50859335 production planning and scheduling

8/3/2019 50859335 Production Planning and Scheduling

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Production Planning and Scheduling

Group 9

Bipul Megotia

Gaurav Kataria

Jammy Maisnam

Kaavish Kidwai

Neer Prajapati


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Production Planning

Scheduling production

Manufacturing production

Controlling production activities

Scheduling production

Manufacturing production

Controlling production activities


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Dealing with the Problem Complexitythrough Decomposition

Aggregate Planning

Master Production Scheduling

Materials Requirement Planning

Aggregate Unit

Demand

End Item (SKU)

Demand

Corporate Strategy

Capacity and Aggregate Production Plans

SKU-level Production Plans

Manufacturing

and Procurement

lead timesComponent Production lots and due dates

Part process

plans

(Plan. Hor.: 1 year, Time Unit: 1 month)

(Plan. Hor.: a few months, Time Unit: 1 week)

(Plan. Hor.: a few months, Time Unit: 1 week)

Shop floor-level Production Control

(Plan. Hor.: a day or a shift, Time Unit: real-time)


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Aggregate PlanningSolution techniques


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Solution Approaches

Graphical Approaches: Spreadsheet-based simulation

Analytical Approaches: Mathematical (mainly linear

programming) Programming formulations


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A prototype problem

Forecasted demand:Jan: 1280

Feb: 640

Mar: 900

Apr: 1200

May:2000

Jun: 1400

On-hand Inventory:500

Required on-hand

Inventory at end

of June:

600

Current WorkforceLevel: 300

Worker prod.capacity:

0.14653 units/day

Working days per month

Jan: 20

Feb: 24

Mar: 18

Apr: 26

May: 22Jun: 15

Cost structure:

Inv. holding cost: $80/unit x month

Hiring cost: $500/workerFiring cost: $1000/worker


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A prototype problem (cont.)

Net predicted demand:Jan: 780

Feb: 640

Mar: 900

Apr: 1200

May: 2000

Jun: 2000

Forecasted demand:Jan: 1280

Feb: 640

Mar: 900

Apr: 1200

May:2000

Jun: 1400

On-hand Inventory:500

Required on-hand

Inventory at end

of June:

600


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An LP formulation for the prototype problemProblem Parameters

Dt = Forecasted demand for period tdt = working days at period t

c = daily worker capacity

W0=Initial workforce level

I0 = Current on-hand inventory

CH = Hiring cost per worker

CF = Firing cost per workerCI = Inventory holding cost per unit per period

ProblemDecision Variables

Ht = Workers hired at period t

Ft = Workers fired at period t

Wt = Workforce level at period t

Pt = Level of production at period tIt = Inventory at the end of period t


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An LP formulation for the prototype problem

)min(6

1

6

1

6

1

!!!

t

tI

t

tF

t

tHICFCHC

s.t.

6,...,1,1!!

tFHWWtttt

6,...,1,)( !! tWcdPttt

6,...,1,1

!!

tDPIItttt

6006!I

6,...,1,0,,,, !u tIPFHWttttt


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Optimal Plan for the considered example

Fire 27 workers in January

Hire 465 workers in May

Produce at full (labor) capacity every month

Resulting total cost:

$379320.900


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WHY WE NEED PRODUCTION PLANNING

&SCHEDULING


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Problem area

complex production environments plastic, petrochemical, chemical, pharmaceutical industries

several different resources producers, movers, stores

batch/serial processing with time windows

transition patterns (set-up times)

by-products, co-products (re-cycling)

non-ordered production (for store)

alternatives processing routes, production formulas, raw material


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Problem area - example & objectivescomplex production environment

Task

preparing a schedule for a given time period

(not minimising the makespan)

objective

maximising the profit (minimising the cost)

silo

sackswarehous

e

processor B1

processor B2

siloprocessor

A

purchase

order

order


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Constraint Programming (CP)

