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A very good document to understand SNP optimizer.
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Copyright IBM Corporation 2007
IBM Global Business Services
Understanding SNP Optimizer
Date: 04 Oct 2012
Ravindra Deokule
Chandan Dubey
Nikhil Kalkar
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Objective
The objective of this session is to provide insight on the basics ofoptimization.
Provide overview on the standard SNP optimizations features & its
usability.
Discuss few SNP Optimizer scenarios and their setup
Target Audience
All SAP APO practitioners
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Contents
1. What is Optimization?2. What is Business Optimization?
3. Optimization Methods & Architecture
4. About SNP Optimizer
- Optimizer Logic
- Optimizer Master Data
- Optimizer Setup
- Optimizer Run & Log Interpretation
5. SNP Optimization Scenario- Case Studies
Scenario 1: Choice of plant (more than one plant supplying a customer)
Scenario 2: Choice of PDS within a plant
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What is Optimization?
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Some background
Designer ConsiderationsReduce Thermal emission fromthrust
Reduce Radar detection
Reducing radar detection whenthe aircraft opens its weaponsbays
Instability ofDesign
AerodynamicLimitation
ElectromagneticEmission
ReducePayload
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Optimization is the process of finding the greatest or least value of a function
for some constraint, which must be true regardless of the solution.How to arrive at the objective function is illustrated in the below farmer issue
example;
John Doe has area A sq. m of farm land
- Can plant Rice or Wheat
- Selling Price of Rice is Sr and Sw of wheat
- Amount he can spend on fertilizer is F (usage rate is Fr & Fw)- Amount he can spend on pesticide is P (usage rate is Pr & Pw)
Simplex method the equation becomes;
- Ar 0; Aw 0; X1 0; X2 0; X3 0; X4 0
- Ar + Aw + X1 = A
- FrAr + FwAw + X2 = F
- PrAr +PwAw + X3 = P
- SrAr +SwAw +X4 = S
Optimization Process
Farmer need to maximize theselling price to maximize theprofit!
He needs to decide the area Arfor rice and area Aw for wheat??
Constraints: Total Areacannot exceed A
Amount for Pesticides &Fertilizer Cannotexceed P& F resp.
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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.
Objective Function: The Objective Function is the single benchmark for evaluating all
combinations of decisions that satisfy the constraints. It usually represents a quantifiable goal,and sometimes two or more goals e.g Farmer need to maximize the selling price to maximize
the profit!
Decision Variables: Decisions variable are the independent variables of the problem.
Typically, decisions take the form of Production lot sizes, Transport lot sizes, Purchase of
additional capacities and so on .E.g. What is the area for Rice & Wheat plantation needs to be
decided?
Constraints: Constraints represent limitations on which decision can be made and how
decisions can be made. e.g. Total Area cannot exceed A Sq Meters e.g. Total expenses on
Fertilizers & Pesticides cannot exceed F & P?
Constraints are also used to apply business rules when solving a problem.
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Optimization- Methods & Architecture
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 APO Optimization Architecture
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About SNP Optimizer
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SNP Optimizer Logic
Objective:
The objective of the SNP Optimiser is to produce a plan that minimises the
overall supply chain cost whilst meeting the constraints.
Total supply chain cost = Storage cost + Transportation cost + Production
cost + Penalty for violating Safety Stock + Penalty for delayed deliveries +
Penalty for non-delivery
Two types of constraints considered by Optimizer.
Hard constraints:
These are constraints that may under no circumstances be violated -Lot sizes (Minimum and Rounding value) in case Discretization is used.
Resource capacities in case it is selected in optimizer profile.
Transportation lead times
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SNP Optimizer Logic (2)
Soft constraints:
These are constraints that may be violated if required to reach a feasible
plan: -
Optimiser cost settings
Safety stock levels
Example:
An example is provided in the next slide to illustrate the optimiser logic.
The optimiser does not work through a step by step process as shown
here, but this is done to make the example easier to understand.
