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SAP APO Overview, Master data, SNP and PPDS planning processes.
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MindTree Consulting Proprietary & Confidential
Page 1
SAP APO Overview
MindTree Consulting Confidential
Page 2 1997 SAP AG APO 2
SAP APO ArchitectureA Solution to Provide An Integrated & Synchronized SC Process
BusinessWarehouse(Reporting)
APOSolversliveliveCacheCache
SC Cockpit
Application Link EnablingModel Generator, Mapping, Connectivity
APO
OLTP
R/3R/3 R/3R/3 LegacyOLTP
Non-R/3OLTP
Non-R/3OLTP
SAP Advanced Planner & Optimizer (APO)
PP/DSSNPDP TP/VS ATP
MindTree Consulting Confidential
Page 3
R/3
SAP APO Application Architecture
Supply Chain Cockpit
ATPATP
DeploymentDeployment
BIW
HistoricalData
KeyPerform.
Indicators
External data
(e.g.POS)
Advanced Forecasting and Demand PlanningAdvanced Forecasting and Demand Planning
Customerorder
ManufacturingExecution
InventoryManagement
ProductionPlanning andScheduling SupplySupply
NetworkNetworkPlanningPlanning
MindTree Consulting Confidential
Page 4
Multiple System Environment
R/34.x
Internet &
EDI
R/33.x
BW
ALE
• infocubes• liveCache
APO
LegacySystemsPlanning
Systems
WebGUIs
SAPGUIs
MindTree Consulting Confidential
Page 5
liveCache
liveCache
Memory-resident data
object processing
Application
Production ProcessModel
Optimized for real-time scheduling and pegging (supply demand, constraints)
Supply ChainNetwork
Representation ofthe extended supply
chain
APO Solvers
Model Generator,Metaheuristics,
Optimizing AlgorithmsHeuristics
Time Series
Optimized forfast
response ATP
-5+20+10
MindTree Consulting Confidential
Page 6
liveCache
• Application– Manages large amounts of data in main memory– Uses both relational and object-oriented functionality
(ADABAS)
• Integration with R/3– Uses SQL Interface
• Standard Transaction Handling– Locks, Rollbacks, Commits
• Advantages (Performance)– Avoids disk I/O– References object via pointers– Stores complex data structures in object oriented data
structures– Recovers quickly in case of system crash
MindTree Consulting Confidential
Page 7
liveCache
Memory-resident data
object processing
Application
liveCache
• A tool – A tool for processing large volumes of data in main memory.
• Main objective = higher performance• Avoid disk I/O• Stores optimized data structures• Scalability
– number of processors within one liveCache– number of liveCaches
MindTree Consulting Confidential
Page 8
ApplicationApplication
Data StorageData Storage
> 1 ms
Application
Data Storage
< 10 µs
liveliveCacheCache
liveCache
• Main objective = higher performance: – Performance critical routines (in C++) are running in address space
of liveliveCache Management System Cache Management System => no heavy data transfer between application and data storage
MindTree Consulting Confidential
Page 9
SD MM PP
DPDP
Sales Order
Planned Order
Production OrderConfirmation
Purchase ReqmtsTransport ReqmtsTransport Orders
On HandIn transitsPurchase ReqmtsPurchase Order
Forecast
Sales Order
liveCacheSupply Plans
liveCache Integration with R/3
MindTree Consulting Confidential
Page 10
Optimizer
Optimizers are used for:– Supply Network Planning (SNP)– Production Planning / Detailed Scheduling (DPS)– Capable to Match (CTM)
Optimizer routines are developed in C++, which increases the speed of the program
Only available on NT and Windows 2000 Communication between the Optimizer routines and APO
through SAP Gateway
MindTree Consulting Confidential
Page 11
SAP APO Integration
R/34.x
R/34.xR/3
4.x
R/34.x
R/34.xR/3
3.x
R/34.xR/3
4.xNonR/3
Production Planning / Detail Scheduling
Supply Network Planning
Demand Planning
ATP
APO
Live Cache®
Supply
Chain
Cockpit
For SAP R/3 instances, SAP provides the Core Interface Facility (CIF) which dramatically simplifies integration to/from APO. Integration with non-R/3 systems is achieved through ALE and SAP provided BAPI’s.:
MindTree Consulting Confidential
Page 12
APO - R/3 Integration
Core Interface Facility (CIF)• CIF is an online transaction that defines active data
channel(s) in R/3 for data transfer between R/3 Systems and APO. It has the following features :– Real Time Interface– Determines Source and Target Systems within Complex
System Environments– Supplies APO with Relevant Master and Transaction Data– Forwards Data Changes (Transaction Data)– Returns Planning Results to SAP R/3
MindTree Consulting Confidential
Page 13
CIF : Core Interface
APO Core Interface (CIF) is the communication layer to be applied to R/3 to enable an exchange of data between R/3 and APO.
