Intermediate-range capacity planning

Usually covers a period of 12 months.

Shortrange

Intermediate range

Long range

Now 2 months 1 Year

Aggregate Planning

• Long-range plans – Long term capacity– Location / layout

• Intermediate plans (Aggregate Planning)– Manpower Utilization regular time, overtime– Outsourcing Buying from a third party– Inventory carrying product for latter periods– Backlog satisfying the demand of the earlier periods– Hiring and layoff

• Short-range plans (Scheduling)– Job assignments – Machine loading

Overview of Planning Levels

Aggregate planning is a big picture approach to production planning.

It is a production plan to meet the demand throughout the year.

It is not concerned with individual products, but with a single aggregate product representing all products.

For example, in a TV manufacturing plant, the aggregate planning does not go into all models and sizes. It only deals with a single representative aggregate TV. Such an aggregate TV may even does not exist in reality.

All models are lump together and represent a single product; hence the term aggregate planning.

Aggregate Planning

Aggregate approach permits planners to develop intermediate-range capacity planning without being involved in too much details.

In aggregate planning we are concerned with the quantity and also timing of demand. Demand is uneven through the year.

Two basic characteristics of aggregate planning1-Aggregate Product2-Uneven Demand

It begins with a forecast of aggregate demand for one year. Then a one year plan is prepared for each month. It includes volume of output, working hours, overtime, outsourcing, inventories, back orders, and hiring and layoffs.

Aggregate Planning

1-Demand

2-Regular time production

3-Overtime production

4-Outsourcing; buying from a third party

5-Inventory; production in one period and sale in one or more later periods.

6-Backlog; production in one period to satisfy the demand of one or more earlier periods.

7-Hiring and layoffs

A number of aggregate plans are examined in terms of feasibility and their costs. The best one is selected.

Aggregate Planning

Aggregate Planning : Summary

The question is how to produce to meet the demand. How many employees, how much overtime, outsourcing, inventories, back orders. Basic aggregate planning strategies areLevel CapacityChase Demand

Demand

Time period (year)

Maintaining a steady rate of output while meeting variations in demand by a combination of options

Level Capacity

Demand

Time period (one year)

Production

Production

Cumulativeproduction

Cumulativedemand

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and

Interesting Observation in Cumulative Graph

1 2 3 4 5 6 7 8 9 10 11 12

Cumulativeproduction

Cumulativedemand

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Interesting Observation in Cumulative Graph

1 2 3 4 5 6 7 8 9 10 11 12

Give the following demand and production.Using a line segment show the maximum inventory?

Matching capacity to demand; production in each period is equal to the expected demand for that period.

Chase Demand

Demand

Time period (year)

andProduction

• Trial and error

• Linear Programming

Techniques for Aggregate Planning

• Determine demand for each period

• Determine capacities (regular time, over time, subcontracting) for each period.

• Identify company’s policies regarding inventories and work force. How much inventory is allowed. What rate of overtime and outsourcing is allowed.

• Determine cost of working regular time and over time work, subcontracting, inventories, back orders.

• Develop alternative plans, compare them and select

General Procedure for Aggregate Planning

Work Sheet

Period 1 2 3 n TotalDemand ForecastProduction Regular Overtime SubcontractProduction-DemandInventory Beginning EndingAverage InventoryBacklogCostsProduction Regular Overtime SubcontractHire/layoffInventoryBack ordersTotal

Back order ( backlog)

Back order cost is cost of satisfying the demand of one period in one or more periods later.

It is cost of loss of goodwill, potential discounts, backtracking, extra paper works of transactions, etc

Back order cost is stated as cost / unit / period ( the same as inventory cost).

Total back order cost per period is (cost / unit / period) × ( total back order in the period)

Aggregate Planning; Assumptions

Aggregate Planning;Trial and Error, 15 workers

Aggregate Planning;Trial and Error; 14 workers

LP Formulation; Supply and Demand

Period 1 2 3 4 5 6 Production1 Regular 3001 Overtime 3001 OutSource 3002 Regular 3002 Overtime 3002 OutSource 3003 Regular 3003 Overtime 3003 OutSource 3004 Regular 3004 Overtime 3004 OutSource 3005 Regular 3005 Overtime 3005 OutSource 3006 Regular 3006 Overtime 3006 OutSource 300