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Optimiser Logic Example
SU
Storage cost = 7
SU-DC
Storage cost = 1
PDC
Storage cost = 4
Transportation
cost = 0
Transportation
cost = 0
Transportation
cost = 999
W4
Demand 100W4 Demand 100
Produce Line 1: 60 in W4, 10 in W3
Produce Line 2: 20 in W4, 10 in W3
W3 Ship 20
W4
Ship 100(Received
W5)
Store 20 for 1 week (W3)
W4 Ship 80
W5 Forecast 110W5 Safety Stock 30
W4 Closing stock 40
Lead time
= 7 days
Lead time = 0 days
Line Cost Capacity
W3
Capacity W4
1 0 10 60
2 1 20 20
Step 1: Take note of (a) Forecast and stock in PDC, (b) Alternative packing lines in SU - Line 1 is the
preferred line, (c) Alternative routespreferred route via SU-DC Step 2:
Cheapest route selected
Demand propagated to SU (taking
1 week lead time into account)
Step 3:
Production cost and free capacity
of alternative lines considered to
determine how much to produce on
each line and when.
Step 4:
W3 production shipped immediately
and kept in SU-DC for one week
where it will incur the lowest
storage cost
Step 5:
W4 production shipped to SU-DC
Step 6:
Stock shipped from SU-DC to PDC
in W4 to meet demand in W5
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SNP Optimizer Master Data
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Optimizer Costs
Penalty costs are used to ensure the correct planning behaviour.
Costs are only required where a choice needs to be made. Example: if there is
only one possible transportation route, no transportation cost is required.
Non Delivery Penalty
Late Delivery Penalty
Max days Late Delivery
Safety Stock Penalty
Storage Cost
Transportation Cost
Production Cost
Procurement Cost
Cost of Increasing Capacities
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Delay and Non Delivery Penalty
Maintained on SNP1 tab of location-product masterMaximum Delay: Maximum number of days that a product is allowed to be
delivered after the demand date.
Delay Penalty: Penalty incurred (per Unit per Day) in case the product is
delivered late (applicable only up to the Maximum Delay).
Non Delivery: Penalty (per Unit) of product that cannot be delivered within the
period specified by the Maximum Delay.
Generally Non Delivery Penalty is set as a very high value as Non Delivery ofproduct means direct business loss.
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Delay and Non Delivery Penalty (2)
Example: In case if 5 Units of the product can be delivered only after 6 days afterthe demand date, penalty = 5*3000*6= 90000
For the same example: if 5 Unit cannot be delivered within 7 days,
penalty = 5*3000000 = 15000000
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Storage and Target Stock Penalty
Maintained on Procurement tab of location-product masterStorage cost: Cost (per Unit per day) to keep product in stock on a location.
Example storage costs:
Factory = 9 Factory DC = 7 Primary DC = 5 Secondary DC = 7
Example Safety Stock Penalty: 150
Total storage cost at Primary DC for the week 31.2012 in example below =
608*5*7 = 21280
Safety Stock violation for same example = (1600 - 608)*150*7 = 1041600
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Transportation Cost
Maintained on Transportation LaneTransportation cost: cost of using this lane (per Unit)
Example Settings:
Do not maintain cost where there is no choice of lane.
Cost of 10 for non-preferred T Lane.
Cost of 0 for preferred T Lane.
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Production Cost
Maintained on SNP PPMSingle level variable cost: Cost per PPM output quantity
Example settings:
Preferred PPM = 0/Unit
Alternative 1 = 1/Unit, Alternative 2 = 2/Unit etc.
Production cost per Unit =
200/100 = 2
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Lot Size
Maintained in ECC material master, integrated to APO location-product
Minimum Lot Size should be a multiple of the Rounding Value.
Rounding value will only work if the Discretization check box is set inthe SNP PPM.
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Optimizer Settings
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Optimizer Settings
The planning result is influenced by:
- Master data (as described before)
- Optimiser Profile
- Cost Profile
- Priority Profile
This section describes the settings in the Profiles. These normally do not
require regular changes, but it is important to understand the influence of
certain parameters.
Optimiserprofile
Costprofile
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Optimizer Profile
Optimization method can be selected here.
Select Capacities and Lot Sizes that you
want optimizer to respect. These are Hard
Constraints.
Absolute Deviation means SS short fall
quantity (Not the percentage value of short fall
quantity) will be multiplied with the penalty to
get an absolute value.
Shelf life planning can be done withOptimizer.
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Optimizer Profile (2)
Quota arrangements can be considered in optimizer. You need to define cost
of falling below or exceeding the quota. It can be modified to get an optimized
Quota Value, if selected in the background optimizer run.
Product interchangeability is supported with Optimizer.
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Optimizer Profile (3)
Constraints on this Tab arerespected in case of Discrete
Planning. Number of Periods
maintained here are periods by
which Optimizer will respect
these constraints. It is used for
problem simplification.
Linear Planning is
unconstrained planning.
