ISEN 315Spring 2011
Dr. Gary Gaukler
1. Master production schedule2. Bill of material (BOM)3. Inventory availability4. Purchase orders outstanding5. Lead times
Effective use of dependent demand inventory models requires the following
Dependent Demand
| | | | | | | |
1 2 3 4 5 6 7 8Time in weeks
F
2 weeks
3 weeks
1 week
A
2 weeks
1 weekD
E
2 weeks
D
G
1 week
1 week
2 weeks to produce
B
C
E
Start production of DMust 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, 20Setup cost for production is $80Holding 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
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 downtimeScrap
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
200 –
100 –
Inve
ntor
y
Time
Q2 When average order size = 100average inventory is 50
Q1 When average order size = 200average 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