Declarative problem solving

stating constraints about the problem variables a

set of va

riab

les X={x1,,xn} variables domains Di(usually finite set of possible values)

a set of constraints (constraint is a relation among severalunknowns)

finding a solution satisfying all (most) the constraints systematic search with consistency techniques & constraint

propagation

stochastic and heuristic methods (local search)


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CP - Advantages & Limitations Advantages

declarative modelling transparent representation of real-life problems

easy introduction of heuristics

co-operative solving

integration of solving methods from different areas (OR, AI)

semantic foundation amazingly clean and elegant languages

Weaknesses

NP-hard problems & tractability unpredictable behaviour

model instability


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Planning and Scheduling - Traditional View

Planning finding a sequence of activities

transferring the initial world

into a required state

AI & CP

uses schedulers constraints

(otherwise too tighten or too

relaxed plans)

Scheduling

allocating the activities to

available resources over time

respecting the constraints

OR & CP

all activities are know in

advance

PLANNER SCHEDULER

Plan = a listof activities

Schedule =allocated

activities


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Planning and Scheduling in Industry

not strictly distinguished different discrimination criteria (time horizon & resolution)

marketing planning

what and when should be produced not planning in AI terminology

production planning generation of activities

allocation to departments

production scheduling exact allocation of activities

to machines over time

sometimes new activities introduced


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Separate Planning and Scheduling

Co-operation between planner and scheduler too tighten plans (impossible to schedule)

too free plans (less profitable schedule)

backtrack from the scheduler to the planner

Activity generation

what if appearance of the activity depends on theallocation of other activities?

alternatives

transition patterns (set-ups)

processing of by-products

non-ordered production


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Mixing Planning and Scheduling

A scheduler with planning capabilities generating activities during scheduling

MARKETINGPLANNING

Marketing Plan =

what should be

produced (custom

orders plus expected

stock)

Schedule

- what activities

are necessary to

satisfy the

marketing plan

- how the

activities areallocated to the

resources over

time

P

RODUCTION SCHEDUL

ERACTIVITY

GENERATOR

ACTIVITY

ALLOCATOR

Activity Values for

parameters


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Conceptual models

Expressiveness What could be modelled? (problem area)

What is easy/hard to express? (constraints)

time-line model

discrete time

(time slices with equal duration)

order-centric model

per order (task)

resource-centric model

per resource

grouping activities?

even-based time

(activities)

view of time?


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Constraint classification in scheduling

resource constraints

resource limits in given time point

capacity, compatibility

transition constraints

activity transitions in single resource

set-ups

dependency constraints dependencies between different resources

supplier-consumer relation


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Time-line model a discrete time line with time slices

description of situation at each time point/slice

planning and scheduling - no difference a variable for activity in the description of time point/slice

comments

covers all the typical problems in complex production environments all the variables are known in advance

too many variables in large-scale industrial problems

Production ( item 1) Change- over Production (item 2) Production (item 3)

Storing (item 1)empty Storing (items 1&B)

No production Production (item4) Production (item5)

time

resources

empty

Time slice


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Order-centric model

a chain of activities per order (task)

description of the activity start, end (duration), resource

enhancement activities in the production chain are generated during scheduling

starting from the order (alternatives, set-ups)

sharing activities between production chains (by-products)

time

resources

storing

extruding

storing

storing

storing

extruding

polymerizingpolymerizing


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How to model? (in order-centric model)

alternatives pre-processing (chosen by the planner)

alternative activities in slots

set-ups

set-up slot is either empty or contains the set-up activity (dependingon the allocation of the next activity)

by-products (re-cycling) sharing activities between the production chains

non-ordered production pre-processing (non-ordered production is planned in advance -

before the scheduling)


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Resource-centric model

a sequence of activities per resource what the resource can process rather than how

to satisfy the order

description of the activity start, end (duration), quantities, state, suppliers, consumers

representation a list of virtual activities

transition constraints between successive activities

Prod ucti on ( item1) Chang e-over Production (item 2) Production (item 3)

