One of the popular requirements in terms of adjusting output forms is to add contact person to the document header. In Billing Documents, for example, there is no such default field, therefore custom field and logic are necessary. In most cases we can use name of employee, who created the document as contact person name. Let´s have a look, how to implement this requirement in Invoice form.
Authorization trace is mainly performed to identify and record the missing access against the user access. Tracing supports when the default authorization values are stored in Tcodes like SU22 and SU24 and when maintaining the same authorization data for roles. Traces are of basically of types, they can be either system-wide or limited to a specific user or instance. Tracing can be executed for trouble- shooting, especially for missing access in SAP GUI by validating the access provided to the SAP User ID. This article majorly describes how to perform tracing in R/3, ECC and S/4 systems.
For delivering the day-to-day business requirements in SAP, users require ID and password to login. The types of users that login to SAP are categorized as follows:
Intelligent Scenario Lifecycle Management (ISLM) is a framework that allows you to perform lifecycle management operations on machine learning scenarios. You can consider ISLM as a self-service tool that allows you to handle various operation requirements of machine learning scenarios. You can train an intelligent scenario and use the trained model to get an accurate inference result.
Depending on the machine learning scenario that is associated with a business application, the scenarios can be categorized as:
◉ Embedded: In this approach, a business application, for example SAP S/4HANA runs in the same stack as its machine learning provider SAP HANA machine learning with the analytics libraries SAP HANA Automated Predictive Library (APL) or SAP HANA Predictive Analysis Library (PAL). APL exposes the data mining capabilities of an Automated Analytics engine for developing predictive modelling processes for business analysts. PAL offers advanced analytics algorithms for data scientists. This can be used to solve use cases of forecasting, trending, and so on.
During ERP era Customer Service (CS) component supports all the processes related to Services which we provide to customers. We can Structure and create Technical Object, manage data for Warranties, Create Service Requests, bill the cost occurred during service processing. However, this is not part of long-term scope in SAP S/4HANA journey. CS will no longer available after 2025, hence no license and support would be provided as well. Moreover, SAP could choose to remove them at any time after 2025.
SAP is providing the solutions to cover the scope of CS within S/4HANA itself.
Until S/4HANA 1809 the new service functionality was an add-on for S/4HANA called Customer Management.
Growing complexities of any industry along with its own growth provides a way to compete and try to derive best solution possible. Same happened with discrete manufacturing industry, big giants like SAP, Oracle, Salesforce, and others focused their will to deliver a best-known solution to make the complex process of discrete manufacturing easy to consume for the customer. Integration of the solution with all other modules of complete Product Lifecycle Management (PLM) process is exciting and a big challenge for everyone. More exciting is to find out the solutions they provide and how they differ and challenges the complexity of the discrete manufacturing industry problems.
A lot of data science and machine learning work is done in Python. For this, the “Python machine learning client for SAP HANA” (hana-ml for short) provides easy access to SAP HANA tables in the form of Pandas compatible data-frames. However, with its multi-model capabilities (like spatial, graph, and document store), SAP HANA has more to offer. In this blog post, you will learn about the enhancements of the library to support these multi-model capabilities and how you can leverage them for your work. We will walk through an end-to-end example based on Wellington’s stormwater network. We will evaluate and visualize the graph and show how we can analyze problems that might occur in the network based on built in graph algorithms. The data and code is available on github.com. If you want to run the Jupyter Notebook yourself, you need to take care about the prerequisites.
In this blog post we will cover Order-Related Replenishment at Storage Type level with Rough Bin Determination.
Rough bin determination means the storage type must be written to the delivery item even if no stock exists. You specify that the storage type (without storage bin) is written to the source data of a delivery item, even if there is no stock for the product in the storage type. This enables you to use the order-related replenishment to perform replenishment for the pick storage type, since a requirement for picking occurs in this storage type as a result of this. If rough bin is not enabled, the storage type is generally not used if there is no stock for the product in that storage type.
◉ Like planned replenishment, it can be triggered either interactively or in the background
◉ It calculates replenishment quantities according to open warehouse request quantity
◉ It is started when stock figure is lower than the required quantity
◉ It rounds up to multiples of minimum replenishment quantity
◉ Exceeding maximum quantity is possible
This document will explain you, how to connect HANA database and table operations using python language.
◉ Python latest software installation
In this blog, I will share with you the minimum configuration needed to activate and leave the app ready to use. Also, a tour of its functionalities, the multiple available fields you can change, and the option to include a new field by extensibility.
Let´s start with the required configurations:
Step 1 – Activate the services as indicated in the Fiori app library in the level “Implementation Information”.
Step 2 – It is necessary to configure the number of parallel processes it allows. Please check this with your technical team to avoid any negative impact on the daily operations.
HANA admin / developers frequently get tasks to export/import selective list of HANA tables data to disk for stage refresh, backup, re-partitioning etc.
Developed bash script code helps automate export or import activity for multiple selective tables.
Features
◉ Server level export /import execution instead from studio as client
◉ script can be run in background at HANA server and progress can be monitor
◉ easy configuration using hdbuserstore
◉ input list of tables and script will take care of export /import
The goal of fast restart option is to improve large in-memory SAP HANA database restart times. This article outlines the plan to address this important area of reducing downtime with a step by step approach, recommendation on tmpfs sizing and FAQ’s.
