ISEN 315
Spring 2011
Dr. Gary Gaukler
Dependent Demand
Effective use of dependent demand inventory
models requires the following
1.
2.
3.
4.
5.
Master production schedule
Bill of material (BOM)
Inventory availability
Purchase orders outstanding
Lead times
Time-phased Product Structure
Must have D and E
completed here so
production can begin
on B
Start production of D
1 week
D
2 weeks to
produce
B
2 weeks
E
A
2 weeks
1 week
E
1 week
2 weeks
G
C
3 weeks
F
1 week
D
|
|
|
1
2
3
|
|
4 in weeks
5
Time
|
|
|
6
7
8
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
Shortcomings of MRP
MRP II
Just-in-time and Lean Production
 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
Waste Reduction
 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
Variability Reduction
 JIT systems require managers to reduce
variability
 Variability is any deviation from the
optimum process
 Less variability = less waste
 Inventory hides variability
Reduce Variability
Inventory level
Process
downtime
Scrap
Setup
time
Quality
problems
Late deliveries
Reduce Variability
Inventory
level
Process
downtime
Scrap
Setup
time
Quality
problems
Late deliveries
Enabling JIT: Pull System
 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
Experiment
Reduce Lot Sizes
Q1 When average order size = 200
average inventory is 100
Inventory
200 –
Q2 When average order size = 100
average inventory is 50
100 –
Time
Reduce Setup Costs
 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
Implications for Manufacturing
Reduced space and inventory
 With reduced space, inventory must be in
very small lots
 Units are always moving because there is
no storage