APO-CIF is delivered as a plug-in . This is a general product name given by SAP for the R/3 interfaces to the new dimension applications.
MindTree Consulting Confidential
Page 14
APO -> ERPPlanning Results
ATP Results Manufacturing Orders Procurement Orders VMI Sales Orders
APOAPO
APOAPO
APOAPOBWBWERPERP
ERPERP ERPERP
ERPERP
ERP -> APOMaster Data
Locations Products PPMs (BOM+Routing) Characteristics Capacities
Transaction Data
Planned/Production Orders
Sales Orders Purchase Orders Stocks ATP Requests
CIF Functions Architecture
MindTree Consulting Confidential
Page 15
Technical Considerations
• Modules being Implemented• Multiple Parallel Rollouts• Distributed vs. Central Architecture• UNIX vs. NT• Single Client Strategy• Number / Location of Users• Amount of ALE Traffic• Volume of Data
MindTree Consulting Confidential
Page 16
SAP APODemand Planning
MindTree Consulting Confidential
Page 17
Supply Network Planning (SNP)
Demand Planning (DP)
Production Planning (PP)
Detailed Production Scheduling (DPS)
Deployment
Planning horizon
Transportation Plan / Vehicle Sched.
Supply Chain Planning Cycle
MindTree Consulting Confidential
Page 19
Planning Area Functionality
• Defined like an InfoCube• Contains Characteristics and Key Figures• Maps where key figures for the planning area are
stored (InfoCube, Orders in liveCache, Time Series in liveCache)
• Planning Areas can be relevant for DP and SNP at the same time
• Forecast settings are done for planning area• Creation of time series objects
MindTree Consulting Confidential
Page 20
Planning Area Flow
Planning objectsPlanning objects Key FiguresKey Figures
Sales Production Stock
Assign Key Figuresby Aggregate
Brand
Customer
Brand
Product
Customer
Sales
Production
Sales
Production
Stock
Aggregate
Details
APO Planning Version
Planning UOM
Time Bucket Profile
…
MindTree Consulting Confidential
Page 21
Consistent Planning
DisaggregationProportional values
generated
Pro Rataor
Proportional Factors
Aggre
gatio
n
Lowest Level
PlanningPlanning Level
MindTree Consulting Confidential
Page 22
Statistical Toolbox
• Univariate Forecasting– Moving Average– Simple Linear Regression– Exponential Smoothing – Holt-Winters – Croston’s Model (for sporadic demand)
• Causal Analysis– Multiple Linear Regression
• Composite Forecasting– Weighted Averaging of Multiple Models
(Ex. Constant, Trend, Seasonal, MLR)
MindTree Consulting Confidential
Page 23
Master Forecast profile
Univariate profile
MLR profile
Composite profile
Forecast Profiles
Profiles:• Assign a Planning Area• Define which key figure you
want to be forecasted• Define past and future
periods• Specify models to be used
for:
– Univariate forecast– Multiple linear regression– Composite forecast
MindTree Consulting Proprietary & Confidential
Page 24
SAP APO Overview:Supply Network Planning
MindTree Consulting Confidential
Page 25
Increase Customer Responsiveness at Least Cost
Supply Chain OptimizationSupply Chain Optimization
ManufacturingManufacturing DistributionDistributionSupplierSupplier
INFORMATION FLOW
Retail OutletRetail Outlet ConsumerConsumer
CASH FLOW
Transfer Transfer Transfer Transfer
The Supply Chain: Original Supply to Final Consumption
MindTree Consulting Confidential
Page 26
• A planning approach to create Tactical Plans and Sourcing Decisions that takes the complete supply network into consideration
DeploymentDeployment TransportTransportLoad BuilderLoad Builder
Supply Network Planning
• Meet Forecast and Actual Demand by:– Optimal use of Manufacturing, Distribution and
Transportation Resources– Consider all constraints in the supply chain
What is Supply Network Planning ?
