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Author's profile photo Gajanan Patki

SNP Optimizer Analysis

SNP Optimizer- Solution for Make To Stock SCM

Table of Contents

Revision History. 2

Abstract 5

Conventions Used. 5

1        Introduction. 6

2        SNP Optimizer 7

  1. 2.1        Master Data in optimizer 8
  2. 2.2        Transaction Data in optimizer 8
  3. 2.3        Costs in optimizer 9
  4. 2.4        Optimization Logic. 9
  5. 2.5        Optimizer Result Analysis. 10
  6. 2.5.1    Input Log. 10
  7. 2.5.2    Output/Result Log. 14

3        Conclusion. 16

4        Definitions, Abbreviation and Acronyms. 16

5        References. 16

6        Acknowledgements. 16

Table of Illustrations

Figure 1:  Optimization Model in terms of the Masters and Constraints

Figure 2:  Optimization model

Figure 3:  Supply Chain


SNP Optimizer is a unique tool to model Supply chain. It is a perfect tool to “Make To Stock” scenario and works on Cost based prioritization. Optimizer Considers Demand Elements, safety S tocks and various receipts elements as well as creates Stock transfers & Production Orders. Optimizer enables planning of FG’s and SFG’s too. Optimizer considers resource capacity and does constrained based planning.

SNP Optimizer result analysis is not available in any standard document and the analysis is provided by SAP as paid consulting. The paper highlights the Make to stock model mapped in SNP optimizer and the complete analysis of the SNP Optimizer result log

Conventions Used


1 Introduction

SNP optimizer is a tool used by APO which works on the cost logics. Various costs in the supply chain can be the guideline for an optimal solution. The optimizer works on the two major objective functions and those are Profit maximization and Cost Minimization. If we are operating on Front End supply Chain with a decision to increase margins we can use Profit maximization. If we are operating in the back end of the supply chain the major objective of the business is to save cost hence the Cost Minimization objective can be used to operate in these scenarios.

Figure 1: Optimization Model in terms of the Masters and Constraints

2 SNP Optimizer

                                                                                    Figure 2: Optimization model

Figure 3: Supply Chain

2.1 Master Data in optimizer

The essential master data considered in the SNP Optimizer can be listed as follows:

2.2 Transaction Data in optimizer

The essential transaction data considered in the SNP Optimizer can be listed as follows:

2.3 Costs in optimizer

Following are the costs which are considered in optimizer:

2.4 Optimization Logic

The objective function of the optimizer guides to model to keep the Total Cost of the supply chain to be minimum. Total Cost of the model can be defined as:

Here to save one set of cost we have to incur other. This can be explained with examples:

  1. To save on Demand Lost and Demand Delay production and Transportation Cost is to be incurred
  2. If we store a quantity at a location to meet the safety norm we have to incur the Storage Cost

The optimization model is created as equations and solved using the Linear Programming problem. An optimization model in mathematical optimization consists of four key objects:

2.5  Optimizer Result Analysis

The results of the optimization run are stored in the Optimizer log. The logs are split in two halves

  1. Input Log which has all the input data considered for the run
  2. Output Log which contains the result of the run

2.5.1 Input Log

ET_BUCKDF: Contains the details of the bucket and the horizon of the run

Masters considered in the Logs:

Location Products: To check if the Product and the location have participated in the run

ET_ARC: Contains the details about the lanes between source to destination and the Transportation Duration

ET_ARCMAT: Contains further details about the lanes at product level and the Transportation Cost (Per EA)

ET_PROMO: Contains details about PPMs the Min Lot Size Resource used and the Production Cost

ET_PROMAT: Contains details about input products in the PPM and the Charge Quantity as Var Quantity

ET_PRORES: Contains details about the time required to produce the charge Quantity and also has the resource on which the production will happen

ET_RESC: Contains details about the capacity of the resource defined in Sec and the initial consumption of the resource (if any)

Transaction Data in the Logs:

ET_DEMAND: Contains the Demand per bucket. All the three Demand Class will be reflected in this Log

ET_DEMCLTIM: Contains the Demand Delay Cost (Per EA per Bucket), Demand Lost Penalty per period (Per EA) per bucket. Also the permissible demand delay (In Days)

ET_LOCPROD: Contains the In-Transit and the safety Stock Quantity and also the Safety Stock number of days maintained at the location

ET_LOCMAT: Contains the Initial Stock present for the product at the locations. Also have the information about the Safety Stock Violation Cost (Per EA Per Day) and Storage Cost (Per EA Per Day) at each location

ET_RESINI: Contains the fixed production orders

2.5.2 Output/Result Log

IT_DEMAND: Contains the details of the Demand met for the bucket in that location. Demand delayed is also visible in this log

Calculations: Delivered Qty 370 is delayed by a Bucket (Week in this case). Delay per EA Per bucket for COMH100 at ABDH is 13.6. Total Delay will be 370*13.6*7(7 days for one week) = 35224

IT_NOTDELI: Contains the details of the Demand unmet for the bucket in that location.

Calculations: The demand lost is for 440 EA and the demand lost penalty for bucket 17 for Demand Class 2 for COMH100 at ABDH is 4202.400.

Demand Lost Penalty = 440*4202.4 =1849056

IT_PROMO: Contains the details of the Production lots produced and Total Production Cost

Production Qty = Prod Qty * Charge Quantity= 2.270* 33335.0 = 75671 EA

PPM Cost = Prod Qty * PPM Costs =2.270 * 387966 = 880599

Production Qty is rounded to third decimal. Actual value is 2.269784

IT_ARCMAT: Contains the details of the total Stock transfer happened to ABDH

Transportation cost=

Quantity (Stock Transfer) * Transportation Cost per EA (ET_ARCMAT)

=2183.667 *0.099 = 216.18

IT_LOCMAT: Contains the details of the Storage Cost and the Safety Stock Violation Cost


Total Storage Cost

= Storage Qty * Storage Cost per EA Per Day * Days in the Bucket

= 429.33* 0.170 *7 = 510.907

Safety Stock Violation Cost

= Safety stock violation Qty * Safety Stock violation penalty Per EA Per Day * Days in the Bucket

= 766.667* 2.176 *7 = 11677.867

3 Conclusion

SNP Optimizer is a unique tool to model “Make To Stock” scenario in Supply chain. Works on the Cost based prioritization and considers Demand Elements, Safety Stocks and various Receipts elements as well as create Stock transfers & Production Orders

This paper will enable an SNP Consultant in Company to interpret and analyze the planning process in an SNP optimizer. The consultant will know not only know the masters and transaction data considered in the run but will also know the cost defined and the output of the optimizer to be checked in which logs

4 Definitions, Abbreviation and Acronyms




Production Process Model

5 References



Real Optimization with SAP® APO

Josef Kallrath- Thomas I.Maindl

6 Acknowledgements



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