = $75
Optimal probability of no-shows
© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Yield Management
NO-SHOWS PROBABILITY P(N < X)
0 .15 .001 .25 .152 .30 .403 .30 .70
Optimal probability of no-shows
P(n < x) = = .517Cu
Cu + Co
7575 + 70
.517
Hotel should be overbooked by two rooms
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© 2014 John Wiley & Sons, Inc. - Russell and Taylor 8e
Copyright 2014 John Wiley & Sons, Inc.All rights reserved. Reproduction or translation of this work beyond that permitted in section 117 of the 1976 United States Copyright Act without express permission of the copyright owner is unlawful. Request for further information should be addressed to the Permission Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages caused by the use of these programs or from the use of the information herein.
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