Andy Guo Planning Demand and Supply in a Supply Chain 第二單元 (3) : Planning Demand and...
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Transcript of Andy Guo Planning Demand and Supply in a Supply Chain 第二單元 (3) : Planning Demand and...
Andy Guo
Planning Demand and Supply in a Supply Chain
Planning Demand and Supply in a Supply Chain
第二單元 (3) : Planning Demand and Supply in a Supply Chain
郭瑞祥教授【 本 著 作 除 另 有 註 明 外 , 採 取創用CC「姓名標示-非商業性-相同方式分享」台灣3.0版授權釋出】
1
Andy Guo
Outline
►Part I: Aggregate planning
►Part II: Managing predictable variability
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Andy Guo
Aggregate Planning► A process by which a company determines levels of capacity, production,
subcontracting, inventory, stockouts, and pricing to maximize the firm’s profit over the planning horizon (3-18 months) given the demand forecast for each period.
► Focus on aggregate decisions rather than SKU level decisions.
► Identify operational parameters:
– Production rate
– Workforce
– Overtime
– Machine capacity level
– Subcontracting
– Backlog
– Inventory on hand3
Andy Guo
Information Required for Aggregate Planning
► Demand forecast for each period in the planning horizon
► Production costs
► Labor/machine hours required per unit
► Inventory holding cost
► Stockout or backlog cost
► Constraints: limits on overtime, layoffs, capital, stockouts/backlogs
– Labor costs: regular time and overtime costs
– Subcontracting cost
– Cost of changing capacity: hiring/laying off labor, machine capacity change
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Andy Guo
Aggregate Planning
► Production quantity from regular time, overtime, and subcontracted time
► Inventory held► Backlog/stockout quantity► Workforce hired/laid off► Machine capacity increase/decrease
► Capacity (regular time, overtime, subcontracted)► Inventory► Backlog/lost sales
Determinations
Tradeoffs
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Andy Guo
Basic Strategy
► Use capacity as the lever
► Production rate synchronized with demand rate by
varying capacity of employee and machine
► Result in low inventory and high level of change in
capacity and workforce► Used when the cost of carrying inventory is very
expensive and cost to change levels of capacity and workforce are low
Chase Strategy
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Andy Guo
Basic Strategy
Time Flexibility from Workforce or Capacity Strategy
► Use utilization as the lever
► Workforce is kept stable but the number of hours
worked is varied over time
► Result in low inventory and lower average utilization
► Used when inventory carrying cost are high and
capacity is inexpensive
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Andy Guo
Basic Strategy
► Use inventory as the lever
► Stable machine capacity and workforce are maintained with
a constant output rate
► Result in large inventory and backlogs
► Used when inventory carrying costs and backlog costs are
low
Level Strategy
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Andy Guo
Scenario 1: Aggregate Planning at Red Tomato Tools
Month Demand Forecast
January 1,600
February 3,000
March 3,200
April 3,800
May 2,200
June 2,200
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9
Andy Guo
Costs for Red Tomato
Item Cost
Material cost $10/unit
Inventory holding cost $2/unit/month
Marginal cost of stockout/backlog $5/unit/month
Hiring and training costs $300/worker
Layoff cost $500/worker
Labor hours required 4/unit
Regular time cost $4/hour
Overtime cost $6/hour
Cost of subcontracting $30/unit
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Andy Guo
Red Tomato
