50859335 production planning and scheduling
TRANSCRIPT
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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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cmpm A00267ppmmJun93E - 34 -
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
SATURN: BUSINESS PROCESS
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
SATURN: BUSINESS PROCESS
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
SATURN: BUSINESS PROCESS
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
SATURN: BUSINESS PROCESS
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
SATURN: BUSINESS PROCESS
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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
SATURN: BUSINESS PROCESS
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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