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Optimizer Profile (4)
Runtime is split downbetween the number of sub
problems within the
selection by the optimiser.
Number of iterations
Number of times optimiser
will try to improve the firstsolution it finds.
Whichever is reached fist
will stop that solution. If all 6
improvements are reached
first then it will give an
Optimal Solution.Heuristic first solution
Reduces runtimes. Runs
Heuristics internally to get
first solution. Then
Optimizer tries to improve
the first solution.
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Time Allocation
Product decompositionthis is used to split up the products in logical subproblems.
The total time is then shared across all the problems using an internal formula
within the optimiser recognising how many problems and how many elements
are there to the calculation.
- This time is allocated to problem 1
- After problem 1 completed then time is re-allocated (time left divided by no
of problems left using the same dynamic calculation)this is repeated until
all problems have been solved.
So optimiser calculates solutions within 120 mins, if it finds 6 feasible solutions
each better than the last then the optimiser stops when it reaches 6 even if it
has only used half time allocated.
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Optimizer Profile (5)
Select all the
horizons you want
Optimizer to
respect.
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Optimizer Profile (6)
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Optimizer Run
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Interactive Run
Points to remember when running the optimiser on-line:Select the product across the full supply chain (i.e. include all relevant
locations).
If the product is produced on a constrained resource, include all products
on this line.
In case you want to see results and not wish to save them then afterrunning optimizer and analyzing results you can press refresh and do not
save the result. It will however generate optimizer log which can be
accessed though transaction /SAPAPO/SNPOPLOG.
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Interactive Run (2)
Load your network in planning book and use Optimizer Button which will take youto the next screen shown below:
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Background Run
You can schedule your OptimizerJobs in background. It will create
and save orders in Live Cache.
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Optimizer Log
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Optimizer Log
A detailed log is created for every optimiser runFor both background as
well as interactive runs.
The log contains a lot of information
- Inputs utilised for the calculation
- Outputs from the calculations
- Error Messages from the run
- Trace File: It is the detail of what happened in the Optimiser server
Transaction /SAPAPO/SNPOPLOG: Here you will see details aboutOptimizer runs like runtimes, Selection ID, User etc.
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Optimizer Log (2)
Optimizer work in 3 steps and related runtimes can be seen here: -
Runtime1. Read and delete orders, model creation and populating input log.
Runtime2. Model consistency check and computation at ILOG server.
Runtime3. Order creation in Live Cache, Output Log generation.
Message log will contain summary of master data consistency check andSolution resultOptimal or Feasible.
Tables in Optimizer Log are mentioned below:
Input Parameters: Info about Planning Book, Data View, Cost Profile,Optimizer Profile etc.
Location Products: List of location product given to optimizer as input.
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G oba us ess Se ces
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Optimizer Log (3)
Deletion Time Period: Complete horizon in which deletion happened for
each location product.
Input Log: Below mentioned are few important tables:
ET_BUCKDFBucket Definitions
ET_LOCMATLocation Products
ET_QTAHEADQuota Arrangement (Header Data) ET_QTAITEMQuota Arrangement (Item Data)
ET_RESOURCEProduction Resources
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Optimizer Log (3)
ET_ARCT Lane MOT
ET_ARCMATProduct specific MOT
ET_RESCAvailable capacity of Production resources
ET_PROMOPPMs
ET_PRORESResource consumption in PPMs
ET_DEMANDDemands
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Optimizer Log (4)
Results Log: Below mentioned are few important tables:
IT_LOCMATLocation Products
IT_RESOURCProduction Resources (Standard capacity)
IT_ARCMATStock Transfers
IT_PROMOPlanned Orders
IT_PROCURPurchase Requisitions
IT_DEMANDFulfilled DemandsIT_NOTDELIUnfulfilled Demands
Message Log: It contains Stepwise details of the Optimizer run.
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Optimizer Log (5)
Trace File:This is a large file containing details of the optimizer run at the
server. This lists the sub problems the optimiser has used product
decomposition for to break up the problems. It also states success of that
sub problem.
Optimum Solution Found
- This is where the optimiser has found solutions that cannot be improved
within the number of iterations maintained in the optimizer profile.
Feasible solution found for this sub problem, but time-out
- This is where the optimiser has found a solution, however it has not had
time to complete improvements.
No Solution Found
- This is where the problem the optimiser is trying to solve cannot be
solved in the time allocated. No results will be seen in the planning book
for these products. Optimizer patch upgrade should be considered.
