March 25, 2013

Aggregate Planning in the Supply Chain - Review Notes

Chopra and Meindl's book, Supply Chain Management: Strategy, Planning, and Operation, is a comprehensive introduction on supply chain management.

In this chapter, the author only described the general nature of the aggregate planning problem and the details involved in aggregrate planning are to be learned from books in production planning and control or operations planning and control.

Aggregate Planning



The objective of aggregate plan is to satisfy demand in a way that maximizes profit for the firm over the planning horizon. The time period for the aggregate planning  is not sufficient for building a new set of facilities to increase production to meet the increase in demand.  So in some periods, inventory may need to be accumulated.

 Aggregate planning is done for a given supply chain design. This means that capacity of the various facilities in the supply chain are constraints now. There may be scope to do multiple shifts or to stop using multiple shits by recruiting extra manpower or laying off them.  But demand has predictable or predicted variability for period to period in the planning horizon. Also there is a demand variation which cannot be predicted. Aggregate plan is made to get maximize profit from the estimated demand and given supply chain constraints.


The definition of aggregate planning problem



Given the demand forecast for each period in the planning horizon, determine the production level, inventory level and the capacity level (to extent variation is possible like number of shifts, overtime etc.) for each period that maximizes the firm's profit over the planning horizon (Chopra and Meindl).

Data Required for Aggregate Planning



Demand forecast in units for each period in the planning horizon

Cost data:

Labor cost - for regular time and overtime

cost of subcontracting

cost of changing capacity by hiring and firing workforce

Cost of adding or reducing machine capacity

Inventory carrying cost or holding cost

Stockout or backlog cost or backfilling cost


Manhours and machine hours required per unit

Constraints

overtime

layoffs

capital available for inventory financing

stockouts

To get the cost data required for the decision making model, supply chain managers, production managers, and production planners have to design and develop systems in management accounting system to get the past data and have to get the help of executives involved in economic forecasting and various operating activities like purchasing/sub contracting, human resource management etc.


Aggregate Planning Strategies



1. Chase strategy: Capacity is the lever. Capacity is changed as per the demand. Capacity includes both machine capacity and man power capacity.

2. Workforce time flexibility based capacity strategy: Workforce works for more or less time depending on the demand. The machine capacity is not varied. Workforce size is also not varied but the working time is made flexible.

3. Level Strategy: Production levels are kept uniform and inventory is accumulated during slack periods and used during peak demand periods. In this case in some months excess production is there and it is carried as inventory and in some month, some orders will not be fulfilled. It can be used when inventory and backorder costs are relatively low.

Aggregate planning problems can be formulated as linear programming problems and solved. The book has given more explanation for the L.P. formulation of the problem. It also has material on using excel for solving the problem.

Some Suggestions for Effective Aggregate Planning:



Do sensitivity analysis and be flexible with aggregate plans.

Be ready to rerun the aggregate plan when conditions warrant.

As capacity utilization increases more attention is required on capacity planning.


References


Sunil Chopra and Peter Meindl, Supply Chain Management: Strategy, Planning and Operations, Prentice Hall, 2001.

Originally posted at
http://knol.google.com/k/narayana-rao/supply-chain-planning-aggregate-planning/2utb2lsm2k7a/1357#

Updated   29.3.2012, 23.1.2012

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