Name: Aggregation Behavior of Analytical List Pages in Inventory Management - SAP S/4HANA Supply Chain
Uploaded: 2020-6-2
Duration: 715 s
Description: In this video you learn how to optimize your analytical inventory management overview to understand the aggregation behavior of your stocks. You will understand how the stock values depend on the aggregation level using the example of the Slow or Non-Moving Materials app.

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<div><div><div><div><div>In this video you learn how to optimize your analytical inventory management overview to understand the aggregation behavior of your stocks. You will understand how the stock values depend on the aggregation level using the example of the Slow or Non-Moving Materials app.</div></div></div></div></div>

Aggregation Behavior of Analytical List Pages in Inventory Management - SAP S/4HANA Supply Chain

Hello, and welcome to the SAP S/4HANA Supply Chain  microlearning with the title Aggregation Behavior of Analytical List Pages in  Inventory Management. My name is Gerhard Welker, and I am product  manager of SAP S/4HANA Manufacturing, Supply Chain,  and Asset Management. In this video, you will learn how to optimize your analytical inventory management overview to understand the aggregation behavior of your  stocks. You will understand how the stock values depend on the aggregation level using the  example of the Slow or Non-Moving Materials app. In the first part of this microlearning, we will take a look at the aggregation pyramid, which explains the principle of sum aggregation. You will see the effect on the aggregation results when we apply a filter. Based on this explanation, you will learn the golden rule of understanding aggregated stock figures. In the second part of this microlearning, we will study the aggregation behavior of the analytical list page, Slow or Non-Moving  Materials, in the SAP S/4HANA system and apply the  golden rule of understanding the aggregated stock figures. First, let us visualize the principle of  aggregation. For this purpose, we will develop an aggregation  pyramid. The following example focuses on sum  aggregation, which is used as a default aggregation method for numerical values, such as stock quantities. The top of the pyramid shows the aggregated  stock quantity for material TG11 with 51 pieces at the group level of the company. In the next step, we will drill down at the plant  level of the company. The pyramid shows two plants. In Plant 1010, material TG11 has zero stock. In Plant 1710, the stock quantity of the material is 51 pieces. So stock quantity at group level is the sum of stock quantities in the plants. Even this simple example shows the basic  problem with the interpretation of aggregated data. If you, as a warehouse clerk or inventory  manager, are responsible for Plant 1010 only, and you are not aware of Plant 1710, you probably expect zero stock for material TG11 in Plant 1010. Without knowledge of the stocks in the other  plant 1710, you cannot understand the aggregated stock  figures if you do not drill down the query on the analytical list page at the plant level. Now we add further levels of complexity to the  example. The third level of drill-down is the storage  location. Each plant has three storage locations, A, B, C, which are numbered, additionally, with a plant  prefix, like 101 for storage locations at Plant 1010. In the stock of Plant 1010, there is a special  feature that the negative stock of storage location 101C offsets the stocks of storage locations 101A and  101B. If the user of an analytical list page is not aware of the negative stock posting in storage location  101C, the aggregated zero stock information on plant  level could be completely misinterpreted. Here too, it is recommended that you drill down  the query to the next stock identifying level, namely the storage location level, to understand the stock figures. The right half of the pyramid shows the sum  aggregation of stock quantities in plant 1710. The aggregated total of 51 pieces at plant level is distributed among the three storage locations as shown in the pyramid. Last but not least, let us enhance the example with the next level of complexity, the different  stock types. We will limit our considerations here only to the original stock types, namely unrestricted used stock, blocked stock, and quality inspection stock. The pyramid shows you the stock quantities are divided among the different storage types. For instance, on the left side of the pyramid, you see that the 20 pieces in storage location  101A has an aggregated total of 12 pieces of unrestricted use stock, five pieces of blocked  stock, and three pieces of stock in quality inspection. Please note that the special stock types  enhance the complexity onthe  stocks types level  additionally. In the meantime, we have expanded our  aggregation pyramid to four drill-down levels, group, plant, storage locations, and stock types. The next slide will show you that there are many more stock identifying  fields, which are relevant for understanding the aggregated stock figures. But before we leave the aggregation pyramid, let's look at what happens when the user applies  a filter, for instance, unrestricted use stock. As you see in the pyramid, the filter is applied to the individual data records. This means that data records that do not match the