“Architecture” Contents Index The Authors€¦ · In this section, we explain th e challenges of applying machine learning in the context of SAP S/4HANA. Solving those challenges

Browse the BookIn this chapter, you’ll establish a strong technical background with a complete view of the machine learning and predictive analytics archi-tecture in SAP S/4HANA. You’ll walk through the solution architecture for all three implementation methods: embedded machine learning, si-de-by-side machine learning, and side-by-side predictive analytics with SAP Analytics Cloud.

Siar Sarferaz, Raghu Banda

Implementing Machine Learning with SAP S/4HANA408 Pages, 2020, $89.95 ISBN 978-1-4932-2011-3

www.sap-press.com/5158

First-hand knowledge.

“Architecture”

Contents

Index

The Authors

https://www.sap-press.com/implementing-machine-learning-with-sap-s4hana_5158/#utm_source=AWS&utm_medium=readingsamples&utm_campaign=Browse%20the%20Book&utm_content=2011

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Chapter 4

Architecture

Before we dive into the implementation steps, it’s important to first get

a clear picture of the architecture of machine learning and predictive

analytics in SAP S/4HANA. We’ll walk through the architecture of the

various implementation models in this chapter.

In this chapter, we’ll dive into the architecture of SAP S/4HANA with machine learning

and predictive technologies. We’ll begin with a discussion of the technical challenges

involved with implementing machine learning and intelligent business processes.

We’ll then move on to an overview of the SAP S/4HANA solution architecture before

taking a closer look at the technical architecture for embedded machine learning, side-

by-side machine learning, and side-by-side predictive analytics. The objective is to pro-

vide you with the technical background for using the SAP S/4HANA delivered scenarios

and for implementing your own machine learning and predictive analytics use cases.

4.1 Introduction

In this section, we explain the challenges of applying machine learning in the context of

SAP S/4HANA. Solving those challenges is the added value that comes from the machine

learning approach provided with SAP S/4HANA. We also answer an important, often

asked question concerning what intelligence means within the scope of SAP S/4HANA.

4.1.1 Technical Challenges in SAP S/4HANA

Improved processing power, better algorithms, and the availability of big data are facil-

itating the implementation of machine learning for infusing intelligence into back-

office processes and providing an intelligent ERP system. With SAP S/4HANA, SAP

delivered the first intelligent ERP system on the market. SAP S/4HANA’s underlying

in-memory database SAP HANA increases speed, combines analytical and transac-

tional data, and brings innovation with embedded machine learning capabilities.

Thus, machine learning is natively integrated into SAP S/4HANA and can be easily used

across an entire organization to optimize business operations, improve employee job

satisfaction, and create better customer services. With SAP Conversational AI, a natural

language experience is provided by SAP S/4HANA, which revisits the way users interact

with the system due to enablement of hands-free applications.

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However, incorporating machine learning capabilities into ERP solutions is a challeng-

ing task. This is due to the high complexity of such systems. SAP S/4HANA, for exam-

ple, consists of more than 250 million lines of code and 143,000 tables. It supports 25

industry verticals, localizations for 64 countries, and thousands of business processes.

Two substantial challenges must be solved:

1. How can we integrate machine learning systematically into business processes for

ease of consumption?

2. How can we make machine learning enterprise-ready?

Machine learning models have been created by data scientists for decades. However,

often those models resided in special tools and were consumed by experts only, and so

they added hardly any value.

The best approach for solution architecture is to systematically build machine learning

into business processes so that intelligence is provided to the right person, in the right

place, and at the right time. In context of ERP systems, fused machine learning func-

tionality must be enterprise-ready (which we’ll discuss at length in Chapter 5, Section

5.5). This covers qualities like compliance, security, scalability, robustness, extensibil-

ity, configurability, operations, supportability, globalization or auditing. For example:

� Training and inference processes of machine learning must consider the General

Data Protection Regulation (GDPR)

� Machine learning models are new development artifacts that require a solution for

lifecycle management, starting from activation and moving through upgrades and

operations

� Prediction power of machine learning models decreases with time; degradation

strategies must be devised

� Reasoning behind inference results must be explained to end users and recorded for

legal auditing

In this chapter, we’ll focus on the solution architecture and conceptual foundations

that underly enterprise readiness.

4.1.2 How to Operationalize Intelligence

Despite a long history of research and debate, there is still no common definition of

intelligence. Scientists have provided various models for mathematical, linguistic, tech-

nical, musical, and emotional intelligence, but none of them are generally accepted. But

how can we make SAP S/4HANA intelligent without knowing exactly what this means?