► Sell gardening tool which is highly seasonal– Peak in spring– Main capacity is “size of workforce”
► Sell each tool at $40► Starting inventory in January is 1000 tools► Starting employees in January is 80 persons► The plant has a total of 20 working days/month and 8 regular
hours/day► Ending inventory is 500► All demand must be satisfied; no stock out in the last period
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Andy Guo
Costs for Red Tomato
Item Cost
Material cost $10/unit
Inventory holding cost $2/unit/month
Marginal cost of stockout/backlog $5/unit/month
Hiring and training costs $300/worker
Layoff cost $500/worker
Labor hours required 4/unit
Regular time cost $4/hour
Overtime cost $6/hour
Cost of subcontracting $30/unit
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Andy Guo
Aggregate Planning Model
Wt = Workforce size for month t, t = 1, ...,6
Ht = Number of employees hired at the beginning of month t, t = 1, ...,6
Lt = Number of employees laid off at the beginning of month t, t = 1, ...,6
Pt = Production in month t, t = 1, ...,6
It = Inventory at the end of month t, t = 1, ...,6
St = Number of units stocked out/backlogged at the end of month t, t = 1, ...,6
Ct = Number of units subcontracted for month t, t = 1, ...,6
Ot = Number of overtime hours worked in month t, t = 1, ...,6
Define Decision Variables
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Andy Guo
Define Objective Function
6
1
6
1
6
1
6
1
6
1
6
1
6
1
6
13010525003006640
tt
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tt
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tt
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tt CPSILHOWMin
Regular time labor cost
Overtime labor cost
Cost of hiring and layoffs
Cost of inventory and stockout
Cost of materials and subcontracting
Regular time labor cost
Overtime labor cost
Cost of hiring and layoffs
Cost of inventory and stockout
Regular time labor cost = 4/hr * 8 hr/day * 20 day/month= 640
Since all demands is met, revenue is fixed
maximizing profit = minimizing cost
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Andy Guo
► Workforce size for each month is based on hiring and layoffs
► Production for each month cannot exceed capacity
► Inventory balance for each month
► Overtime limit for each month
Define Constraints
80 where6,...,1fo r 01 WtLHWW tttt
6,...,1fo r 440 tOWP ttt
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Andy Guo
► Workforce size for each month is based on hiring and layoffs
► Production for each month cannot exceed capacity
► Inventory balance for each month
► Overtime limit for each month
Define Constraints
80 where6,...,1fo r 01 WtLHWW tttt
6,...,1fo r 440 tOWP ttt
6,...,1fo r 440 tOWP ttt
20*8=160 hrs4 hours/unit
160
4
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Andy Guo
► Workforce size for each month is based on hiring and layoffs
► Production for each month cannot exceed capacity
► Inventory balance for each month
► Overtime limit for each month
Define Constraints
80 where6,...,1fo r 01 WtLHWW tttt
6,...,1fo r 440 tOWP ttt
0 ,0 ,500 ,000,1where
6,...,1for
600
11
6
SSII
tSISDCPI ttttttt
6,...,1fo r 11 tSISDCPI ttttttt
Net supply in current
period
Net supply in previous
period
Supply Demand
DCPSISI ttttttt )()( ) ( 11
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Andy Guo
► Workforce size for each month is based on hiring and layoffs
► Production for each month cannot exceed capacity
► Inventory balance for each month
► Overtime limit for each month
Define Constraints
80 where6,...,1fo r 01 WtLHWW tttt
6,...,1fo r 440 tOWP ttt
0 ,0 ,500 ,000,1where
6,...,1for
600
11
6
SSII
tSISDCPI ttttttt
6,...,1fo r 10 tWO tt
18