Storing (item 1)empty Storing (items 1&B)

No production Production (item4) Production (item5)

time

resources

empty

No order Order1 No order


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Comparison of modelsTime-line Order-centric Resource-centric

Resourceconstraints

easy complicated easy

Transitions easy complicated easy

Dependencies easy easy complicated

Non-orderedproduction

implicit no (limited) implicit

Cycling implicit limited implicit

Alternatives implicit limited implicit

DRAWBACKS too manyvariables

limitedcapabilities


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Whats next?

ad-hoc implementation

dynamic constraints

propagation (early detection of inconsistencies)

labelling (incremental)

heuristics (choice of alternatives)

theoretical foundation

structural constraint satisfaction (A. Nareyek) parallelism

agent based scheduling


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Program-Centric Strategic Planning

organizations are ill-prepared for the

competitive demands and lack the discipline

to identify, organize, and execute the

appropriate work in any sustainable way.

Need for an advanced way of planning and

orchestrating strategic direction-setting.

Changes need to be made on the fly. Pressure

to automate business processes.


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R.I.T.A.

Resilient I.T. Architecture - resilience built incapable of rapid change and extensibility

Four basic dimensions

Workthe work performed by the organization Informationthe data needed to perform the

work

Applicationsthe automated systems needed to

manage the data Technologythe computing and communication

devices needed to run the systems


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Uninterrupted Business Design

Mass production to mass customization

Focus on forging relationship with vendors in

order to be able to outsource businessprocesses to meet fluctuating demand.

Most industried required to radically

restructure business before going for

automation.


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A CASE STUDY


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cmpm A00267ppmmJun93E - 33 -

Saturn: Production Planningand Scheduling

Background Business Process Redesign:Process, IT

Implications


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General Motors, Saturns Parent, Is the Worlds LargestIndustrial Corporation and Leading Automobile Producer

Revenues for 1992 were $132.4 billion, and net income was $92 milliona

Automotive Products generated $118.6 billion in sales, but experienced anoperating loss of $3.2 billion (before accounting changes):

- Operating loss comes from North American operations:

International operations earned profit of $1.2 billion in sales of $30.8 billion

- Performance improved over 1991; sales increased 8.6% and losses decreased by 92%

Brands include GMC Truck, Chevrolet, Buick, Cadillac, Oldsmobile, Pontiacand Saturn

GMs subsidiaries have been extremely profitable:

- GMAC, GMs vehicle financing subsidiary: $1.2 billion in profits

- EDS: $600 million in profits

- GM Hughes Electronics: $700 million in profits

SATURN: BACKGROUND

a. Including accounting changes, GM

experienced net loss of $23.4 billion

Despite the poor performance of its parent, Saturn has grown at record

pace, increasing its market share ten-fold from 1990 to 1991.


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GM Has Experienced Erosion of Its Market Share

SATURN: BACKGROUND

GeneralMotorsMarket

Share of U.S.Auto Sales

Year

10%

20%

30%

40%

60%

Source: Business Week, April 20, 1992, p. 32.

50%

19911990198919881987198619851984


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CompetitivePressures

GM Faces Significant Challenges from LeanManufacturers, Both Here and Overseas

SATURN: BACKGROUND

Toyota:

Best-in-Classproduction system:

- Level scheduling

- Lower inventory

- Supplier coordination

Chrysler:

New platforms

New dealerawareness

Ford:

Successful platformteams

Leaner systems

Intense competitiveenvironment for GM

Industryovercapacity

Increased laborcosts

Other Japanesemanufacturers:

Rationalized productionalternatives

Lower inventory

Intense competitiveenvironment for GM

Customerdissatisfactionwith:

Long lead times

Dealer relations


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GMs Eroding Share Is Largely a Result of OutdatedProduction Paradigms and Business Processes

