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
 2.1 Master Data in optimizer 8
 2.2 Transaction Data in optimizer 8
 2.3 Costs in optimizer 9
 2.4 Optimization Logic. 9
 2.5 Optimizer Result Analysis. 10
 2.5.1 Input Log. 10
 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 
Abstract
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:
 To save on Demand Lost and Demand Delay production and Transportation Cost is to be incurred
 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
 Input Log which has all the input data considered for the run
 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 InTransit 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
Calculations:
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
Acronym 
Description 
PPM 
Production Process Model 
5 References
Item 
Description 
Real Optimization with SAP® APO 
Josef Kallrath Thomas I.Maindl 
6 Acknowledgements
Name 
Description 