Andy Guo
Aggregate Planning Model
Wt = Workforce size for month t, t = 1, ...,6
Ht = Number of employees hired at the beginning of month t, t = 1, ...,6
Lt = Number of employees laid off at the beginning of month t, t = 1, ...,6
Pt = Production in month t, t = 1, ...,6
It = Inventory at the end of month t, t = 1, ...,6
St = Number of units stocked out/backlogged at the end of month t, t = 1, ...,6
Ct = Number of units subcontracted for month t, t = 1, ...,6
Ot = Number of overtime hours worked in month t, t = 1, ...,6
Define Decision Variables
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Andy Guo
Defining Constraints
6,...,1fo r 440 tOWP ttt
1604
20*8=160 hrs4 hours/unit
6,...,1fo r 11 tSISDCPI ttttttt
DCPSISI ttttttt )()( ) ( 11
Net supply in current
period
Net supply in previous
period
Supply Demand
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Andy Guo
Evaluation of Performance
TIIIT
ttT /2/)(
1
10
Little’s law: average flow time = average inventory / throughput
► Average inventory =
► Average flow time =
21
Andy Guo
Evaluation of Performance
TIIIT
ttT /2/)(
1
10
T
tt
T
ttT TDTIII
1
1
10 //2/)(
Little’s law: average flow time = average inventory / throughput
► Average inventory =
► Average flow time =
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Andy Guo
EXCEL-DEMO
Wt = Workforce size for month t, t = 1, ...,6
Ht = Number of employees hired at the beginning of month t, t = 1, ...,6
Lt = Number of employees laid off at the beginning of month t, t = 1, ...,6
Pt = Production in month t, t = 1, ...,6
It = Inventory at the end of month t, t = 1, ...,6
St = Number of units stocked out/backlogged at the end of month t, t = 1, ...,6
Ct = Number of units subcontracted for month t, t = 1, ...,6
Ot = Number of overtime hours worked in month t, t = 1, ...,6
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Andy Guo
EXCEL-DEMO
300 X B5
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Andy Guo
EXCEL-DEMO2
6
1
6
1
6
1
6
1
6
1
6
1
6
1
6
13010525003006640
tt
tt
tt
tt
tt
tt
tt
tt CPSILHOWMin
Define Objective Function
6
1
6
1
6
1
6
1
6
1
6
1
6
1
6
13010525003006640
tt
tt
tt
tt
tt
tt
tt
tt CPSILHOWMin
Define Objective Function
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Andy Guo
EXCEL-DEMO3
cost
Decision Variables
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Andy Guo
EXCEL-DEMO4
6
1
6
1
6
1
6
1
6
1
6
1
6
1
6
13010525003006640
tt
tt
tt
tt
tt
tt
tt
tt CPSILHOWMin
Define Objective Function
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Andy Guo
EXCEL-DEMO5
6
1
6
1
6
1
6
1
6
1
6
1
6
1
6
13010525003006640
tt
tt
tt
tt
tt
tt
tt
tt CPSILHOWMin
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Andy Guo
EXCEL-DEMO6
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Andy Guo
► Workforce size for each month is based on hiring and layoffs
► Production for each month cannot exceed capacity
► Inventory balance for each month
► Overtime limit for each month
Define Constraints
80 where6,...,1fo r 01 WtLHWW tttt
6,...,1fo r 440 tOWP ttt
0 ,0 ,500 ,000,1where
6,...,1for
600
11
6
SSII
tSISDCPI ttttttt
6,...,1fo r 10 tWO tt
30
Andy Guo
EXCEL-DEMO7
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Andy Guo
Optimal Aggregate Plan for Scenario 1
Periodt
No.Hired
Ht
No. LaidOff Lt
WorkforceSize Wt
OvertimeOt
InventoryIt
StockoutSt
SubcontractCt
TotalProduction
Pt
0 0 0 80 0 1,000 0 0
1 0 15 65 0 1,983 0 0 2,583
2 0 0 65 0 1,567 0 0 2,583
3 0 0 65 0 950 0 0 2,583
4 0 0 65 0 0 267 0 2,583
5 0 0 65 0 117 0 0 2,583
6 0 0 65 0 500 0 0 2,583
► Total cost over planning horizon = $422,275► Revenue over planning horizon = 40 16,000 = $640,000► Average seasonal inventory = 895► Average flow time = 895 / 2,667 = 0.34 months
