Undertanding SNP Optimizer.2.0

5/21/2018 Undertanding SNP Optimizer.2.0

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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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Copyright IBM Corporation 2007SCM SOQ| 12-Aug-142

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

IBM Gl b l B i S i


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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.

IBM Gl b l B i S i


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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.

IBM Gl b l B i S i


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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.

IBM Gl b l B i S i


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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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Select all the

horizons you want

Optimizer to

respect.



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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


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 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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Resource load at resource 1




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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 Scenarios 2



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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 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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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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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!!

Documents

Undertanding SNP Optimizer.2.0