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1WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Material Requirements Planning (MRP)
Unlike many other approaches and techniques, material
requirements planning works which is its best
recommendation.
Joseph Orlicky , 1974
2WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Assumptions
1. Known deterministic demands.
2. Fixed, known production leadtimes.
3. Infinite capacity.
Idea is to back out demand for components by using leadtimes andbills of material.
3WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Key Insight
Independent Demand --- finished products
Dependent Demand --- components
It makes no sense to independently forecast dependent demands.
Lot Sizing: Wagner Whitin
Capacity Constraints
6WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Where We Have Been
Before 1960s - Scientific Management Techniques (EOQ,ROP)
1960s - Advent of MRP
1970s - APICS MRP II Crusade
1980s and 90s - JIT/TQM Revolution
1990s to present - ERP/MES/APS/???
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7WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Conclusions
Insight:distinction between independent and dependent demands
Advantages:
General approach
Supports planning hierarchy (MRP II)
Problems:
Assumptions --- especially infinite capacity
Cultural factors --- e.g., data accuracy, training, etc.
Focus --- authority delegated to computer
8WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Manufacturing Resource Planning (MRP II)
Sometimes called MRP, in contrast with mrp (little mrp); morerecent implementations are called ERP (Enterprise Resource
Planning).
Extended MRP into:
Master Production Scheduling (MPS)
Rough Cut Capacity Planning (RCCP)
Capacity Requirements Planning (CRP )
Production Activity Control (PAC)
9WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
MRP II Planning Hierarchy
DemandForecast
Aggregate ProductionPlanning
Master ProductionScheduling
Material RequirementsPlanning
JobPool
Job
Release
JobDispatching
Capacity RequirementsPlanning
Rough-cut CapacityPlanning
ResourcePlanning
Routing
Data
InventoryStatus
Bills ofMaterial
MRP II
DemandForecast
Aggregateplanning
Resourceplanning
Long-range planning
Master production schedulingRough-cutcapacity planning
MRPBOM
Inventory statusJob Pool
Capacity requirements
planning
Intermediate-range
planning
Job Release
Job Dispatching
Routing Data
Short-term control
11WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Components
Resour ce plann ing: determines capacity requirements
over the long term
Aggregate planni ng: determines levels of production,
staffing, inventory, overtime, and so on over the long
term
Rough-cut capacity plannin g: provides a quick capacity
check of a few critical resources to ensure feasibility
(uses a bill of resources)
Capacity requir ements plann ing: estimates job completion
times for each process center using fixed lead times
and computes a predicted loading over time
12WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Master Production Scheduling (MPS)
MPS drives MRP
Should be accurate in near term (firm orders)
May be inaccurate in long term (forecasts)
Software supports
forecastin g
order entry
netting against inventory
Frequently establishes a frozen zone in MPS
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13WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Rough Cut Capacity Planning (RCCP)
Quick check on capacity of key resources
Use Bill of Resource (BOR) for each item in MPS
Generates usage of resources by exploding MPS against BOR(offset by leadtimes)
Infeasibilities addressed by altering MPS or adding capacity (e.g.,overtime)
14WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Rough Cut Capacity Planning
Use output from master production scheduler (quantity anddue dates)
Use a bill of resources which has times for each part type oneach resource
Compare with resource time capacity over planning period
15WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Example
MPS
1 2 3 4
A 1000 1000 1000 1000
B 500 500
C 1500 1500 1500 1500
D 600 600
Bill of Resources (min)
Assemble Inspect
A 20 2.0
B 24 2.5
C 22 2.0
D 25 2.4
Resource Capacity
assemble = 1200 hr
inspection = 110 hr
16WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Solution
Week (hr)
1 2 3 4
assemble 1133 1083 1333 883
inspect 107 104 128 83
Capacity infeasibility in week 3!
What can we do to correct this?
17WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Capacity Requirements Planning (CRP)
Uses routing data (work centers and times) for all items
Explodes orders against routing information
Generates usage profile of all work centers
Identifies overload conditions
More detailed than RCCP
No provision for fixing problems
Leadtimesremain fixed despite queueing
18WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Example
Data3 day lead time
400 parts capacity per day
Parts in the system:
400 just released, 500 for 1 day, 300 for 2 days
Days 1 2 3 4 5
Planned Order Releases 300 350 400 350 300
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19WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
CRP Load Profile
CRP Load Profile
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8
Days
Load
20WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Production Activity Control (PAC)
Sometimes called shop floor control
Provides routing/standard time information
Sets planned start times
Can be used for prioritizing/expediting
Can perform input-output control (compare planned with actual
throughput)
Modern term is MES (Manufacturing Execution System), which
represents functions between Planning and Control.
21WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Bottleneck Approach
Many systems focus on the bottleneck (theory of constraints).
22WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Theory of Constraint Steps
Identify the systems constraints
Decide how to exploit the systems constraint
Subordinate everything else to Step 2
Elevate the systems constraint
If a constraint is broken, go to Step 1
Note: This is a bottleneck approach
23WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Goldratt Product Mix Problem(Theory of Constraints Scheduling)
Product P Q
Price
Max Weekly Sales 100 50
Machines A,B,C,DMachines run 2400 min/weekfixed expenses of $5000/week
D
C C
A B
C
B
B
A
D
$5 $20 $20 $20 $20
15
10
15
5
15
5
15
15
10
5
P Q
24WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
A Cost Approach
Unit ProfitProductP : $45
Product Q : $60
Maximum Production of Q : 50 units
Available Capacity for ProducingP
2400 - 10 (50) = 1,900 minutes on WorkcenterA
2400 - 30 (50) = 900 minutes on WorkcenterB
2400 - 5 (50) = 2,150 minutes on WorkcenterC
2400 - 5 (50) = 2,150 minutes on WorkcenterD
Maximum Production ofP: 900/15 =60 units
Net Weekly Profit:$45 60 +$60 50 -$5,000 = $700
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25WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Weekly Load
Resource P Q week week weekA 1500 500 2000 2400 83B 1500 1500 3000 2400 125C 1500 250 1750 2400 73D 1500 250 1750 2400 73
Process Avail. %load per time per load per(Min)
Resource B is Constraint (bottleneck)!26
WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Product Contribution to Constraint
Product P Q
Selling Price ($) 90 100Material Cost ($) 45 40Contribution ($) 45 60Time (resource B in min) 15 30$ per constraint minute 3 2
Produce as much of P as possible (i.e. 100
units of P, which leaves time for 30 units of Q)
27WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Remaining Steps
Make sure and keep resource B busy (since constraint)
Make effort to achieve higher performance of B through things
like setup time reduction, preventive maintenance, etc.
If at any point, another resource becomes constrained, repeatprocess
28WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Changes: in processing times on workcenters B and D.
Data:
Product P Q
Sellin rice $90 $100
Raw Material Cost $45 $40
Max Weekly Sales 100 50
Minutes per unit on orkcenter A 15 10
Minutes per unit on orkcenter B 15 35
Minutes per unit on orkcenter C 15 5
Mi nu tes p er u ni t on orkcenter D 25 14
A Modified Example
29WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
A Bottleneck Approach
Identifying the Bottleneck: Workcenter B, because15 (100) + 10 (50) = 2,000 minutes on workcenter A
15 (100) + 35 (50) = 3,250 minutes on workcenter B
15 (100) + 5 (50) = 1,750 minutes on workcenter C
25 (100) + 14 (50) = 3,200 minutes on workcenter D
Bottleneck at B:$45/15 = $3 per minute spent processingP
$60/35 = $1.71 per minute spent processing Q
Maximum Production ofP: 2400/25 = 96 units
Maximum Production of Q: 0 units
Net Weekly Profit: $4596 -$5,000 = -$680
30WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
A Bottleneck Approach (cont.)
Bottleneck at D:$45/25 = $1.80 per minute spent processingP
$60/14 = $4.29 per minute spent processing Q
Maximum Production ofQ: 2400/35 = 68.57>50, produce 50
Available time on Bottleneck:
2400 - 14(50) = 1,700 minutes on workcenter D
Maximum Production ofP: 1700/25= 68 units
Net Weekly Profit: $4543+$6050-$5000= -$65
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31WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Formulation:
24001425
240051524003515
24001015
:subject to
50006045ax
+
+
+
+
+
QP
QP
QP
QP
QP
XX
XXXX
XX
XX
Solution:
09.36
79.75
$557.9ObjectiveOptimal
*
*
=
=
=
Q
P
X
X
Net Weekly Profit : Round solution down (still feasible) to:
36
75
*
*
=
=
Q
P
X
X
To get $45 75 + $60 36 -$5,000 = $535.
An LP Approach
32WallaceJ. Hopp,MarkL.Spearman,1996,2000 http://www.factory-physics.com
Conclusions
Insight: distinction between independent and dependent demands
Advantages:
General approach
Supports planning hierarchy (MRP II, ERP)
Problems:
Assumptions especially infinite capacity
Cultural factors e.g., data accuracy, training, etc.
Focus authority delegated to computer
Recommended