ISEN 315 Spring 2011 Dr. Gary Gaukler

1.Master production schedule 2.Bill of material (BOM) 3.Inventory availability 4.Purchase orders outstanding 5.Lead times Effective use of dependent demand inventory models requires the following Dependent Demand

|||||||||||||||| |||||||||||||||| Time in weeks F 2 weeks 3 weeks 1 week A 2 weeks 1 week D E 2 weeks D G 1 week 2 weeks to produce B C E Start production of D Must have D and E completed here so production can begin on B Time-phased Product Structure

Lot Sizing For MRP Systems Assumptions: Consider only one item Demand known and deterministic Finite horizon No shortages No capacity constraints

Lot Sizing For MRP Systems Problem formulation:

Lot Sizing: Silver-Meal Heuristic In any given period, produce to cover demand in a future period as long as the average cost per period is reduced by doing so Algorithm: 1.Start in period 1. Calculate C(t): average per-period cost if all units for next t periods produced in period 1. 2.Select lowest t such that C(t)<C(t+1): t* 3.Produce enough in period 1 to cover t* 4.Repeat, starting from period t*+1

Silver-Meal Example Assume net requirements are 18, 30, 42, 5, 20 Setup cost for production is $80 Holding cost $2 per unit per period

Production Schedule Changes So far, the MRP examples we discussed were static In reality, we need to update our production plans as time passes, thus MRP plans become dynamic The widest-used technique involves “rolling horizons”:

Production Schedule Changes Using rolling horizons can lead to fluctuations in the production schedule As we include more and updated information (e.g., demand forecasts) in each period, our production schedule can change This is called “system nervousness”

Production Schedule Changes Results of fluctuations: –Planning for capacity utilization becomes difficult Fluctuations become larger when we re-run MRP more often But: re-running MRP is our only way of incorporating more information!

Shortcomings of MRP

MRP II

 Goal of Lean production:  Supply the customer with their exact wants when the customer wants it without waste  Method: JIT  JIT is a philosophy of continuous and forced problem solving  JIT: continual improvement, pull system Just-in-time and Lean Production

 Waste is anything that does not add value from the customer point of view  Storage, inspection, delay, waiting in queues, and defective products do not add value and are 100% waste Waste Reduction

 Faster delivery, reduced work-in-process, and faster throughput all reduce waste  Reduced waste reduces room for errors emphasizing quality  Reduced inventory releases assets for other, productive purposes Waste Reduction

 JIT systems require managers to reduce variability  Variability is any deviation from the optimum process  Less variability = less waste  Inventory hides variability Variability Reduction

Inventory level Process downtime Scrap Setup time Late deliveries Quality problems Reduce Variability

Inventory level Scrap Setup time Late deliveries Quality problems Process downtime Reduce Variability

 A pull system uses signals to request production and delivery from upstream stations  Upstream stations only produce when signaled  System is used within the immediate production process and with suppliers Enabling JIT: Pull System

Experiment

– –Inventory Time Q 2 When average order size = 100 average inventory is 50 Q 1 When average order size = 200 average inventory is 100 Reduce Lot Sizes

 High setup costs encourage large lot sizes  Reducing setup costs reduces lot size and reduces average inventory  Setup time can be reduced through preparation prior to shutdown and changeover Reduce Setup Costs

Reduced space and inventory  With reduced space, inventory must be in very small lots  Units are always moving because there is no storage Implications for Manufacturing