32
Andy Guo
6
1
6
1
6
1
6
1
6
1
6
1
6
1
6
13010525003006640
tt
tt
tt
tt
tt
tt
tt
tt CPSILHOWMin
Regular time labor cost
Overtime labor cost
Cost of hiring and layoffs
Cost of inventory and stockout
Cost of materials and subcontracting
Define Objective Function
33
Andy Guo
► Workforce size for each month is based on hiring and layoffs
► Production for each month cannot exceed capacity
► Inventory balance for each month
► Overtime limit for each month
Define Constraints
80 where6,...,1fo r 01 WtLHWW tttt
6,...,1fo r 440 tOWP ttt
0 ,0 ,500 ,000,1where
6,...,1for
600
11
6
SSII
tSISDCPI ttttttt
6,...,1fo r 10 tWO tt
34
Andy Guo
Optimal Aggregate Plan for Scenario 1
Periodt
No.Hired
Ht
No. LaidOff Lt
Workforce
Size Wt
OvertimeOt
InventoryIt
StockoutSt
SubcontractCt
TotalProducti
onPt
0 0 0 80 0 1,000 0 0
1 0 15 65 0 1,983 0 0 2,583
2 0 0 65 0 1,567 0 0 2,583
3 0 0 65 0 950 0 0 2,583
4 0 0 65 0 0 267 0 2,583
5 0 0 65 0 117 0 0 2,583
6 0 0 65 0 500 0 0 2,583
► Total cost over planning horizon = $422,275► Revenue over planning horizon = 40 16,000 = $640,000► Average seasonal inventory = 895► Average flow time = 895 / 2,667 = 0.34 months
35
Andy Guo
Scenario 2: Increased Demand Fluctuation
The same overall demand (16,000 units) as scenario 1
Month Demand Forecast
January 1,000
February 3,000
March 3,800
April 4,800
May 2,000
June 1,400
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36
Andy Guo
Optimal Aggregate Plan for Scenario 2
Period
t
No.
Hired
Ht
No. Laid
Off Lt
Workforce
Size Wt
Overtime
Ot
Inventory
It
Stockout
St
Subcontract
Ct
Total
Production
Pt
0 0 0 80 0 1,000 0 0
1 0 15 65 0 2,583 0 0 2,583
2 0 0 65 0 2,167 0 0 2,583
3 0 0 65 0 950 0 0 2,583
4 0 0 65 0 0 1,267 0 2,583
5 0 0 65 0 0 683 0 2,583
6 0 0 65 0 500 0 0 2,583
► Total cost over planning horizon is higher = $432,858
► Average seasonal inventory = 1,075
► Average flow time = 1,075 / 2,667 = 0.40 months
► Inventories and stockouts go up compared with the plan for scenario 1
37
Andy Guo
Optimal Aggregate Plan for Scenario 3
Period
t
No.
Hired
Ht
No. Laid
Off Lt
Workforce
Size Wt
Overtime
Ot
Inventory
It
Stockout
St
Subcontract
Ct
Total
Production
Pt
0 0 0 80 0 1,000 0 0
1 0 23 57 0 1,667 0 0 2,267
2 0 0 57 0 933 0 0 2,267
3 0 0 57 0 0 0 0 2,267
4 0 0 57 0 0 67 1,467 2,267
5 0 0 57 0 0 0 0 2,267
6 0 0 57 0 500 0 433 2,267
► Suppose holding cost is increased from $2 to $6 compared to scenario 1► Total cost over planning horizon is higher = $441,200► Average seasonal inventory = 558► Average flow time = 558 / 2,667 = 0.21 months► Inventories carried is reduced while subcontracted amount is increased
compared with the plan for scenario 1
38
Andy Guo
Handle Forecast Error in Aggregate Plans
► Use safety stock or safety capacity
– Use overtime (safety capacity)
– Carry extra workforce (safety capacity)
– Use subcontractors (safety capacity)
– Build and carry extra inventory (safety inventory)
– Purchase capacity or product from open market (safety capacity)
39
Andy Guo
Outline
►Part I: Aggregate planning
►Part II: Managing predictable variability
40
Andy Guo
Managing Predictable Variability- Managing Supply -
► Managing capacity– Time flexibility from workforce– Use of seasonal workforce– Use of subcontracting– Use of dual facilities – dedicated and flexible– Designing product flexibility into the production
processes
► Managing inventory– Using common components across multiple products– Building inventory of high demand or predictable
demand products
41
Andy Guo
Managing Predictable Variability- Managing Demand -
► Demand can be influenced using pricing and other forms of promotions.