To understand the concept of fast restart option (FRO) with tmpfs, basic understanding of tmpfs file system functioning is required
Basics Of tmpfs filesystem: –
tmpfs (temporary file system) is a virtual filesystem created to store files in dynamic (volatile) memory also known as DRAM (RAM). Tmpfs filesystem is typically created on RAM.
Commitment management in SAP S/4HANA tracks future commitments against cost objects (I.e. Cost center, Internal order etc.) This helps user to do comparison between actuals and commitments. Commitment amounts are available as information for reporting purpose. You cannot use commitments to prevent user from creating the purchase order that exceeds budget. Prevent user to post actual greater than budgeted is available in Availability Control functionality in SAP S/4HANA Cloud. Commitment management is a part of the cost monitoring process. Commitment Management line items are triggered by purchase requisitions and purchase orders that utilize cost centers. These commitments are updated in Extension Ledger 0E (pre-delivered) in the ACDOCA table. Commitments are reduced by various business transactions such as goods receipt, service entry sheet etc.
We live in an exciting era where business is transforming at an unprecedented rate that we have not fully come to grasp its impact on data privacy. Look at the statistics which are staggering. Globally, smartphone penetration is estimated to be 8 billion smartphone subscriptions worldwide. An average smart phone user has 80 application on its phone and approximately 10 application are used daily. The mobile phone application collects enormous amount of personal data such as GPS location, IP addresses, voice, video, contact lists, text messages, passwords, call history. The e-commerce and service providers use advance digital technologies such as Artificial Intelligence, Machine Learning, Software Robotics to profile and process personal data. Realizing the impact of data privacy and harm it may cause to individuals and society, many countries globally have been enacting data privacy regulations.
Managing Customer Experience:
While an unhappy customer might start to ring alarm bells, most of the successful companies see it as an opportunity to resolve the complaint and keep the customer loyal. In fact, 73% of customers say they stay loyal to brands because of friendly customer service agents. (HubSpot, 2021).
More Info: C_HANATEC_17 Key Points
So, it’s an opportunity to demonstrate to your customer in a much more direct and personal way that their feedback is important, and you care about the outcome.
But it’s not just about preventing unhappy customers from becoming detractors – it’s equally useful to follow up with neutral customers and turn them into promoters.
This blog is specifically written how ARM for customer return can be integrated to SAP embedded EWM 2020
JSON is a widely spread format for exchanging and storing structured data. It enjoys great popularity in the age of microservices, e.g. for exchanging data between servers and clients via RESTful APIs. In this blog post, I’ll show you how to persist data in JSON format to an SAP HANA table or an SAP Data Warehouse Cloud table using SAP Data Intelligence.
For this purpose, the “SAP HANA Client” operator in SAP Data Intelligence is used. In addition to other options, such as executing SQL commands and inserting csv. data, it offers the possibility to persist JSON files in SAP HANA and SAP Data Warehouse Cloud. It is crucial that the file has the correct format. You can see examples of the correct implementation of the JSON file in this blog post.
SAP BTP Launchpad service could be used as a central point of access to SAP, custom-build and third-party applications and extensions there by leading to higher user productivity.
The Fiori content can be exposed to BTP launchpad manually or via Content federation. To know how S/4HANA launchpad content can be federated to BTP, check out this Tutorial on developers.sap.com. This is a detailed set by step process.
While I tried this , I stumbled upon couple of issue and had spend some time to figure out what’s wrong and that is what i want to share in this blog so as to help others in case they face they same issue. We will also look at some of the content federation limitations.
As we all know SAP has some robust documentation and approach in upgrading from lower release of S/4 HANA to higher release. This personal insight is just based on some lessons learned and how we can proactively take control to ensure that we adhere to a timeline with less uncertainty.
While working on some of the upgrade projects, we have come across some common issues, and as we all learn from our experience. This blog will help us to dig into the system and understand it beforehand so that there is no surprise at a later point in time.
The latest addition to the SAP Analytics Cloud is developed for business plans and to consolidate operations in unison with predictive analytics in the functionalities components of any business. As a result, businesses leverage their strengths and weaknesses by improving their success scores and developing new strategies to operate better in the market.
In this blog post, I will show how to use some basic SAP HANA Graph functions to understand dependencies in a directed graph. The demo data describes python packages and their dependencies. Resolving package dependencies is required if a package manager like “Anaconda” needs to check for pre-requisites before installing a new package. However, the patterns discussed in this article are generic and apply to use cases in manufacturing, utilities, IT and many other industries. The data and code is available on github.
Today I would like to talk about Cash Management in SAP S/4HANA. There is great news about this business process, no matter if the decision was to go for the basic or the advanced version of it. Today, it is possible to work with Cash Position and Liquidity Forecast (AKA FF7AN and FF7BN) from the backend or the Launchpad, together with Cash Flow Analyzer app, to obtain better and more complete information about financial flows derived from items in Finance and Logistics.
As Cash Flow Analyzer uses a predefined structure and brings more dynamism in financial position visualization with less selection criteria, let´s move thru its different selection fields and see how we could get a similar output in Cash Management and Forecast FF7AN /FF7BN transaction.