The operationalization method is applied to resolve this dilemma and make the term

intelligence measurable by defining different automation levels, like psychologists are

doing with IQ values. Intelligence in the context of SAP S/4HANA is not just an end in

itself; rather, it’s about increasing automation toward an autonomous ERP system to

reduce the total cost of ownership (TCO), such as through faster process runtimes or

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4

optimized resources consumption. Thus, the following equation applies: the higher the

level of automation of a business process or a system, the higher the level of intelli-

gence is.

In this section, we introduce a methodology for how to measure intelligence of busi-

ness processes provided by SAP S/4HANA and describe both the business and technol-

ogy perspectives.

Methodology

SAP S/4HANA operates as a central system for an organization’s business processes.

Key questions for defining automation levels center on understanding the common

structure of all those business processes. As illustrated in Figure 4.1, for a business pro-

cess, there are four dimensions to be considered for automation. This is an industry

standard that’s transformed for ERP software.

Figure 4.1 Methodology for Operationalizing Intelligence

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Each dimension is rated from 1 (low) to 5 (high) for its level of automation. By determin-

ing the level of automation for each dimension for a given business process or a sys-

tem, the overall level of automation can be detected. Thus, the current and target level

of intelligence can be determined, and an execution plan can be defined for making the

business process more intelligent.

Data acquisition is the process of inputting data into SAP S/4HANA using devices such

as a keyboard, scanner, disk, or voice. The following qualities could be used for deter-

mining the different automation levels for data acquisition:

1. Manual entry by user

2. Manual entry and data integration

3. Data integration and manual entry (exceptional)

4. Conversational AI and data integration

5. AI-based data extraction and integration (e.g., PDF document is transformed to

structured data and entered by robotic bot)

Information analysis is the process of studying and interpreting the data for meaning-

ful findings. The following qualities could be used for determining the different auto-

mation levels for information analysis:

1. Descriptive (what happened)

2. Diagnostic (why did it happened)

3. Predictive (what will happen)

4. Prescriptive (what should we do)

5. Cognitive (autonomous self-learning analysis of happenings)

Decision-making is the process of selecting a logical choice from the available options

by considering the impacts. The following qualities could be used for determining the

different automation levels for decision-making:

1. User makes decisions manually

2. User consumes system events and changes for decisions

3. System provides relevant information to the user for making decisions

4. System actively evaluates and recommends decisions

5. System autonomously makes decisions that are traceable and auditable

Action execution is the process of enforcing instructions to achieve a specific goal. The

following qualities could be used for determining the different automation levels for

action execution:

1. User performs actions manually

2. User consumes system events and changes for performing actions

3. System provides relevant information to the user for performing actions

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4.1 Introduction

4

4. System actively evaluates and recommends actions

5. System autonomously perform actions that are traceable and auditable

To deepen your understanding, let’s apply this methodology to the following sales per-

formance use case: A sales plan is a strategy that sets out revenue goals and identifies

the steps to meet those targets. Developing such a sales plan is usually a manual process

of analyzing historical data in order to forecast the revenue as a key figure. The situation

could be improved by applying machine learning to predict the forecast in terms of how

sales could develop in the future. Thus, the manual effort for sales planning can be

decreased and the actual sales volume can be increased by providing better insights to

take action where needed. Figure 4.2 depicts the application of the approach to deter-

mine the current and target levels of intelligence for the explained sales performance

scenario. As you can see, with predicting the sales revenue based on machine learning,

the automation level of the dimensions decision and action making are increased. How-

ever, the level of automation for the dimensions data acquisition and information anal-

ysis remain unchanged because no additional intelligence is infused into them.

Figure 4.2 Example of Applying Methodology

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Intelligence level ofsales performance(target)

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Business View

Businesses of all sizes aim to increase their top line and bottom line by using a modern

ERP system like SAP S/4HANA. Increasing levels of intelligence within business pro-

cesses support them in achieving these goals by generating new insights from data or

by enabling data-driven automation. However, the individual business value of adding

intelligence is dependent upon the context or the use case in which it is used. For

example, already an incremental improvement through rule-based approaches can be

perceived as more intelligent compared to the status quo, thus adding more business

value. The proposed framework provides the foundation for an objective discussion

with various stakeholders about the current and future state of a business process and

the spectrum on which business value can be generated.

Technology View

To make a business process more intelligent, first the current level of automation

based on the described methodology should be determined. This is the foundation

from which the target level can be derived by solution managers based on business

needs. The next question is how to achieve the defined level of automation per dimen-

sion. There is not just one tool but various concepts and technologies to achieve the

specified intelligence level for the dedicated dimensions.