► Four key factors influence the timing of a trade promotion:
► Demand increase from promotion results from three factors:– Market growth– Stealing market share– Forward buying
– Impact of the promotion on demand– Product margins– Cost of holding inventory– Cost of changing capacity
42
Andy Guo
Scenario 4: Aggregate Planning and Promotion at Red Tomato
► Discounting a unit from $40 to $39 results in the period demand’s increasing by 10 percent because of increased consumption or substitution. Further, 20 percent of each of the two following months demand is moved forward.
► Consider the discount offering in off-peak month of January. The demand forecast is shown below:
Month Demand Forecast
January 3,000
February 2,400
March 2,560
April 3,800
May 2,200
June 2,200
43
Andy Guo
Optimal Aggregate Plan for Scenario 4
Period
t
No.
Hired
Ht
No. Laid
Off Lt
Workforce
Size Wt
Overtime
Ot
Inventory
It
Stockout
St
Subcontract
Ct
Total
Production
Pt
0 0 0 80 0 1,000 0 0
1 0 15 65 0 610 0 0 2,610
2 0 0 65 0 820 0 0 2,610
3 0 0 65 0 870 0 0 2,610
4 0 0 65 0 0 320 0 2,610
5 0 0 65 0 90 0 0 2,610
6 0 0 65 0 500 0 0 2,610
► Total cost over planning horizon = $421,915► Revenue over planning horizon = $643,400► Profit over planning horizon = $221,485
44
Andy Guo
Scenario 5: Aggregate Planning and Promotion at Red Tomato
Demand fluctuation has increased relative to the profile in scenario 1.
► Discounting a unit from $40 to $39 results in the period demand’s increasing by 10 percent because of increased consumption or substitution. Further, 20 percent of each of the two following months demand is moved forward.
► Consider the discount offering in peak month of April.
The demand forecast is shown below:
Month Demand Forecast
January 1,600
February 3,000
March 3,200
April 5,060
May 1,760
June 1,760
45
Andy Guo
Optimal Aggregate Plan for Scenario 5
Period
t
No.
Hired
Ht
No. Laid
Off Lt
Workforce
Size Wt
Overtime
Ot
Inventory
It
Stockout
St
Subcontract
Ct
Total
Production
Pt
0 0 0 80 0 1,000 0 0
1 0 14 66 0 2,047 0 0 2,647
2 0 0 66 0 1,693 0 0 2,647
3 0 0 66 0 1,140 0 0 2,647
4 0 0 66 0 0 1,273 0 2,647
5 0 0 66 0 0 387 0 2,647
6 0 0 66 0 500 0 0 2,647
► Total cost over planning horizon = $438,857► Revenue over planning horizon = $650,140► Profit over planning horizon = $211,283
46
Andy Guo
Conclusions based on Scenarios 1, 4 & 5
► A price promotion in January (scenario 4) results in a higher profit than no promotion (scenario 1). A promotion in April (scenario 5) results in a lower profit than no promotion (scenario 1).
► Even though revenues are higher when promotions is offered in April, the increase in operating costs makes it a less profitable option.
► Red Tomato should offer the discount in the off-peak month of January.
► The above conclusions could be different if Red Tomato were in a situation in which most of the demand increase comes from market growth or stealing market share rather than forward buying (see scenarios 6 & 7)
► It is not appropriate for a firm to leave pricing decisions solely in the domain of marketing and aggregate planning solely in the domain of operations. It is crucial that forecasts, pricing, and aggregate planning be coordinated in the supply chain.47
Andy Guo
Scenario 6: Aggregate Planning and Promotion at Red Tomato
► Discounting a unit from $40 to $39 results in the period demand’s increasing by 100 percent because of increased consumption or substitution. Further, 20 percent of each of the two following months demand is moved forward.
