{"id":11346,"date":"2017-03-15T16:00:00","date_gmt":"2017-03-15T16:00:00","guid":{"rendered":"https:\/\/zapliance.com\/?p=11346"},"modified":"2022-12-09T15:11:27","modified_gmt":"2022-12-09T15:11:27","slug":"duplicate-payments-in-sap-reducing-false-positives-using-a-utility-analysis","status":"publish","type":"post","link":"https:\/\/zapliance.com\/en\/blog\/duplicate-payments-in-sap-reducing-false-positives-using-a-utility-analysis\/","title":{"rendered":"Duplicate Payments in SAP: Reducing False Positives using a Utility Analysis"},"content":{"rendered":"\n

\u201cHow can we reduce the amount of results of twice payed invoices?\u201d was a question in the preparation of this blog post. In collaboration with the Professor and my vague recollection of past business administration lectures, we opted for a utility analysis. Therefore, the combination of Professional Judgement and hard facts of data analytics should become a part of this article.<\/p>\n\n\n\n

Part III of the series: \u201cUncovering duplicate payments\u201d<\/strong><\/p>\n\n\n\n

1.\u00a0Cashback: How the audit department pays off with duplicate payments<\/a>
2.\u00a03 steps for detecting duplicate payments in SAP<\/a>
3. Duplicate Payments in SAP: Reducing False Positives using a Utility Analysis<\/strong>
4.\u00a0How to prevent Duplicate Payments in SAP with the Internal Control System<\/a><\/p>\n\n\n\n

Why conduct a utility analysis for potential SAP duplicate payments?<\/h2>\n\n\n\n

\u201cThe term utility analysis (UA) can have a host of different meanings depending on the field of study. In general, it refers to processes that predict the usefulness (utility) of decision options. <\/em>\u201c
Source: Michael C. Sturman Ph.D<\/a><\/p>\n\n\n\n

In our case, the problem is characterized as the twice payed invoice in SAP. As each company has its own processes, your Professional Judgement <\/strong>is required, to successfully implement a utility analysis<\/strong>. Nobody knows the company you are working in better than you do! Therefore, your expertise is needed at this point. I would like to show you an example of how a utility analysis looks like, which purpose it fulfils and to which extent it may or may not be used.<\/p>\n\n\n\n

What is the objective of a utility analysis?<\/h2>\n\n\n\n

I am using the utility analysis to prioritize<\/strong> the identified amounts from the 2nd post of this series. At the end of this post, you will receive a list, ranking the probability for a potential duplicate payment from highest to lowest. <\/p>\n\n\n\n

Enough theory <\/em>for now \u2013 Let\u2019s get started!<\/p>\n\n\n\n

For the implementation of the utility analysis, I will use parts of the results from the instruction introduced in the last series. You can download it here:<\/p>\n\n\n\n

\"Download\u00a0now!\"\/<\/a><\/figure>\n\n\n\n

After you have downloaded the instruction and used the SQL statement, we can start. By using the SQL Statement, you will obtain 10 document pairs<\/strong> showing the same amount. However, you do not need each field of the results for the implementation of the utility analysis. According to the 10 document pairs, you begin by creating an overview of the document pairs. It does not matter, if a document is number 1 or number 2. You should only eliminate duplicate entries, showing the same amount and having the same documents once in cell BELNR1 and once in cell BELNR2. My table looks as follows:<\/p>\n\n\n\n

Case<\/strong><\/td>BELNR1<\/strong><\/td>BELNR2<\/strong><\/td>Same amount<\/strong><\/td><\/tr>
1<\/td>2000000185<\/td>2000000151<\/td>770195,23\u20ac<\/td><\/tr>
2<\/td>2000000188<\/td>2000000154<\/td>575964,94\u20ac<\/td><\/tr>
3<\/td>2000000190<\/td>2000000156<\/td>448569,57\u20ac<\/td><\/tr>
4<\/td>2000000186<\/td>2000000152<\/td>316526,42\u20ac<\/td><\/tr>
5<\/td>2000000155<\/td>2000000189<\/td>216692,30\u20ac<\/td><\/tr>
6<\/td>1500000002<\/td>100008630<\/td>32000\u20ac<\/td><\/tr>
7<\/td>100008654<\/td>100008630<\/td>32000\u20ac<\/td><\/tr>
8<\/td>1500000000<\/td>100008654<\/td>32000\u20ac<\/td><\/tr>
9<\/td>100008630<\/td>100008654<\/td>32000\u20ac<\/td><\/tr>
10<\/td>100008654<\/td>1500000002<\/td>32000\u20ac<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

After identifying the 10 highest duplicate amounts, stay calm in your seat. The duplicate amounts do not already represent twice payed invoices<\/strong>. For the time being, only a reasonable suspicion<\/strong> exists. In the following we will enrich the cases with the help of the utility analysis.<\/p>\n\n\n\n

The approach, or how to reach the target<\/h3>\n\n\n\n

After defining the target, the analyses basically consist of the three following steps:<\/p>\n\n\n\n

    \n
  1. Selection of criteria<\/li>\n\n\n\n
  2. Weighting of the criteria<\/li>\n\n\n\n
  3. Evaluation of alternatives<\/li>\n<\/ol>\n\n\n\n

    Before thinking about the weighting of the criteria, you must select criteria<\/strong> first. For this purpose, you may use our criteria from the last blog post:<\/p>\n\n\n\n