However, as shown in Figure 4.3, categorization of techniques can be provided for real-

izing different levels:

� Manual

� Rule-based

� Self-learning

Business processes can be run manually without any kind of automation. To overcome

this, various rule-based techniques can be applied to increase the level of automation

and intelligence. Let’s consider a few rule-based examples:

� An ABAP report performing input or process validations and displaying error mes-

sages with resolution instructions to the user

� A workflow for performing individual tasks/decisions moving from one step to the

next until a predefined process is complete

� An insight-to-action analytics scenario starting with a key performance indicator (KPI)

tile notifying the user about drifting trends, performing root-cause analysis and exe-

cuting corrective actions

� A situation-handling application informing the user about a problem, providing the

necessary data for resolution, and recommending actions

When it comes to achieving level 5, rule-based approaches are no longer sufficient; they

must be extended to self-learning techniques. These techniques make sense of raw

data and discover hidden insights and relationships by learning from that data, rather

than programming explicit rules. Some examples include the following:

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4

� Deep learning for image recognition

� SAP Conversational AI for natural language processing (NLP)

� Bots or intelligent applications based on machine learning models for autono-

mously making decisions and performing actions

Figure 4.3 Categories for Automation Technologies

In context of SAP S/4HANA, rule-based technologies are well known, so in this book

we’re focusing on self-learning methods based on machine learning and predictive

analytics in order to increase the intelligence level of business processes.

The described approach provides a framework to derive a strategic direction for SAP

S/4HANA in terms of making it more intelligent. To infuse SAP S/4HANA with more

intelligence, additional business processes must be moved to level 4 and 5, targeting an

autonomous ERP system. Thus, the overall intelligence of SAP S/4HANA is reflected by

the level of the intelligence of the provided business processes.

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Rule-based

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4.2 Architecture Overview

In this section, we explain which machine learning application patterns have been

identified in the context of SAP S/4HANA. These are basically the underlying use cases

that must be solved with the proposed architecture. We also describe the guiding prin-

ciples behind the solution architecture.

4.2.1 Machine Learning Application Patterns

For defining the solution architecture, it’s crucial to understand which technical capa-

bilities are required for the implementation of the machine learning scenarios. Thus,

the machine learning use cases were first analyzed concerning this matter. In this con-

text, several machine learning application patterns were identified, and those will be

described in this section. The strategic direction is to provide for each pattern a uni-

form concept and framework for implementation. Thus, the machine learning applica-

tion patterns can be applied as reusable building blocks by development teams to

accelerate the implementation of machine learning use cases.

Matching

Matching assigns relationships and detects similarities and anomalies in a given data-

set. As an example scenario, say that as a master data specialist, you want to reduce the

number of duplicates in the system during consolidation. While manual matching is

very time-consuming for users, intelligent systems can significantly speed up match-

ing decisions by using machine learning methods. The system can present one or more

strategies and their quality to link similar objects. Users then only need to approve or

reject the suggestions or adjust them to their needs. Matching is needed when at least

two artifacts can be linked together due to a degree of similarity.

The process of matching follows a set of rules, which can be dynamically adjusted

(“learned”) by the system. The learned rules can change over time due to user input or

other triggers. Matching can be applied on different content types, including the fol-

lowing:

� Text (e.g., search and replace) or images (e.g., find all dogs in a set of pictures)

� Audio (e.g., natural language processing, when an audio stream is matched to a

query)

� Video (e.g., find out which company logos appear and how often during a football

game)

� Complex business objects (e.g., matching of invoices to good receipts or finding a

duplicate of a customer)

The content to be matched highly influences the type of output and its presentation.

One aspect of matching is the quality of a match. This means that objects can fully or

only partially match together. A full match is given when all defined parameters are

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met, whereas a partial match finds only some of the required parameters to fit. The

more parameters match, the better the quality of a match is. The following matching

types have been identified so far:

� Relationship matching

Logically connects objects of different types—for example, matching multiple

invoices to one payment

� Compatibility matching

Fits objects of different types but with shared properties together to form a com-

plete system—for example, assembly of a high/medium/low-end computer (a com-

puter consists of several components, such as a motherboard, a CPU, memory, and a

display, which have to fit together—and, for instance, the CPU fits only into mother-

boards with a specific socket)

� Similarity matching

Merges similar objects of the same type into one—for example, merging multiple

similar business partners because they’re the same type of object

For developing matching patterns, commonly used algorithms include multiclass clas-

sification algorithms like XGBoost/multilayer perceptron, clustering algorithms like K-

means, and nonparametric methods like the k-nearest neighbors algorithm.