MindTree Consulting Confidential
Page 27
SNP Planning Functionality
• Supply Network Planning Strategies– Heuristics– Optimization– Capable to Match– Propagation
• Deployment– Fair share, push rules and deploy to order– Optimization
• Transport Load Builder– Leveling in transport loading
SNP
Deploy-ment
TLB
PP/DS
MindTree Consulting Confidential
Page 28
APO Planning Functionality Sequence
Supply Network Planning (SNP)
Demand Planning (DP)
Production Planning (PP)
Detailed Production Scheduling (DPS)
Deployment
Planning HorizonPlanning Horizon
Transport Load Builder (TLB)
Transportation Planning and VehicleScheduling (TPVS)
MindTree Consulting Confidential
Page 29
Planning Area Administration
Planning Method Determination & Profile Settings
Supply NetworkPlanning RunTransport Load Building
Deployment Run
Conversion intoPP/DS orders
PP/DS Planning
Release of SNPPlan to DP
Interactive Planning
APO Master Data Setup
Model/Version Creation
Supply Chain Model Setup
Release of Demand Plan to SNP
Simulation
Management by Exception
SNP Process Flow
MindTree Consulting Confidential
Page 30
Examples : SNP Business uses
• Beyond day-to-day planning• High level capacity planning and macro global scheming
to aid with capital equipment decision making based on sales runs.
• Support system for modeling “what if” scenarios that would impact the bottom line.
• Visibility for planning with confidence. System provides cost- trend analysis for control on manufacturing expansion, Out-sourcing, 3rd party supply, etc.
MindTree Consulting Confidential
Page 31
Beyond Traditional “DRP”
Supplier WH
ManufacurerDC
Customer DC
Production Process Model
Manufacturer Plant
Supplier Plant
MindTree Consulting Confidential
Page 32
CustomerDemands
DistributionCenters
Plants
Transport Order Planned Order / Procurement
Suppliers
SNP Distribution Network
MindTree Consulting Confidential
Page 33
Objects in the Supply Chain Network
Objects include:
Locations
Products
Resources
Production Process Models
Transportation Lanes provide links between objects in a supply chain model
MindTree Consulting Confidential
Page 34
Vendor Managed Inventory
MANUFACTURER RETAILER
Plant Warehouse DistributionCenter
SNP
Inventory(integrated)
Forecast(integrated)
Sales order
Inventory(EDI 852)
Forecast(EDI 830)
Order (EDI 855)
VMI
APO
R/3,Legacy
MindTree Consulting Confidential
Page 35
Planning Area Administration
Planning Method Determination & Profile Settings
Supply NetworkPlanning Run
Transport Load Building
Deployment Run
Conversion intoPP/DS orders
PP/DS Planning
Release of SNPPlan to DP
Interactive Planning
APO Master Data Setup
Model/Version Creation
Supply Chain Model Setup
Release of Demand Plan to SNP
Simulation
Management by Exception
SNP Process Flow
MindTree Consulting Confidential
Page 36
SNP Planning Area Administration - Process Flow
Create PlanningObject structure
Create Planning Area
Initialise Planning area
CreatePlanning book
CreatePlanning views
Assign users toPlanning book
Characteristics
Key figures
Attributes
Storage bucket profile
Planning Version
Planningbucket profile
MindTree Consulting Confidential
Page 37
Resources
• The resources are used to define – Capacities of equipment, machines, personnel, means of
transport, warehouses
• Resource data are relevant for planning order dates, taking working time and the capacity of the resources into account
• Resource types:– Bucket Resources– Single activity/ Multi-activity resource– Single Mixed and Multi-Mixed Resource
MindTree Consulting Confidential
Page 38
Planning Parameters
• Parameters in the resource that are relevant to scheduling, which the system uses in PP/DS and SNP.
• Used to control in detail how the system schedules orders to resources.
• Examples: Overload, Bottleneck resource, activity overlap periods, etc.
MindTree Consulting Confidential
Page 39
Shift sequences
Quantities / rates
A
A BCD $
Volume Weight Costs
Shift sequences
Breaks Shiftfactors Shifts
Day number
Validity
Time
Capacity Models
MindTree Consulting Confidential
Page 40
Planning Order
Process
Production Process Model (PPM)
• PPM summarizes – Routing– BOM
• PPM supports– Location-dependent (PPM ID) and
location-independent (Plan No.)– Min/max lot sizes for the master material– Operations
• Set of different process steps Bucketed time intervals for each process step
• Resources assigned to production steps– Validity periods
MindTree Consulting Confidential
Page 41
Production Process Model
Operations:Supply materialPre-assemblyFinal assemblyInspection
Activities: Setup Produce Tear down Queue time
Product Relationship Resources
In / Out
Resource consumption
Header: Costs, Lot size range $
Sequence
MindTree Consulting Confidential
Page 42
Forecasts,Forecasts,Customers ordersCustomers orders
Sourcing Sourcing Production &Production &PurchasingPurchasing
Requirements,Requirements,Inventory levelsInventory levels
APO
Liste
Definition
Incoming quotas Production lead times Transportation lead times
Supply Chain ModelSupply Chain Model
40%60%
What is a Quota arrangement ?