filter criterion are filtered out. The aggregation result at higher levels changes  accordingly. Due to the applied filter, the aggregated stock  quantity is adjusted to nine pieces at group level. Please note the sequence of operations. First, an analytical list page filters the data  records at they lowest drill-down level of stock identifying fields before aggregation  takes place. Let's summarize the findings as the golden rule of understanding aggregated stock figures. If you have doubts about stock figures displayed on analytical list pages, open the App View settings and select all stock identifying fields to drill the query down to the deepest level of stock identification. Consider that the filter will be applied at the lowest level of stock identification, namely the individual and material documents, before the aggregation takes place. Without drilling down to the lowest level of stock identification, it is very difficult to understand aggregated stock  figures, especially when a filter is applied. Let's now continue with the system demo in which the view settings, shown in the screenshot, are a central part. I log into the SAP/4HANA system as an  inventory manager and opens a Slow or Non-Moving Materials app. With this app, you can monitor and make time dependent investigations of the slow or non-moving materials in your  stock. According to the aggregation pyramid, I have prepared an app variant at the starting point of this demo that is limited to material TG11, as you can see here in the filter criteria, at the group level of the company. The slow moving indicator is set equal to zero in the filter criteria, which limits the query to non-moving materials, which represent a combination of locations and  materials where the stock level is not zero and no consumption postings were posted for the defined analysis period of 30 days equal and prior to the reference date. The query contains only the material as a stock identifying field and retrieves a few aggregated stock figures as shown here in the table view. Note that the aggregation method's sum is applied to the numerical numbers like stock quantity and stock value, whereas the aggregation methods max is applied to the date figures like date of last posting and date of last  consumption. In the next step, we drill down the chart query to the plant level. Now we skip to chart, and focus on the table  view. The filter contains a few stock identifying fields like material, plant, storage location, material group, material type. Now we add additional filter criteria. For this purpose, we press the Adapt Filters  button, open the More Filters under aggregation fields, and add the date of last posting for our example as an additional filter criteria. And also here you find more stock identifying  fields. And from this list we choose the stock type and the special stock type for our demo here. Now I will show you how difficult it is to  understand the result of a highly aggregated query if you apply a filter. In the column, Date of Last Posting, you see, due to the max aggregation, that on the 28th of May, the last posting happened. If we now apply a filter, on the 28th of May and apply the filter, you see exactly the stock quantity, stock value of the posting on the 28th of May. If you now additionally apply a filter, for instance on stock type, for unrestricted-use stock. And we apply this filter. See, no data is found, which is true, this means that for unrestricted use stock, we have no posting at the 28th of May. Now we are coming to the golden rule of understanding aggregated stock figures. First, let me delete the filter restriction on stock type and date of last posting, and we are selecting, again, the aggregated  record. You see here that in the meantime, today, the 29th of May, there's a new posting. Now, as mentioned as the golden rule of understanding aggregated stock figures, I open the view settings and follow the golden  rule of understanding aggregated stock figures by selecting all fields. The view settings allows us to put the filters into the desired order. For this purpose, I bring fields of interest on the  top like the plant. Next one is the storage location, stock type, special stock type. And from the top, I bring it in the right order. The plant should be below material, storage location below plant, stock type below storage location, and special stock type below stock type. Now we receive the result of the query with a drill down to all the stock identifying  fields. Adjust a little bit the column widths, and then have a look at the data. You see here that on the last posting date that we had, on the 28th of May, a posting with the stock type 07. This is the reason why we haven't found any data with a filter criteria stock type 01 with a last posting date of 28th of  May. With this, I'm coming to the end of this demo. My recommendation is create also an additional  variant with all fields, as I have prepared here. And check the data on the deepest level of drill  down. Let's summarize the key learnings. You have learned the principle of aggregation  behavior within the analytical list pages in inventory  management and the golden rule of understanding aggregated stock figures. For further learnings, check out offerings of openSAP courses on open.sap.com and check out SAP's Learning  Journeys on help.sap.com/learningjourneys. Thank you, and goodbye.