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Planning Scenarios
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SNP Optimizer Scenarios
Scenario 1: Choice of plant (more than one plant supplying to the customer)
Scenario 2: Choice of PDS within a plant
Scenario 3: Choice of source of supply - internal production vs sub-contractor
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Planning Scenarios 1
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Scenario 1: Choice of plant (more than one plant supplying a
customer)
DC
Mfg Plant 2
Mfg Plant 1
Demand forecast at DC
Plant 1 is the preferred source, Plant 2 is 2ndoption
Plant 1 is loaded 100% before loading Plant 2
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Scenario 1: Choice of plant (more than one plant
supplying a customer) Master Data
Product S100001
DC 10001Mfg Plants BP0Y ( P) BP01
Source of Supply PPM_S100001_BP0Y PPM_S100001_BP01
PPM Single level cost 1 99
Resources WPP_ROL_BP0Y_001 WCM_HRM_BP01_001
Procurement type E E
Transportation
Lane
Mode Duration Transport
ation costBP0Y -> 10001 Truck 24 H 0.01
BP01 -> 10001 Truck 48H 0.1
Procurement cost
Prod storage cost 0.01Safety stock penalty 0.01
DC
Mfg Plant
Mfg Plant
S100001 @ BP0Y
S100001 @ BP01
Procurement cost
Prod storage cost 0.05
Safety stock penalty 0.01
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S1 Material Master data
Material (S100001) @ DC (10001) SNP1 tab and Procurement tab
Material (S100001) @ Primary plant (BP0Y) SNP1 tab and Procurement tab
Material (S100001) @ Secondary plant (BP01) SNP1 tab and Procurement tab
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S1 Transportation data
Transportation Lane
BP0Y -> 10001
BP01 -> 10001
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S1 Resource data
Resource capacity @ BP0Y
Resource capacity @ BP01
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S1 PPM data
PPM @ BP0Y PPM @ BP01
Cheaper
plant
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Planning situation before run
Planning Book Forecast @ DC 10001
Planning Book
Production (planned) @ plant BP0Y
Planning Book Production (planned) @ plant BP01
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Planning situation before run
Resource load at resource 1
Resource load at resource 2
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Planning situation after run
DC 10001
Plant1
BP0Y
Plant1
BP01
Cheaper plant
loaded to
100%
capacity
Cheaper plant
loaded to
100%
capacity
Remaining demand
is fulfilled by
alternate
plant
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Planning situation after run
Resource load at resource 1
Resource load at resource 2
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Planning Scenarios 2
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Scenario 2: Choice of PDS within a plant
Same product can be manufactured via multiple methods
PDS1 is the preferred options, PDS2 is the 2ndoption
PDS1 is fully loaded before loading PDS2
PDS1PDS2
Mfg Plant
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Scenario 2: Choice of PDS within a plant Master Data
PDS1 PDS2
Mfg Plant
Product S200001
Mfg Plants BP0Y
Source of Supply PPM_S200001_P_BP0Y PPM_S200001_BP0Y
PPM Single level cost 1 2
Resources WPP_ROLA_BP0Y_001 WBFIN_JSM_BP0Y_001
Requirement TypeNon
delivery
Delay
penalty
Max
DelayRegard as customer demand 0 0 0
Regard as corrected demand forecast 0 0 0
Regard as demand forecast 21 1 20Regard as demand forecast 1,000 1 10
Regard as demand forecast 2,000 1 20
Regard as demand forecast 3,000 1 30
S200001 @ BP0Y
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S2 Material Master data
Material (S200001) @ Plant (BP0Y) SNP1 tab and Procurement tab
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S2 Resource data
Resource capacity 1
WPP_ROLA_BP0Y_001
Resource capacity 2
WBFIN_JSM_BP0Y_001
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S2 PPM data
PPM 1 - Primary PPM 2 - Secondary
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Planning situation before run
Planning BookForecast @ Plant BP0Y
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Planning situation before runResource load at resource 1 WPP_ROLA_BP0Y_001
Resource load at resource 2 - WBFIN_JSM_BP0Y_001
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Planning situation after run
Plant
BP0Y
Demand is fulfilled by
preferred
resource
First preferred resource
s is loaded 100%
and remaining
demand is
fulfilled by
another resource
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Planning situation after run
Resource load at resource 1
Resource load at resource 2
Preffered resource is
utilized t0
100% capacity
First Preferred resource
is loaded 100%
and remaining
demand is
fulfilled by
another resource
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Questions!!
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Thanks You!!