MindTree Consulting Confidential
Page 43
Quotas
40%60%30%
70%
Outgoing Quotas
Production Location Distribution Center (DC)
QuotaQuota
??Supplier
60% 40%
Product
Incoming Q
uotas
MindTree Consulting Confidential
Page 44
Planning Process
Forecast
Supply Network Pl.
Demand Planning
Production PlanningDetailed Scheduling
Transport Load Builder
RecommendedTransportOrders
Deployment
TransportOrders
PlannedOrders
Confirmed Transport Orders
Transportation Planning& Vehicle Scheduling
MindTree Consulting Confidential
Page 45
SNP Heuristic
Heuristic is an algorithm that has pre-defined set of parameters and workflow to influence the creation of a solution
The plan is not necessarily feasible
Planner must use capacity leveling to formulate a feasible plan
MindTree Consulting Confidential
Page 46
SNP Run Using Heuristics:
• Heuristic is used as part of a repair-based planning process consisting of the Heuristic, Capacity Leveling, and Deployment
• The Heuristic processes each planning location sequentially and determines sourcing requirements
• Heuristic processing lumps all requirements for a given material in the location into one requirement for the period
• Heuristic determines the valid sources and quantity based on pre-defined percentages for each source, then passes the requirements through the supply chain to calculate a plan
MindTree Consulting Confidential
Page 47
SNP Run Using Heuristics:
• Heuristic plans all distribution requirements for all locations in the distribution network before exploding the BOM and processing dependent demand in the production locations
• System explodes the BOM only when the Multi-level Heuristic run option is chosen
• Scope of the planning run– Multi-level– Network– Location
MindTree Consulting Confidential
Page 48
Factors Considered in the Heuristic Run
• Transportation lanes• Lead Times• Quota arrangements• Lot sizing• Scrap• Component
Availability• PPMs
• Location Products• SNP Demand Profile• SNP Supply Profile• Demand Profile
MindTree Consulting Confidential
Page 49
Heuristic Processing - Results
• Replenishment Plan– list of procurement– production orders– transportation orders
• Results can be viewed in the interactive planning table
• If the Level ID option is used, the Heuristic calculates an intersection of the following entries– Model Version– Products and Locations– Level ID from product-location hierarchy
MindTree Consulting Confidential
Page 50
Capacity Leveling
• Capacity leveling is a function within Interactive Planning
• Enables to smooth production schedule• Manual or using a methods-based approach• Provides opportunity to build up inventory or increase
capacity • Alternatives can be easily analyzed • Re-plan even re-forecast before putting the plan into
production.
MindTree Consulting Confidential
Page 51
Optimization-Based Planning Models
• In constraint-based planning, production processes can be represented as optimization models.
• A production model based on optimization consists of Objective Function(s), Decision Variables, and constraints based on market conditions, physical processes, and resources/capacity.
• These kinds of models are usually called mathematical programs.
