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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


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.


Key Insight

Independent Demand --- finished products

Dependent Demand --- components

It makes no sense to independently forecast dependent demands.

Lot Sizing: Wagner Whitin

Capacity Constraints


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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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


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)


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


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


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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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)


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


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


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?


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


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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CRP Load Profile

CRP Load Profile

0

100

200

300

400

500

600

1 2 3 4 5 6 7 8

Days

Load


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.


Bottleneck Approach

Many systems focus on the bottleneck (theory of constraints).


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


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


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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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)


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


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


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


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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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


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

Documents

6201MRPII_6