Manufacturing Systems Research

© C.Hicks, University of Newcastle

Manufacturing Systems Research

Chris Hicks


Computer Aided Production Management Systems in

Engineer to Order Companies

• ACME grant in collaboration with NEI

Objectives• Identify the characteristics of

companies in ETO/MTO sector• Evaluate the status of CAPM• Identify common CAPM problems• Develop methods for modelling CAPM

systems in ETO/MTO environments• Apply modelling methods to identify

solutions to CAPM problems


Identification of Company Characteristics and CAPM

problems

• Nine one day visits• Three long visits (3 days - 1 month)• Developed semi-structured “audit”

methodology• Developed methods for company

classification


Audit

• Markets• Products• Processes• Manufacturing Systems• CAPM Systems


Company Classification

Shallow Deep

Product Structure

Jobbing

Batch

FlowMan

ufac

turin

g P

roce

ss

Main product

Spares

Subcontract

Company Type “A”


Company Type “B”

Shallow Deep

Product Structure

Jobbing

Batch

FlowMan

ufac

turin

g P

roce

ss

Valves & PumpsMotorsCabs


Control Approaches

Shallow Deep

Product Structure

Jobbing

Batch

FlowMan

ufac

turin

g P

roce

ss ProjectManagement

MRP

MRP+ JIT

JIT


Audit Conclusions

• Markets - demand highly variable and lumpy

• Products - complex, highly customised, mix of products

• Processes - wide range yet all areas tend to be controlled in same way

• Manufacturing systems - mainly functional layouts, high capital employed

• CAPM systems - poor integration, wide variety of subsystems, incorrect data structures, poor operational procedures, generally unsuccessful


Systems Modelling

• Functional models decompose complex systems using a hierarchical top-down approach. They provide a means of understanding activities and interrelationships

• Information models enable structure of information to be described

• Dynamic models show changing behaviour over time.


Company X - Context Diagram

Company X

ITT

Tender

ContractAwarded

aCustomer

aCustomer

ProgressReport

bSupplier

bSupplier

QuoteITT Order


D ata F low D iagram - H igh Leve l

Tendering1

P repareTender

ITT

C ustom er

Tender

D1 ITT & Tender

ITT (copy)

Engineering2

D es ign fo rTende r

ITT (copy)

D2 Supplier Deta ils

Suppliers& Costing

Q uote

Q uality3

P repareC Q A R

ITT (copy)

CQ AR

Designs, TPS, PPRecom m end Suppliers

Projects5

Plan & CoordinateProject

ContractF ile

Engineering6

Conceptual &Detailed Design

ContractF ile (copy)

7 Purchasing

Supplier Selection,O rdering &Expediting



ProgressReport

ProjectPlan

ProgressReport

ProgressReport

ProgressReport

Drawings,M anuals

DrawingsDrawings

M3 Contract F ile

D/M4 Client Corresp

Approv eP.O .

8 Q uality

ITP & SupplierApproval

ITP

Update

S upplie r

S upp lie r

Q uote

SupplierApprov al

PurchaseO rder Expedite

Inspectionreport

SupplierApprov al

M5 Pref. Suppliers

Supplier

M6 Historic Designs

Designs

M5 Pref. Suppliers

M6 Historic Designs

Supplier

Designs

M/D7 Suppliers

Supplier

D8 Prev ious Suppliers

ContractAwarded

G en. M anager4

A pprova l O fTende r Tender

G.M

B R R

9ProjectReport

Action

Q uote

New DesignsSupplier

PurchaseO rder(copy)


D ata F low D iagram - Low Leve l :Supp lie r Se lection , O rdering & Exped iting

Purchasing7.1

G enera te B illo f M a te ria ls

D8 Prev ious Suppliers

P ro jects7 . Supplier Selection,Ordering & Expediting

Drawings

Purchasing7.2

S e lec tS upp lie r(s )

Purchasing7.3

O rderC om ponen t

Purchasing7.4

E xped ite

ContractF ile (Copy)

Com ponents(grouped)

Possible Supplier

S upplie r

Q uote

P ro jects

Project P lan(Deliv ery Date)

PurchaseO rder

Project ProgressCheck

E ngineeringApprov eSupplier

Approv eO rder

M/D7 Supplier Literature

NewSupplier

Supplier Selected

PurchaseO rder(Copy)

S upplie rProgress

SupplierDetails

Agreed Deliv eryDate, Location


Method Limitations• Audit, systems analysis and data

modelling provide static “snapshot” views. Longitudinal studies are a series of snapshots. No model of system dynamics.

• At best enable “best practice” or potential solutions to be described and documented.

