Aggregate Planning - PowerPoint PPT Presentation

PPT – Aggregate Planning PowerPoint presentation | free to view - id: 16e630-ZDc1Z

The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
Title:

Aggregate Planning

Description:

S. Chopra / Demand Planning. 2. 9/19 Agenda. 6:00 7:15: Discuss cases (7-11, Guinness) and Ch. 4 ... 7:30 8:15: Aggregate Production Planning example ... – PowerPoint PPT presentation

Number of Views:40
Avg rating:3.0/5.0
Slides: 34
Provided by: sunilc6
Category:
Tags:
Transcript and Presenter's Notes

Title: Aggregate Planning

1
Aggregate Planning
2
9/19 Agenda
• 600 715 Discuss cases (7-11, Guinness) and
Ch. 4
• 715 730 Crash course on Linear Programming
• 730 815 Aggregate Production Planning
example
• 830 930 Using Excels LP Solver for
Aggregate Planning
• 930 1000 Aggregate Planning
investigation/experimentation
• Due next class (10/1)
• Case readings World Co. Wal-Mart
• Group Project Specialty Packaging Corporation,
Parts AB

3
Linear Programming A Simple Example
Jack makes Xylophones and Yo-yos out of string
and wood (and time). The following table
provides per-unit resource and profit data for X
and Y, and resource availability this week.
4
Linear Programming A Simple Example (cont)
Let X and Y represent the number of xylophones
and yo-yos Jack will make this week. Because of
resource constraints (and common sense), X and Y
must obey
50
Time
String
32
25
Feasible Region
Wood
30
32
60
5
Linear Programming A Simple Example (cont)
All points inside the 5-vertex polygonal feasible
region represent possible production plans for
the week. But profit 2x3y, so the vector
(2,3) points in the direction of more
profitability.
50
Time
String
3
32
25
2
Feasible Region
Wood
30
32
60
6
Linear Programming A Simple Example (cont)
Which point is most profitable? The intersection
of constraints Time and Wood is furthest in the
profitable direction.
50
String
Time
32
25
Feasible Region
Wood
30
32
60
7
Linear Programming A Simple Example (cont)
Formulated as a Linear Program, the whole problem
looks like
• Commercial software exists to solve very large
problems of this form very quickly, and MANY
• Permissible variations include
• Max or Min
• Constraints lt, , or gt
• Free variables (allowed to be negative)
• Other variations can be optimized using other
techniques
• Variables forced to be integer or binary (0,1)
• Nonlinear Objective function and/or constraints
• Constraints satisfied with a specified
probability
• Etc!

8
Aggregate Planning at Red Tomato Tools
9
• Capacity (regular time, over time, subcontract)
• Inventory
• Backlog / lost sales

10
Basic Production Planning Strategies
• Chase strategy
• Manipulate production capacity to meet changes in
demand
• Hire/layoff workforce
• Low inventory/stockout cost
• High workforce/capital cost

11
Basic Production Planning Strategies
• Time flexibility from workforce or capacity
• Maintain high capacity, and manipulate
utilization
• Alternate tasking for workforce/machinery in
periods of low demand
• Low inventory/stockout cost
• High overhead due to lower average utilization
• Need flexible/skilled workforce

12
Basic Production Planning Strategies
• Level Strategy
• Maintain capacity and high utilization
• Manipulate Inventory/Stockout levels to meet
changing demand
• High inventory/stockout cost
• Low operating costs due to efficiency (high
utilization)

13
Aggregate Planning Specify Data
14
Aggregate Planning Define Decision Variables
• Wt Workforce size during month t
• Ht Number of employees hired at the beginning
of month t
• Lt Number of employees laid off at the
beginning of month t
• Pt Production in month t
• It Inventory at the end of month t
• St Number of units stocked out at the end of
month t
• Ct Number of units subcontracted for month t
• Ot Number of overtime hours worked in month t
• All for t 16

15
Aggregate Planning Define Objective Function
16
Aggregate Planning Define Variable
Relationships (Constraints)
• Workforce size for each month is based on hiring
and layoffs

17
Aggregate Planning (Constraints)
• Production for each month cannot exceed capacity

18
Aggregate Planning (Constraints)
• Inventory balance for each month

19
Aggregate Planning (Constraints)
• Overtime for each month

20
Scenarios
• Increase in holding cost (from 2 to 6)
• Overtime cost drops to 4.1 per hour
• Increased demand fluctuation

21
Increased Demand Fluctuation
22
Managing Predictable Variability
• Manage Supply
• Manage capacity
• Time flexibility from workforce (OT and
otherwise)
• Use of seasonal workforce
• Use of subcontracting
• Flexible processes
• Counter cyclical products
• Manage inventory
• Component commonality
• Seasonal inventory of predictable products

23
Managing Predictable Variability
• Manage demand with pricing
• Original pricing Cost 422,275, Revenue
640,000
• Demand increase from discounting
• Market growth
• Stealing market share
• Discount of 1 increases period demand by 10 and
moves 20 of next two months demand forward

24
Off-Peak (January) Discount from 40 to 39
Cost 421,915, Revenue 643,400, Profit
221,485
25
Peak (April) Discount from 40 to 39
Cost 438,857, Revenue 650,140, Profit
211,283
26
Demand Management
• Pricing and Aggregate Planning must be done
jointly
• Factors affecting discount timing
• Product Margin Impact of higher margin (40
• Consumption Changing fraction of increase coming
from forward buy (100 increase in consumption

27
Performance Under Different Scenarios
28
Factors Affecting Promotion Timing
29
Summary of Learning Objectives
• Forecasting
• Aggregate planning
• Supply and demand management during aggregate
planning with predictable demand variation
• Supply management levers
• Demand management levers

30
Factors Influencing Discount Timing
• Impact of discount on consumption
• Impact of discount on forward buy
• Product margin

31
• Leveling capacity forces inventory to build up in
anticipation of seasonal variation in demand
• Carrying low levels of inventory requires
capacity to vary with seasonal variation in
demand or enough capacity to cover peak demand
during season

32
January Discount 100 increase in consumption,
sale price 40 (39)
Off peak discount Cost 456,750, Revenue
699,560
33
Peak (April) Discount 100 increase in
consumption, sale price 40 (39)
Peak discount Cost 536,200, Revenue
783,520