200 200 300 400 500 200 0

LP Formulation; Cost Parameters

Period 1 2 3 4 5 61 Regular 2 3 4 5 6 71 Overtime 3 4 5 6 7 81 OutSource 6 7 8 9 10 112 Regular 7 2 3 4 5 62 Overtime 8 3 4 5 6 72 OutSource 11 6 7 8 9 103 Regular 12 7 2 3 4 53 Overtime 13 8 3 4 5 63 OutSource 16 11 6 7 8 94 Regular 17 12 7 2 3 44 Overtime 18 13 8 3 4 54 OutSource 21 16 11 6 7 85 Regular 22 17 12 7 2 35 Overtime 23 18 13 8 3 45 OutSource 26 21 16 11 6 76 Regular 27 22 17 12 7 26 Overtime 28 23 18 13 8 36 OutSource 31 26 21 16 11 6

Period 1 2 3 4 5 6 Production1 Regular 0 <= 3001 Overtime 0 <= 3001 OutSource 0 <= 3002 Regular 0 <= 3002 Overtime 0 <= 3002 OutSource 0 <= 3003 Regular 0 <= 3003 Overtime 0 <= 3003 OutSource 0 <= 3004 Regular 0 <= 3004 Overtime 0 <= 3004 OutSource 0 <= 3005 Regular 0 <= 3005 Overtime 0 <= 3005 OutSource 0 <= 3006 Regular 0 <= 3006 Overtime 0 <= 3006 OutSource 0 <= 300

0 0 0 0 0 0= = = = = =

200 200 300 400 500 200 0

LP Formulation; Sumproduct and Constraints

LP Formulation; Optimal Solution

Period 1 2 3 4 5 6 Production1 Regular 200 0 0 0 0 0 200 3001 Overtime 0 0 0 0 0 0 0 3001 OutSource 0 0 0 0 0 0 0 3002 Regular 0 200 0 0 0 0 200 3002 Overtime 0 0 0 0 0 0 0 3002 OutSource 0 0 0 0 0 0 0 3003 Regular 0 0 300 0 0 0 300 3003 Overtime 0 0 0 0 0 0 0 3003 OutSource 0 0 0 0 0 0 0 3004 Regular 0 0 0 300 0 0 300 3004 Overtime 0 0 0 100 0 0 100 3004 OutSource 0 0 0 0 0 0 0 3005 Regular 0 0 0 0 300 0 300 3005 Overtime 0 0 0 0 200 0 200 3005 OutSource 0 0 0 0 0 0 0 3006 Regular 0 0 0 0 0 200 200 3006 Overtime 0 0 0 0 0 0 0 3006 OutSource 0 0 0 0 0 0 0 300

200 200 300 400 500 200200 200 300 400 500 200 3900

Disaggregation

Working with aggregate units facilitate intermediate planning.But to put this plan into action we should translate it, decompose it, disaggregate it and state it in terms of actual units of products and for a shorter period

Aggregate planning was for 12 or more months. Now we should break it down into shorter periods, say 2-3 months.Disaggregation; Breaking down the aggregate plan into specific products.

From aggregate product to real specific products.

Based on the specific products, then calculate detail of manpower, material and inventory requirements.

Disaggregation

Master Schedule

The result of disaggregation is a master schedule

Master schedule shows quantity and timing of specific productsIt usually covers 6 to 12 weeks.

After preparing a tentative Master Schedule, a planner can do Rough-cut capacity planning.

Rough-cut capacity planning is to check feasibility of master schedule with respect to available manpower and machinery capacities, storage spaces, and vendor capabilities.

It is just a rough check to ensure that the master schedule is achievable.

The master schedule then is used as the basis for short term planning.

Master Schedule

Aggregate plan: 12 months.

Master schedule: 12 weeks.

Master schedule is updated every 2 week.

Therefore, it is on a rolling basis, always we have a disaggregated plan for the next 12 weeks.

Master Production Schedule (MPS)

Master schedule states quantity and delivery time of specific products.

It says we need 75 push lawn mower in Jan. But it does not say how do we get it, from production or from inventory.

Master Production Schedule (MPS) is developed based on Master schedule.

MPS: Quantity and timing of planned production.

MPS determines the promised inventory,production requirements,available to promise inventory for each period.