SATURN: BACKGROUND

The buffer of dealers between consumer production and demand

DealerOperations

Supplier

Production runs batched in stamping, body, paint, and trim

Large inventory stocks at each station

10,000 suppliers in 1988 and 5,500 in 1991; all operating in batch mode

Dealer Orders

Customers

DemandInformation

ProductionInformation

DealerInventory

GM

Information Flow:

Parts

Materials Flow:

Supplier Requests

Customers


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GMs Traditional Business Process Suffered from KeyWeaknesses

SATURN: BUSINESS PROCESS

Order process:

- Limited dealer choice of available cars

- Long lead time for a customor new order

Information systems:

- No trading of real order data

- No synchronization with supplier

- Little adherence to ideal sequence

Process and materials issues:

- Dealer buffer- Large parts inventories

Manufacturing:

- Large setups

- Focus on long runs and quotas


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GM Set Up Saturn as an Independent Entity with a NewParadigm


Separate entity

Centralized Greenfieldplant

Goal of one supplier perinput

Direct EDI input tosuppliers

Noncompetitive retailerswith electronic linkup

Supply chain informationsystem

Integrated supplyproduction-distribution

How to provide superiorservice?

How to deliver cars in 12days?

How to develop worker,supplier, and dealerpartnerships?

How to develop systemcapturing world-classlean principles?

How to treat distributionand supplier as integralpart of production chain?


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Integratedsystem withengineering,materials andretailer orders

Ordermanagementsystem andallocationsystem

Saturns IT Systems Play a Critical Part in Integrating ItsSupply Chain


Retailers

Demand Sales

Data

ProductionPlanning

MaterialsM

anagement

Allocations

Deliveries

Information Flow:

MaterialsSchedule

Suppliers


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Important System Characteristics Help State-of-the-ArtForecasting


Recognizing underlying uncertainty

Incorporating an implicit or external model

Historical data and statistical analysis to update parameter and to trackerrors

Sharing of endpoint data

Measuring price effects

Aggregate forecasts with disaggregation

Consensus among players


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Saturns Time Horizon Is Based on a Benchmarking Study


Aggregate plan

10% on model type20% on options

5% on options

Load plant, 5% exteriorcolors

22 weeks

48 weeks

23 weeks

1 week

Planning FlexibilityTime


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Saturns Production Planning Process Is Integrated withDealers, Suppliers, and Major Component Manufacturers

Places custom orders on pipeline

Issues weekly budgets for dealer distribution

Gives 12 weeks for Saturns 300 suppliers to react

Suppliers own parts until car is finished

Handles daily orders and deliveries on preplanned routes and scheduled

dock times

Demands flexible component manufacturing, with focus of efficiency only atbottlenecks



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The Ideal System Pulls Requirements Exactly When Neededbut May Need an Interior Buffer

Saturn and other lean manufacturers recognize need for buffer

Goal, however, is to minimize buffers

Ideally, buffers are at start and finish

Efficiency important only at bottlenecks

Minimize setups



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Saturns Solid Results Are Related to Managing the EntireProduction and Supply Chain for the Benefit of Its Customers

Outstanding dealer satisfaction (second only to Lexus in 1991)

Sixth in owner satisfaction overall; first in its class

Second in sales and third in profit per outlet

Significant reductions in inventory and suppliers

Lower lead times for customers

SATURN: IMPLICATIONS


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A Number of Key Lessons Can Learned from the SaturnExperience

Must build infrastructure for supply chain communication

Customer focus is essential

Importance of information systems for total system

Must focus on eliminating buffers

Importance of demand pull

Must focus on all sources of waste (dealers, sequence, etc.)

Sensitivity to production stability at some level is necessary

Must have supplier synchronization

Distinction of bottlenecks (non-bottlenecks can be more idle)

SATURN: IMPLICATIONS

50859335 production planning and scheduling

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