► Consider the discount offering in off-peak month of January. The demand forecast is shown below:
Month Demand Forecast
January 4,440
February 2,400
March 2,560
April 3,800
May 2,200
June 2,200
48
Andy Guo
Optimal Aggregate Plan for Scenario 6
Period
t
No.
Hired
Ht
No. Laid
Off Lt
Workforce
Size Wt
Overtime
Ot
Inventory
It
Stockout
St
Subcontract
Ct
Total
Production
Pt
0 0 0 80 0 1,000 0 0
1 0 0 80 0 0 240 0 3,200
2 0 11 69 0 140 0 0 2,780
3 0 0 69 0 360 0 0 2,780
4 0 0 69 0 0 660 0 2,780
5 0 0 69 0 0 80 0 2,780
6 0 0 69 0 500 0 0 2,780
► Total cost over planning horizon = $456,750► Revenue over planning horizon = $699,560► Profit over planning horizon = $242,810
49
Andy Guo
Scenario 7: Aggregate Planning and Promotion at Red Tomato
Demand fluctuation has increased relative to the profile in scenario 1.
► Discounting a unit from $40 to $39 results in the period demand’s increasing by 100 percent because of increased consumption or substitution. Further, 20 percent of each of the two following months demand is moved forward.
► Consider the discount offering in peak month of April. The demand forecast is shown below:
Month Demand Forecast
January 1,600
February 3,000
March 3,200
April 8,480
May 1,760
June 1,760
50
Andy Guo
Optimal Aggregate Plan for Scenario 7
Period
t
No.
Hired
Ht
No. Laid
Off Lt
Workforce
Size Wt
Overtime
Ot
Inventory
It
Stockout
St
Subcontract
Ct
Total
Production
Pt
0 0 0 80 0 1,000 0 0
1 0 0 80 0 2,600 0 0 3,200
2 0 0 80 0 2,800 0 0 3,200
3 0 0 80 0 2,800 0 0 3,200
4 0 0 80 0 0 2,380 100 3,200
5 0 0 80 0 0 940 0 3,200
6 0 0 80 0 500 0 0 3,200
► Total cost over planning horizon = $536,200► Revenue over planning horizon = $783,520► Profit over planning horizon = $247,320► When forward buying is a small part of the increase in demand from
discounting, Red Tomato should offer the discount in the peak demand month of April.
51
Andy Guo
Performance Under Different Scenarios
Regular Price
Promotion Price
Promotion Period
Percent Increase in
Demand
Percent Forward Buy
Profit Average Inventory
$40 $40 NA NA NA $217,725 895
$40 $39 January 20 % 20 % $221,485 523
$40 $39 April 20% 20% $211,283 938
$40 $39 January 100% 20% $242,810 208
$40 $39 April 100% 20% $247,320 1,492
$31 $31 NA NA NA $73,725 895
$31 $30 January 100% 20% $84,410 208
$31 $30 April 100% 20% $69,120 1,492
52
Andy Guo
Conclusions Regarding Promotions
► Pricing and aggregate planning must be done jointly.
► Average inventory increases if a promotion is run during the peak period and decreases if run during off-peak period.
► Promotion during the peak period may decrease overall profitability if a significant fraction of the demand increase results from a forward buy.
► Promotion during the peak period may increase overall profitability if forward buying becomes a smaller fraction of the demand increase.
► As product margin declines, promotion during the peak period becomes less profitable.
53
Andy Guo
Summary of Impact on Promotion Timing
Factor Favored timing
High forward buying Low demand period
High stealing market share High demand period
High growth of market High demand period
High margin High demand period
Low margin Low demand period
High holding costs Low demand period
High cost of changing capacity Low demand period
54
Andy Guo
頁碼 作品 授權條件 作者 /來源
9, 36本作品轉載自Microsoft Office 2007多媒體藝廊,依據Microsoft 服務合約及著作權法第 46 、 50 、 52 、 65條合理使用。
版權聲明
55