Masterscheduling

Beginning inventory

Forecast

Customer orders

Inputs Outputs

Projected inventory

Master production schedule

Uncommitted inventory(Available to Promise)

Master Scheduling Process

The key idea is: we have forecast, but it turn into actual order when we receive a customer order.

MPS start with preliminary calculation of projected inventory.This reveals when we need production to get additional inventory.

64 1 2 3 4 5 6 7 8Forecast 30 30 30 30 40 40 40 40

Customer Orders (committed) 33 20 10 4 2

Projected on-hand inventory 31 1 -29

JUNE JULY

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

Example; Projected on-hand Inventory

Master Production Scheduling Process

Negative projected on-hand inventory is the signal for production.

Suppose the economic production lot size for this product is 70 units.

Whenever production is called, 70 units are produced.

The negative projected inventory of -29 in period 3 calls for production, 70 units are produced, the projected inventory becomes 41.

The same calculation continues for the whole planning horizon

Example; Projected on-hand Inventory

Example; Projected on-hand Inventory

Available To Promise (ATP)

Now we can determine available to promise at each period. We use a look ahead procedure.Sum booked customer orders week by week up to (not including) the next week of production. This is booked orders.The remaining inventory is ATP.ATP is only calculated for weeks in which there is a MPS quantity andthe first week In this example; weeks 1, 3, 5, 7, 8.

64 June July1 2 3 4 5 6 7 8

Forecast 30 30 30 30 40 40 40 40Customer Orders (committed) 33 20 10 4 2Projected on hand inventory 31 1 41 11 41 1 31 61MPS 70 70 70 70

Available To Promise (ATP); First week

Available to promise in week 1 = Inventory in week 0 + Production in week 1 - Customer Orders at week 1 - Customer Orders at week 2

ATP(1) = I(0)+ P(1) - CO(1) - CO(2)ATP(1) = 64+0 -33 -20 = 11This is an uncommitted inventory. Can be assigned to week 1, week 2, or both.

64 June July1 2 3 4 5 6 7 8

Forecast 30 30 30 30 40 40 40 40Customer Orders (committed) 33 20 10 4 2Projected on hand inventory 31 1 41 11 41 1 31 61MPS 70 70 70 70ATP 11 56 68 70 70

Available To Promise (ATP); Other weeks

For other week, beginning inventory is removed from the formulaATP(3) = P(3)-CO(3)-CO(4) ATP(3)= 70-10-4= 56

64 June July1 2 3 4 5 6 7 8

Forecast 30 30 30 30 40 40 40 40Customer Orders (committed) 33 20 10 4 2Projected on hand inventory 31 1 41 11 41 1 31 61MPS 70 70 70 70ATP 11 56 68 70 70

64 June July1 2 3 4 5 6 7 8

Forecast 30 30 30 30 40 40 40 40Customer Orders (committed) 33 20 10 4 2Projected on hand inventory 31 1 41 11 41 1 31 61MPS 70 70 70 70ATP 11 56 68 70 70

For weeks 7 and 8 no CO, therefore ATP = MPS

Updating ATP

As additional orders are booked, They would be entered into the schedule.

ATP would be updated to reflect new booked orders.

Marketing can use updated ATP amounts to provide realistic delivery dates to customers

Updating MPS

Changing to a master production schedule can be disruptive.Particularly changes in the immediate periods of the schedule

Aggregate Plan is developed for say 1 yearMaster Production Schedule is developed for a period of say 12 weeks.

MPS is updated say every 2 weeks, it is on a rolling basis.

Frozen Firm Full Open

1 2 3 4 5 6 7 8 9 10 11 12

Solved Problem 2 page 567.Solve the problem first then look at the book

Need More Practice?

1. Problem 1 page 565 (Modified)a) Demand 1 2 3 4 5 6 7 8 9

Total190 230 260 280 210 170 160 260 180

1940

There are 20 full time employees, each can produce 10 units per period at the cost of $6 per unit. Therefore the supply of full time workers is as follows

1 2 3 4 5 6 7 8 9Total

200 200 200 200 200 200 200 200 2001800

Inventory carrying cost $5 per unit per periodBacklog cost $10 per unit per periodOvertime cost is $13 per unit. Maximum over time production is 20 units per period a) Formulated the problem as a Linear Programming model. Using

excel and solver find the optimal solution. b) Suppose the backlog cost is $1 instead of $10. Find the new

optimal solution. c) Suppose the backlog cost is $1 instead of $10, and overtime cost

is $9 instead of $13. Find the new optimal solution.