MindTree Consulting Confidential
Page 52
Optimization - Components
• Decisions variable are the independent variables of the problem
• The Objective Function is the single benchmark for evaluating all combinations of decisions that satisfy the constraints
• Constraints represent limitations on which decision can be made and how decisions can be made
F(x,y2)=
MindTree Consulting Confidential
Page 53
Optimization of the Network
ForecastsCustomers orders
Sourcing production &purchasing
requirements
Priorities for:demand typesdefined viacosts
Control costsPenalty costs
$
Goal: Minimize costs
Goal: Maximize Profits*
MindTree Consulting Confidential
Page 54
Optimization Methods
• Linear Programming– Continuous Linear Optimization Problems
• Primal Simplex Method• Dual Simplex Method• Interior Point Method
– Discrete Linear Optimization Problems• Mixed Integer Linear Programming
• Prioritization• Decomposition• Vertical Aggregated Planning• Horizontal Aggregated Planning• Discretization
MindTree Consulting Confidential
Page 55
• Decision Variables– Production lot sizes– Transportation lot sizes– Capacity increase
• Objectives– Lateness – Storage costs – Transportation costs – Production costs– Penalty for increasing
capacity– Penalty cost for not
maintaining safety stock*– Penalty cost for late or non
delivery*
Optimization Parameters
• Constraints– Production capacities– Transportation capacities– Handling capacity– Due dates (demands)– Safety stock– Discrete Values
• Production Lot Size• Transportation Lot Size
MindTree Consulting Confidential
Page 56
Optimization Relevant Profiles
• SNP Optimization Profile– Specifies the linear programming method to be used and
the constraints to be considered during the Optimization run
• SNP Cost Profile– Specifies the weight given to different categories of costs
in the objective function
• Optimization Bound Profile*– Specifies the time buckets where the new plan is
constrained by an upper and lower limit on the allowable change
MindTree Consulting Confidential
Page 57
Optimization Total Costs
Total Cost (Sum Total) Source of cost dataProduction PPM
Storage Resource
Production resource expansion Resource
Storage expansion Resource
Penalty cost for safety stock Cost Profile
Transport cost Resource
Transport capacity expansion Resource
Penalty for non-delivery Master data
Handling capacity expansion Resource
Procurement costs Master data
Delay Penalty Master data
MindTree Consulting Confidential
Page 58
SNP Optimization Run Results
• Distribution Plan• Production Plan• SNP Resulting Costs• Alerts
MindTree Consulting Confidential
Page 59
CTM Process
DemandPrioritization
SupplyCategorization
CTM Engine
Phase 1Build CTMapplication model
Phase 2Match supply
to demand
Orders inliveCache
• Constraint-based heuristics to conduct multi-site checks of production/ transportation capabilities
• Supply categorization• Demand prioritization• CTM Engine
– Create CTM application model
– Match supply to demand using the CTM algorithm
CTM Process (Overview)
MindTree Consulting Confidential
Page 60
• Demands– forecasts
(from APO Demand Planning)
– sales orders
• Prioritization based on • order type• customer priority• product priority• due date
• Defined in Sort Profile*
Prioritizeddemands Demands
1.2.3.4.5.6.
7.8.9.
10.11.
Demand Prioritization
MindTree Consulting Confidential
Page 61
CategorizedSuppliesSupplies
excess
normal
target
Supply Categorization
• Supplies include– inventory– purchase orders
• Categorization is based on supply limits
– for each location– for each product
MindTree Consulting Confidential
Page 62
1.2.3.4.5.6.
7.8.9.
10.11.
CTM Results
CategorizedSupplies
PrioritizedDemandsCapable to Match
Multi-site capacity and transportation capability check
MindTree Consulting Confidential
Page 63
Rule Based Capable to Match*
• Allows to influence the supply and demand matching process, depending on a demand’s specific attribute
• Determines the following based on the attributes of individual demands– find the product/location substitutes for the particular
demand with substitution– influence the solution process for a particular demand using
demand-dependent constraints
MindTree Consulting Confidential
Page 64
Sales and Operations Planning: Overview
• Supports interactive SNP Planning– create a feasible plan for the entire supply chain
• Considers resource capacities (Production and Transport)
• Flexible Controlling via quotas, priorities,and cost• Parameters are time dependant• No lot size Planning• No optimization• No Orders
MindTree Consulting Confidential
Page 65
Sales and Operations Planning:Overview
Product
Location x Product
Location x Product x Channel
SNP: Propagation (finite, multi-level)
DP: (dis-) A
ggregation
MindTree Consulting Confidential
Page 66
Sales and Operations Planning:Features• Pre-configured Planning Environment• Bucketed Planning based on Time Series• Aggregated Planning possible• Fully integrated with Demand Planning
– statistical forecasting– promotion planning
• Planning books can be configured to compare real world (OLTP) with tactical plan
MindTree Consulting Confidential
Page 67
Conversion of SNP Orders to PP/DS Orders
• System automatically stores the final supply network plan in liveCache