Recommendations

Recommendations propose datasets or actions based on the current context. For

example, as a material requirements planner, you want to see potential solutions for

resolving a material shortage issue. Intelligent systems can help users by recommend-

ing appropriate content or by suggesting an action or input the user may prefer. In this

case, we speak of a recommendation pattern and its impact on the UI. We differentiate

among three types of recommendations:

1. Content recommendations

The system filters the content down to what may be interesting for the user, based

on the user’s behavior or the content characteristics. Examples of typical content

recommender systems include Amazon and Netflix.

2. Input assistance

The system assists the user by entering data or filtering data. Typical examples might

be a search phrase suggestion, an appropriate form template, or a set of suggested

default values for certain fields, based on the user input and interaction history.

3. Solution proposals

The system supports users working on complex problems by recommending spe-

cific actions or proposed solutions. In some use cases, this might be combined with

a simulation of the possible outcome. Typically, solution proposals involve various

decision-support systems. Examples use cases include payment and invoice match-

ing, and material shortage scenarios.

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As a prerequisite for the implementation of recommendation patterns, you must have

historical data concerning actions performed and inputs provided during business

processes. For the recommendation type solution proposal, the logging of business

processes is mandatory; for content recommendation, the necessary historical data

might be derived from application data. For the recommendation type, input assis-

tance in addition to texts/descriptions could be required.

For recommendation patterns, commonly used algorithms include social analysis,

multiclass classification algorithms like XGBoost/multilayer perceptron, text analysis/

mining, or the recurrent neural network (RNN).

Ranking

Ranking distinguishes between relevant and less relevant datasets of the same type in

relation to the current context. For example, as a purchaser, you want to see the top

suppliers for a specific product in the context of a given purchasing request. Ranking

simplifies complex decisions for business users by showing the best options first.

Items in a group are ranked by comparing criteria that are relevant for the user’s busi-

ness context, such as an amount, priority, or score. In a ranked table or list, the results

are always sorted to show the most highly ranked items on top. We differentiate

between two types of ranking:

1. Ranking by inherent value

Ranking is based on a value that is already available in the existing dataset, such as

the price. The value is typically known and understood by the user and requires no

further explanation.

2. Ranking by score

Ranking is based on a calculated grade, mark, or score. In this case, users might need

to understand the calculation behind the score, especially if machine learning meth-

ods are used.

Although you can rank a list of items based on their rating, ranking and rating are two

different concepts. A rating places a single item on a predetermined scale—for exam-

ple, rating your service provider on a scale from 1 (very bad) to 5 (very good). Ranking is

always about comparing a common value across a group of items. In the UI, rankings

are usually shown for a list or group.

For developing relevance and ranking patterns, commonly used algorithms include clas-

sification algorithms like XGBoost and clustering algorithms like K-means/the gaussian

mixture model, as well as nonparametric methods like k-nearest neighbors algorithm.

Predictions

Predictions forecast future data and trends based on patterns identified in past data,

taking into account all potentially relevant information. For example, imagine that as

a master data manager, you want to estimate the potential number of change requests

a team will need to process in the next quarter to balance the workload. Intelligent

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systems based on predictive models significantly reduce the cost required for compa-

nies to forecast business outcomes, environmental factors, competitive intelligence,

and market conditions.

There are two main classes of predictive models: parametric and nonparametric. The

features of these can be combined to form a third class, semiparametric models. Usu-

ally, parametric models make specific assumptions with respect to one or more of the

population parameters that characterize the underlying distribution. Nonparametric

models typically include fewer assumptions about structure and distributional

arrangement but often encompass strong assumptions about independencies.

Some algorithms for predictive models include ordinary least squares, generalized lin-

ear models (GLM), logistic regression, random forests, decision trees, neural networks,

and multivariate adaptive regression splines (MARS). Each of those algorithms has a

particular use and answers a specific question or uses a certain type of dataset.

Categorization

Categorization assigns datasets to predefined groups (classes). For example, as a service

agent, say you want to classify the priority of incoming requests (high/medium/low)

based on their content to improve customer service. Categorization can also discover

new groups (clusters) in the datasets, such as grouping customers into segments for

appropriate product offerings, targeted marketing, or fraud detection.

Categorization is a complex task in which intelligent systems can help increase the

level of automation by applying machine learning methods of classification and clus-

tering. Those are used for the categorization of objects into one or more classes and

clusters based on their features. Classification and clustering are similar processes, but

there is one minor difference. For classification, there are predefined labels assigned to

each input instance according to its properties; in clustering, those labels are missing.

Because classification uses labels, training and testing datasets are required for verify-

ing models. This is not necessary for clustering. Usually classification is more complex

than clustering because there are many levels in classification, whereas only grouping

is done in clustering.

