1. Production Scheduling for the McGuiness & Co. Microbrewery

2. A Production Planning & Control Framework Demand Forecasting Capacity Planning Production Scheduling Material Requirements Planning Sales order Processing Purchasing Production Control Shop-floor data Collection Inventory records Tactical Planning Execution Recording

3. The Production Scheduling Problem Production Scheduling Placed Orders Forecasted Demand Current Inventory Positions Already Initiated Production Master Production Schedule: When & How Much to produce for each product Capacity Consts. Company Policies Economic Considerations Product Charact. Planning Horizon Time unit Capacity Planning

4. Problem Specialization for McGuinness Microbrewery Case Study Capacity Constraints: Number and capacity of fermentors Company Policies: –Product cannot be shelved for more than 2 months –Production in a fermentor can be started at any level of its capacity. Product Characteristics: –Production lead times Economic Considerations: (Unnecessary) Inventories should be minimized (consistent with the Just-In-Time philosophy) Planning Horizon: 6-12 months (based on production lead times, product seasonalities, and product obsolescence) Time unit: 1 week (based on the order of production lead times)

5. Possible Approaches Empirical Approach: Spreadsheet-based Simulation Analytical Approach: Mathematical (Integer) Programming formulation

6. The Driving Logic for the Empirical Approach DemandAvailability: Initial Inventory Position Scheduled Receipts Future inventories Net Requirements Lot Sizing Scheduled Releases Resource (Fermentor) Occupancy Product i Feasibility Testing Master Production Schedule Schedule Infeasibilities Revise Prod. Reqs Compute Future Inventory Positions

7. Example: Implementing the Empirical Approach in Excel

8. Computing Inventory Positions and Net Requirements Net Requirement: NR i = abs(min{0, IP i }) Inventory Position: IP i = max{IP i-1,0}+ SR i +BNR i -D i (Material Balance Equation) i DiDi IP i (IP i-1 ) + SR i +BNR i

9. Problem Decision Variables: Scheduled Releases

10. Testing the Schedule Feasibility

11. Fixing the Original Schedule

12. Infeasible Production Requirements

13. Modeling the Inventory Spoilage

14. A feasible schedule with spoilage effects

15. Computing Spoilage and Modified Inventory Position Spoilage: SP i = max{0, IP i-1 -  SR i-1 +SR i-2 +…+SR i-sl+1 ) -  BNR i-1 +BNR i-2 +…+BNR i-sl+1 )} Inventory Position: IP i = max{IP i-1,0}+ SR i +BNR i -D i -SP i (Material Balance Equation) i DiDi IP i (IP i-1 ) + SR i +BNR i SP i

16. Advantages and Disadvantages of the Empirical Approach Advantages: –Easy to present and motivate –Provides clear visibility to the problems and their underlying causes –Supports effective and efficient “what-if” analysis –Provides modeling flexibility Disadvantages –No guarantee for optimality or exhaustive search for a feasible solution –Hard to trace for more complex production environments