• Converting SNP Orders to PP/DS orders makes it available for finite scheduling
• PP/DS enables to synchronize production planning with execution
• PP/DS creates a viable plan• Two ways of conversion
– Conversion of SNP Orders in Production Horizon– Conversion of Individual SNP Orders
MindTree Consulting Confidential
Page 68
Collaborative Supply Planning - Overview
• The goal of collaborative supply planning is the exchange of materials requirements at an early stage between manufacturers and suppliers so that all parties involved can adjust their supply and production plans
• Partners can exchange data in two ways: automatically using time series data exchange between SAP
systems manually via a web browser for collaboration between SAP
and non-SAP systems
MindTree Consulting Confidential
Page 69
Collaborative Supply Planning
Business Benefits
Better Transparency
Reduced costs by less inventory
Better Service level
More stability in demand
Easy communication with all related parties
Front End Agreements
Exchange Component
requirements/constr-aint or unconstraint
forecastSupply NetworkPlanning/Production
Planning
Supply NetworkPlanning/Production
Planning
Collaborate on exceptions
Supply NetworkPlanning/Production
Planning
Supply NetworkPlanning/Production
Planning
Business Benefits
Better service level of supplier
More accurate supply
Reduced costs by less inventory
Easy communication with all related parties
Supplier Both Manufacturer
MindTree Consulting Confidential
Page 70
Collaborative Supply Planning - Process Flow
SupplyPlanner
AccountManager
Alertbroadcasting
(email)
Manufacturer Supplier
APO SNP
R/3
plan replan SupplierSystem
Reviewrequirements
Purchase order
Committed supply planData change information (alert,email)
Internet
MindTree Consulting Confidential
Page 71
Planning Process & Time Buckets
Supply Network Planning (SNP)
Demand Planning (DP)
Production Planning (PP)
Detailed Scheduling (DS)
Deployment
Transport Load Builder (TLB)
Year, Quarter, Month
Year, Quarter, Month, Week
Week, Day, Hour, Minute
Month, Week, Day, Hour
Week, Day, Hour, Minute
Quarter, Month, Week, Day
Network Design (ND)
Vehicle Scheduling
MindTree Consulting Confidential
Page 72
Which products should be planned using PP/DS?
• Externally procured products with long replenishment lead time
• All in-house products produced on a bottleneck resource
MindTree Consulting Confidential
Page 73
What PPDS delivers?
• Consistent Model through Production Process Model• Simultaneous Capacity (CRP) and Material (MRP)
Planning• Creation of Feasible Production Plans• Multi-level Forward and Backward Scheduling• Automatic Multi-level Transfer of Changes (e.g.
orders)
MindTree Consulting Confidential
Page 74
Why perform Production Planning/ Detailed Scheduling?
• Improve customer response (due date performance)• Improve throughput• Reduce inventory/Reduce WIP• Reduce overtime expense• Increase asset utilization• Reduce cycle time
MindTree Consulting Confidential
Page 75
How SAP delivers this solution?
• Built for quick response (liveCache)• Designed for a multi-plant heterogeneous
environment• Packaged to support R/3 implementations • Simultaneous material and capacity planning• Constraint solving & optimization engine• Simulation and what-if analysis• Exception driven decision support tool
MindTree Consulting Confidential
Page 76
Differences between SNP and PPDS
SNP• Capacity and product receipts
and requirements are considered in a bucketed fashion
• No material problem if all receipts until a certain bucket equal all requirements until that bucket
• Sequence not relevant• LP-optimizer can:
– generate orders – optimize lot size– best for sourcing problems
PPDS• Both, capacity and
product are checked with exact time.
• It is a potential problem if material receipt is 1 second after requirement.
• Sequence relevant• Optimizer can:
– alter sequence of existing orders
MindTree Consulting Confidential
Page 77
SCC
AlertsQ
ueriesMas
terD
ata
Sourcing,Confirmation
(SC)
HistoricalData
Uncon- strainedDemand
Plans
DP SNP PlannedOrders PP Feasible
Schedule DS
Inventory TransportOrdersATP
FinalProduction
Plan
Deploy-ment
ActualSales
Orders
SC
MindTree Consulting Confidential
Page 78
Total Process Flow: APO
Production Process Model Evaluation / Selection
APO
PPM Explosion
Creating Order Network
Availability check against unassigned receipts
Core
TransferOrders
MRP
PP
OrderStock
Planning Functionality
Order Creation(OLTP, SNP, PP/DS)
MindTree Consulting Confidential
Page 79
PP/DS Setup and Planning Cycle
MindTree Consulting Confidential
Page 80
Features in PP
• Automatic planning– When integrated with R/3 continually, this can provide the
most up-to-date information
• Manual planning– to handle critical products that require particular attention
when planning
• Interactive planning– Planning board feature
• Cross-plant planning– Stock transfer or between parties in a supply chain
MindTree Consulting Confidential
Page 81
Features in PP - (Continued)
• Lot-size calculation– Lot-for-Lot Order Quantity– Fixed Lot Size– Rounding profile
• Scrap calculation– Scrap at assembly level– Scrap at activity level
• Calculation of Days’ Supply• Planning with shelf-life data
MindTree Consulting Confidential