• Not possible to perform experiments to examine alternative configurations and evaluate them in terms of specified performance criteria


Simulation

• Allows modelling of system dynamics• Very expensive in terms of model

building and computational resources• Validation often a problem• Predominantly used for either small

scale models or rough-cut high level models


Simulation Model CAPM Modules

PP

BOMP

P/A P

MPS

MRP

SFC

FIN

CP

INV

POC

S IM U LA TIO N

C A P M M odu les

E xis ting

D eve lop ing


PRODUCT DATA

PRODUCT STRUCTURECurrent operationCurrent operation typeCurrent valueActual operation start timesQueue times before each operationActual set up timesActual processing timesQueue times after each operationActual transfer time

PRODUCT STRUCTURE

Product structure codesProduct instance codes

PRODUCT DATA

PART CODEPart codeNameComponent codesComponent quantities

RESOURCE DATAShift patternAudit periodData update periodAvailabilityDispatching ruleOptional resource ruleBatch splitting ruleBatch sizesTransfer deviceMinimum set up timeMinimum processing timeMinimum transfer timeStochastic distributionEfficiencyOverhead cost per hourCost per hour

MINOR RESOURCE DATAMinor resource codeLocationQuantityCost per hourMinimum use time

SHIFT DATAShift numberDaily work pattern

PRODUCT STRUCTURE

Product familyOriginal due timePlanned due timePlanned operation start times

PART CODERoutingOperation typesMinor resources usedMinor resource quantitiesWhen minor resources usedPlanned set up timesPlanned operation timesPlanned transfer timesLead timeLot sizeLot sizing ruleSafety stockSafety lead timePlanned product structure

PRODUCT DATA

MASTER PRODUCTION SCHEDULEMPS Item NumberPart codeQuantity

DEFAULT DATAMinimum set up timeMinimum processing timeMinimum transfer timeMinimum queue before operationMinimum queue after operation

STATIC DATA

RESOURCE DATAResource codeNameOptional resourceLocation (x,y)Rectangular size (w,h)CoordinatesCompany reference

OPERATIONAL DATA

RESOURCE DATAResource statusWork in progress (non added value)Work in progress (added value)Queue lengthMaximum queue lengthOperations per familySum set up timeSum processing timeLast partBatch counter

CONDITIONAL DATA

Refers to

Static data - time invarientOperational data - specified configurationConditional data - dynamic stochastic data


Key Features

• Large scale model allows whole manufacturing facilities to be represented

• Models facilities, products, processes and planning and control systems

• Many product families can be represented with shallow, medium or deep product structure

• Data structures match ETO/MTO requirements

• Allows variety of planning and control methods to meet local requirements

• May be used as a research tool or for planning and simulation.


Simulation Case Study

• Heavy Machine Shop used for case study

• Static configuration used layout and other resource constraint information

• BOM information obtained for products• Process data and planning data

obtained for 18 month period.


Experimental Design

• Series of full factorial experiments

Factors• Process - assembly lead time,

minimum set-up, machining and transfer

• CAPM, scheduling methods, BOMs, dispatching rules, capacity planning

• Product mix / load • Operational - data update period


Conclusions• Assembly planning and capacity

planning important CAPM subsystems• “Dispatching” rules not very significant• Manufacturing performance sensitive

to transfer times.• Significant advantages gained through

having close control of key resources• Real time data recording led to

improved manufacturing performance


Consequent research topics

• Manufacturing Layout (Hinrichs, Wall)• Capacity planning (Tay, Holmes,

Hines, Pongcharoen) • Assembly planning (Sullivan ..)• Planning of product development

activities• Planning under uncertainty (Wall,

Brand, Song)• Integration of project planning methods

with MRP type approaches (EPSRC proposal)

• Plan Optimisation through Genetic Algorithms


Manufacturing Layout

• All data required for layout analysis, clustering and generation in simulation data structures

• Work started with Chris Lee who was interested in improving the layout of Vickers’ Scotswood Road factory

• Much work in layout has focused on moving from functional layout to cellular layouts


Methods

• Clustering– Matrix based methods– Similarity coefficient methods

• Layout generation– Starting with some candidate

solution generate new layout that minimises (maximises) some objective function

– Simulated annealing– Genetic algorithms


Capacity Planning

• Finite / infinite loading• Re-planning rules (Tay)• Finite loading rules (Holmes)• Interactive tools (Hines, Poncharoen)• Schedule “optimisation” (Poncharoen)


Supply Chain Management

• ETO companies moving towards buy rather than make

• Business process analysis approach (McGovern, Earl, Harrison, Hamilton)

• Agent based modelling of supply chains (Harvey, McLeay, Hines)


IT Implementation

• Embodies audit, business process analysis and requirements definition

• Transfer of computing expertise• 3 Teaching Company Schemes


Layout design & effect on benchmarks

• Tony Wells Siemens Semiconductors• Data from North Tyneside, US,

Germany, Taiwan.• Pareto analysis of costs• Identification of cost drivers• Relating cost drivers to plant design

configurations• Results so far: potential cost reduction

of 50% on £30m/annum -pity the plant has closed!


Management of Knowledge

• Data modelling / systems analysis based upon thematic knowledge that is formal, explicit and easily shared

• Knowledge management requires knowledge of product and process structure

• “Tacit” or embedded knowledge that is disorganised, informal, context dependent and relatively inaccessible often important.

• Interested in developing methodologies for systems integration that include both thematic and tacit knowledge

• LABS Proposal with Paul Braiden


Other Activities

• £180k EDF grant with ISRU to develop distance learning material (Dave Stewardson & Mark Gary)

• TCS with House of Hardy aimed at improving manufacturing efficiency (Rob Davidson & Paul Braiden)


Summary• Wide portfolio of manufacturing

systems research• Various types of research undertaken

from the theoretical through to applied.• “Market led” rather than “product led”

Documents

Manufacturing Systems Research