Cost Table, Solution Table

Changing Cells

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1Prod_Over1Prod_Reg2Prod_Over2Prod_Reg3Prod_Over3Prod_Reg4Prod_Over4Prod_Reg5Prod_Over5Prod_Reg6Prod_Over6Prod_Reg7Prod_Over7Prod_Reg8Prod_Over8Prod_Reg9Prod_Over9

RHS

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9 RHSProd_Reg1 <= 200Prod_Over1 <= 20Prod_Reg2 <= 200Prod_Over2 <= 20Prod_Reg3 <= 200Prod_Over3 <= 20Prod_Reg4 <= 200Prod_Over4 <= 20Prod_Reg5 <= 200Prod_Over5 <= 20Prod_Reg6 <= 200Prod_Over6 <= 20Prod_Reg7 <= 200Prod_Over7 <= 20Prod_Reg8 <= 200Prod_Over8 <= 20Prod_Reg9 <= 200Prod_Over9 <= 20

= = = = = = = = =RHS 190 230 260 280 210 170 160 260 180 0

LHS

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1 =SUM(B25:J 25)Prod_Over1Prod_Reg2Prod_Over2Prod_Reg3Prod_Over3Prod_Reg4Prod_Over4Prod_Reg5Prod_Over5Prod_Reg6Prod_Over6Prod_Reg7Prod_Over7Prod_Reg8Prod_Over8Prod_Reg9Prod_Over9

=SUM(B25:B42)

LHS

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9 LHS RHSProd_Reg1 0 <= 200Prod_Over1 0 <= 20Prod_Reg2 0 <= 200Prod_Over2 0 <= 20Prod_Reg3 0 <= 200Prod_Over3 0 <= 20Prod_Reg4 0 <= 200Prod_Over4 0 <= 20Prod_Reg5 0 <= 200Prod_Over5 0 <= 20Prod_Reg6 0 <= 200Prod_Over6 0 <= 20Prod_Reg7 0 <= 200Prod_Over7 0 <= 20Prod_Reg8 0 <= 200Prod_Over8 0 <= 20Prod_Reg9 0 <= 200Prod_Over9 0 <= 20LHS 0 0 0 0 0 0 0 0 0

= = = = = = = = =RHS 190 230 260 280 210 170 160 260 180

Cost Table; Format

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1Prod_Over1Prod_Reg2Prod_Over2Prod_Reg3Prod_Over3Prod_Reg4Prod_Over4Prod_Reg5Prod_Over5Prod_Reg6Prod_Over6Prod_Reg7Prod_Over7Prod_Reg8Prod_Over8Prod_Reg9Prod_Over9

Production Cost; Regular, Overtime

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1 6Prod_Over1 13Prod_Reg2 6Prod_Over2 13Prod_Reg3 6Prod_Over3 13Prod_Reg4 6Prod_Over4 13Prod_Reg5 6Prod_Over5 13Prod_Reg6 6Prod_Over6 13Prod_Reg7 6Prod_Over7 13Prod_Reg8 6Prod_Over8 13Prod_Reg9 6Prod_Over9 13

Inventory Cost

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1 6 11 16 21 26 31 36 41 46Prod_Over1 13 18 23 28 33 38 43 48 53Prod_Reg2 6Prod_Over2 13Prod_Reg3 6Prod_Over3 13Prod_Reg4 6Prod_Over4 13Prod_Reg5 6Prod_Over5 13Prod_Reg6 6Prod_Over6 13Prod_Reg7 6Prod_Over7 13Prod_Reg8 6Prod_Over8 13Prod_Reg9 6Prod_Over9 13

Inventory Cost for All Periods

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1 6 11 16 21 26 31 36 41 46Prod_Over1 13 18 23 28 33 38 43 48 53Prod_Reg2 6 11 16 21 26 31 36 41Prod_Over2 13 18 23 28 33 38 43 48Prod_Reg3 6 11 16 21 26 31 36Prod_Over3 13 18 23 28 33 38 43Prod_Reg4 6 11 16 21 26 31Prod_Over4 13 18 23 28 33 38Prod_Reg5 6 11 16 21 26Prod_Over5 13 18 23 28 33Prod_Reg6 6 11 16 21Prod_Over6 13 18 23 28Prod_Reg7 6 11 16Prod_Over7 13 18 23Prod_Reg8 6 11Prod_Over8 13 18Prod_Reg9 6Prod_Over9 13