Page 82
Features in PP - (Continued)
• Production Process Model (PPM) explosion• Pegging (Fixed and Dynamic)• Evaluation Options• Execution Functions in APO and R/3
MindTree Consulting Confidential
Page 83
Pegging
• Pegging network:– Used for
supply/demand allocation
– Changes have to be propagated to all dependent orders
– Find unassigned order quantity
• APO offers fixed and dynamic pegging– Global setting in
APO
50 50
80 20Pegging edge
Input nodeOutput node
Demand
Order
50 2030
MindTree Consulting Proprietary & Confidential
Page 84
Strategy Profile& Scheduling Modes
MindTree Consulting Confidential
Page 85
• Infinite Scheduling– Schedule an operation without considering resource
capacity
• Finite Scheduling – Schedule an operation only when resource capacity is
available– Alert is only generated when planning using finite
scheduling only
Scheduling Strategy Profile
FiniteFinite StrategyStrategy
InfiniteInfiniteStrategyStrategy
Finite SchedulingFinite SchedulingFinite scheduling
Alert displayInfinite scheduling Alert display
Infinite SchedulingInfinite Scheduling No Alert display No Alert displayInfinite scheduling Infinite scheduling
MindTree Consulting Confidential
Page 86
Scheduling Modes
• Insert operation to close gaps in schedule
• Squeeze-in operation
• Add an operation at end
• Dispatch to non-working time
• Infinite loading• Finite loading only
forward • Finite loading only
backwards • Finite loading with
direction switch• Search for a slot in
schedule
MindTree Consulting Confidential
Page 87
Finite Scheduling - Backward Only
t
Resource 1
Resource 2
Resource 3
Available
Occupied
Customer OrderDesired dateand quantity
New Order
today confirm
MindTree Consulting Confidential
Page 88
Finite Scheduling - Forward Only
t
Resource 1
Resource 2
Resource 3
Available
Occupied
Confirmed dateand quantitytoday
Customer OrderDesired dateand quantity
New Order
MindTree Consulting Confidential
Page 89
Finite Scheduling - Backward with Reverse
t
Resource 1
Resource 2
Resource 3
Available
Occupied Confirmed dateand quantity
Final loading
today
Customer OrderDesired dateand quantity
1st Loading attempt
MindTree Consulting Confidential
Page 90
Simultaneous Material and Resource Planning
Due Date
1st loadingInfinite planning strategy
today
Material 1 Delivery time
t
Available
Occupied
Resource 1
Resource 2 (bottleneck)
Resource 3 ALERTS:Resource OverloadSupplier Delivery Time Violated
MindTree Consulting Confidential
Page 91
today
Material 1 Delivery time
t
Resource 1 (finite scheduling)
Resource 2 (bottleneck)
Resource 3
Available
Occupied
1st loadingInfinite planning strategy Due Date2nd LoadingFinite planning strategy Feasible Due Date
Simultaneous Material and Resource Planning
MindTree Consulting Confidential
Page 92
Features in Detailed Scheduling
• Determine production date by taking account of the constraints entered in the strategy profile and in the resources for scheduling
• Changes in DS (e.g. orders, operations, …) will automatically propagate through to all relevant objects
• Taking sequence-dependent set-up times and/or costs into consideration
• If a constraint is relaxed (this is not considered in optimization run) and it is violated in scheduling, the system creates alerts
MindTree Consulting Confidential
Page 93
Real Time Planning vs Batch Optimization
Real Time Planning– supports interactive scheduling
– finds a feasible solution
– real time answering
– Examples • backward/forward propagation
• scheduling in free slots
• simultaneous material and capacity planning
Batch Optimization– takes into account
complete situations
– optimizes feasible solutions
– answering time depends on user-defined run time
– Examples • complete rescheduling
of planning window
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Resource 6
Resource 3
Resource 5
Resource 4
Resource 2
Resource 1
Concept: Optimization
Order 1 Order 4Order 3Order 2
time
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Resource 6
Resource 3
Resource 5
Resource 4
Resource 2
Resource 1
Concept: Optimization
Order 4Order 3 Order 1 Order 2
time
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Foundation of Optimizer in DS: Metaheuristics
• Objective– obtain quality solution for given time frame
(scalability for a given problem size)• Purpose
– simplify the problem• Metaheuristics consists of
– time decomposition– resource decomposition– constraint relaxation
• Local improvement Strategy– Focus on a sub-problem and optimize
planning window
Objects (resources, orders, ops, constraints, …)
Time
Reducedopt model
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Metaheuristics - Time Decomposition
Resources
TimeCurrent window
Sliding window (Rolling time Horizon)1. Optimize only in window2. Move window by a time delta3. Go to first step
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Metaheuristics - Bottleneck Decomposition
Bottleneck1. Determine bottleneck2. Schedule bottleneck resources only3. Fix sequence on bottleneck resource4. Schedule all resources
Time
Resources
Bottleneck
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• A way to make a constraint less restrictive or remove certain constraints