Backlog Cost

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1 6 11 16 21 26 31 36 41 46Prod_Over1 13 18 23 28 33 38 43 48 53Prod_Reg2 6 11 16 21 26 31 36 41Prod_Over2 13 18 23 28 33 38 43 48Prod_Reg3 6 11 16 21 26 31 36Prod_Over3 13 18 23 28 33 38 43Prod_Reg4 6 11 16 21 26 31Prod_Over4 13 18 23 28 33 38Prod_Reg5 6 11 16 21 26Prod_Over5 13 18 23 28 33Prod_Reg6 6 11 16 21Prod_Over6 13 18 23 28Prod_Reg7 6 11 16Prod_Over7 13 18 23Prod_Reg8 6 11Prod_Over8 13 18Prod_Reg9 86 76 66 56 46 36 26 16 6Prod_Over9 93 83 73 63 53 43 33 23 13

Backlog Cost for All Periods

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9Prod_Reg1 6 11 16 21 26 31 36 41 46Prod_Over1 13 18 23 28 33 38 43 48 53Prod_Reg2 16 6 11 16 21 26 31 36 41Prod_Over2 23 13 18 23 28 33 38 43 48Prod_Reg3 26 16 6 11 16 21 26 31 36Prod_Over3 33 23 13 18 23 28 33 38 43Prod_Reg4 36 26 16 6 11 16 21 26 31Prod_Over4 43 33 23 13 18 23 28 33 38Prod_Reg5 46 36 26 16 6 11 16 21 26Prod_Over5 53 43 33 23 13 18 23 28 33Prod_Reg6 56 46 36 26 16 6 11 16 21Prod_Over6 63 53 43 33 23 13 18 23 28Prod_Reg7 66 56 46 36 26 16 6 11 16Prod_Over7 73 63 53 43 33 23 13 18 23Prod_Reg8 76 66 56 46 36 26 16 6 11Prod_Over8 83 73 63 53 43 33 23 13 18Prod_Reg9 86 76 66 56 46 36 26 16 6Prod_Over9 93 83 73 63 53 43 33 23 13

Target Cell

Changing Cells

Constraints

Target Cell, Changing Cells, Constraints

Change to Min

Options

Solve

Solve

Dem1 Dem2 Dem3 Dem4 Dem5 Dem6 Dem7 Dem8 Dem9 LHS RHSProd_Reg1 170 30 0 0 0 0 0 0 0 200 <= 200Prod_Over1 20 0 0 0 0 0 0 0 0 20 <= 20Prod_Reg2 0 200 0 0 0 0 0 0 0 200 <= 200Prod_Over2 0 0 20 0 0 0 0 0 0 20 <= 20Prod_Reg3 0 0 200 0 0 0 0 0 0 200 <= 200Prod_Over3 0 0 20 0 0 0 0 0 0 20 <= 20Prod_Reg4 0 0 0 200 0 0 0 0 0 200 <= 200Prod_Over4 0 0 20 0 0 0 0 0 0 20 <= 20Prod_Reg5 0 0 0 0 200 0 0 0 0 200 <= 200Prod_Over5 0 0 0 10 10 0 0 0 0 20 <= 20Prod_Reg6 0 0 0 70 0 130 0 0 0 200 <= 200Prod_Over6 0 0 0 0 0 20 0 0 0 20 <= 20Prod_Reg7 0 0 0 0 0 20 160 20 0 200 <= 200Prod_Over7 0 0 0 0 0 0 0 0 0 0 <= 20Prod_Reg8 0 0 0 0 0 0 0 200 0 200 <= 200Prod_Over8 0 0 0 0 0 0 0 20 0 20 <= 20Prod_Reg9 0 0 0 0 0 0 0 20 180 200 <= 200Prod_Over9 0 0 0 0 0 0 0 0 0 0 <= 20LHS 190 230 260 280 210 170 160 260 180

= = = = = = = = =RHS 190 230 260 280 210 170 160 260 180 15070

Objective Function Value

Sensitivity Report

Sensitivity Report