• 5 choices– Remove max time constraint– Set resource utilization rate to 100%– Remove calendar– Do not consider sequence-dependent setup– Undo fixing of orders/operations/activities
Metaheuristics - Constraint Relaxation
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Non-work timesFixed operationsOperations, that may be rescheduledRelationshipsOptimization range Transferred Resources B, C, D
Start End
Scope and Size of Optimization: An Example
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Optimization Model in PPDS: Scheduling
• Decision Variables– Resource Allocation
• Alternative Machines• Alternative Storage
– Start dates– End dates
• Constraints– Time Constraints
• Maximal (Shelf Life)• Minimal
– Deadlines– Production and Storage
Capacities– Calendar (Shifts and Breaks)– Sequence- and Resource-
dependent Setup times– Resource Network– Breakable activities– Effectivity of BOM’s and
Routings– Productivity (per Shift)
• Objective Functions (Minimize)– Total Lateness – Maximum Lateness– Total Leadtime– Total Setup Times– Total Setup Costs
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Two Ways to Perform DS
• Optimization Procedures
– Constraint Programming• Constraint propagation• Branch and bound
– Genetic Algorithm• Priority rules• Sequencing
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Constraint-Based Programming
• Approach/method– checks hard constraints during scheduling– propagates constraints during scheduling
• additional reduction of the search space• early detection of dead ends • reduces back tracking
• Tradeoff– Dynamic propagation needs time– but improves quality of search decisions
• Advantages– High-performance constraint propagator (iLog)– Dedicated to complex scheduling problems
• Example: shelf life / expiration
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Constraint-based Programming: Example
Initial Solution
Solution with Changed Variables
• Propagate consequence of each decision– Dynamic constraint
propagation– checks hard constraints
during scheduling• Prune the search tree
– Remove unfeasible solution
– Remove worse solution
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• Based on Concept of Evolution• Population of candidate solutions• New candidate solutions by
– Crossover/Recombination– Mutation
• Exchanging sequence of activities• Change resource allocation of activities
• Preferring the better ones as parents• Eliminating the worse ones
Genetic Algorithms (GA)
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GA - Procedures
Generate initial schedules
Selection of “good” schedules
Generate new schedules by mutation and recombination
Evaluate new schedules
Survival of new schedules
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GA- Advantages
• High-performance in sequencing• Dedicated to not too complex scheduling
problems – Feasibility should be not the problem– Example: no shelf life / expiration
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Optimization Profile
• Maintain in Customizing
• Settings– Optimizing procedure
• Constraint-based Scheduling
• Genetic Algorithm– Runtime– Objective Functions and its
Weights• Total lead-time• Set-up times• Set-up costs• Maximum delay• Average delay
– Constraint Relaxation• Remove maximum time
constraints• Set utilization rate of all
resources to 100%• Remove calendar• Do not consider set-up
times/costs• Undo fixing of activities
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Techniques in Solving Complex Production
Processes and Optimization Models in
APO
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APO SolversA wide variety of computational solvers
applied to specific planning functions with industry-specific variations
DemandPlanning
Heuristic Methods
Linear Programming /Mixed Integer LinearProgramming
Genetic Algorithms, Constraint-based
Programming
Exponential SmoothingHolt WintersMultiple Linear Regression
SupplyNetwork Planning
ProductionPlanning &Scheduling
APO Computational Solvers
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Complexity in Real-life Production Models
• They are too complex to solve. For example, we may have nonlinear equation(s), integer decision variables, scale, ...
• In such cases, we will have to rely on algorithm, heuristics, and other “intelligent” methods.
• Most APS systems mix optimization and heuristic methods.
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Where is Constraint-based Planning Used in APO?
• Creating Production Process Models (PPMs) in SNP and PP/DS are based on Theory of Constraint and Constraint-based Planning
• Where are the decision variables? Where are the constraints identified? Where are the objective identified?
• For example, in PPDS, the decision variables are given in the planning table (which resource is used to produce a given order and its start and end times); objective function is given in the optimizer screen; and the constraints are stated in master data.
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Start Time
End Time
Example: Decision Variables in DS
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Example: Objective Functions in DS
Optimization methods
Obj. functions and its weights
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Optimization Models in APO