For developing categorization patterns, commonly used algorithms include classifi-

cation methods like XGBoost, neural networks like CNN/RNN/GAN, dimension-reduction

algorithms like principal component analysis, and clustering algorithms like K-means/

the gaussian mixture model.

Conversational AI

Conversational AI interacts with a system based on natural language conversation and

enables a hands-free paradigm. For example, as a purchaser, say you want to create a

purchase order by talking with the system. Being able to have a conversation with a dig-

ital assistant to complete your business processes is a key part of the user experience

for an intelligent application.

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SAP Conversational AI understands typical natural language patterns to search for

business entities using various parameters, look up a specific business entity by name

or ID, retrieve the value of an attribute of a specific business entity, and create simple

new entities, including line items. SAP Conversational AI enables a humanized user

experience for applications to get tasks done in the context of the business data. Thus,

interaction with applications in natural language is supported. Creation of business

objects with information prepopulated from a conversational context is ensured. The

SAP Conversational AI technology supports sharing of notes, screenshots, and business

objects with other users within the context of a conversation. The ability to synthesize

business transactions from multiple SAP applications at a single point of interaction is

enabled with this technology. Customer skills for using a digital assistant can be cre-

ated and deployed across applications and channels.

4.2.2 Guiding Principles for Solution Architecture

The defined solution architecture will follow the key guiding principles of SAP S/4HANA,

which we’ll describe in this section, as depicted in Figure 4.4.

Figure 4.4 Guiding Principles for Solution Architecture

One model

AnalyticTrans., …

Planning,mobile

Search,MDM

AnalyticTrans., …

Pmobile

Uniform in cross

Authori-zation, …

UIIntegration

Exten-sibility

Authori-zation, …

Integration

Multiple deployment

Privatemgmt.

On-premise

On-demand

Privatemgmt.

premise

Close to data

Deeplearning

Reg-ression

Classi-fication

Deeplearning

ression

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Let’s walk through these principles:

� One model/uniform in cross

Often the core data model is defined numerous times for different purposes only

because minor metadata must be added. For example, technology solutions for inte-

gration, user interface (UI), analytics, or transactions unnecessarily require their own

specific business object models. This increases the total cost of development (TCD)

as the same content must be provided again and again. Due to incompatible meta-

models, cross topics like UI integration, extensibility, and authorization must be

solved various times. This results in a high TCO and makes the consumption of solu-

tions difficult for customers. Therefore, one core data model will be defined and be

reused in different contexts by domain-specific enhancements.

The data model of SAP S/4HANA is based on core data services (CDS). Thus, redun-

dant data modeling is avoided and cross topics are solved uniformly. For machine

learning, this data model will be reused as much as possible.

� Multiple deployments

SAP S/4HANA supports multiple deployment options. Therefore, the defined solu-

tion must be invariant regarding different deployment options and work in on-

premise, privately managed, and public cloud environments. This must be ensured

also for machine learning applications.

� Close to data

In the context of machine learning, one golden rule is to bring the algorithms to the

data and not vice versa. Algorithms consist of minor lines of code and are usually

self-contained. In contrast, application data has high volume and holds numerous

dependencies. Thus, replication or extraction of application data is usually a very

complex and TCO-intensive task (e.g., handling of deltas, meeting performance re-

quirements, interpreting the data semantically) and should be avoided.

4.2.3 Solution Architecture

Improved processing power, better algorithms, and the availability of big data are facil-

itating the implementation of machine learning functionality in SAP S/4HANA. Fur-

thermore, SAP S/4HANA’s underlying in-memory platform, SAP HANA, increases speed,

combines analytical and transactional data, and brings innovation with embedded

machine learning capabilities. With SAP Data Intelligence, additional features are inte-

grated into SAP S/4HANA and the platform covers the entire spectrum, ranging from

simply consuming intelligent services to training and deploying its own machine

learning models.

Use cases like ranking, categorization, and prediction can be solved with classic algo-

rithms like classification, clustering, regression, or time series analysis. Usually those

algorithms do not allocate much memory and CPU time. Thus, they can be imple-

mented within the SAP S/4HANA stack, where the application data for model training

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and the machine learning consuming business processes also are located. This embed-

ded machine learning architecture has very low TCO and TCD. As illustrated in Figure

4.5, the embedded machine learning architecture is based on the SAP HANA machine

learning provided with the Predictive Analysis Library (PAL) and the Automated Predic-

tive Library (APL) as the necessary algorithms.

Figure 4.5 Overall Solution Architecture

Use cases like image recognition, sentiment analysis, and NLP require deep learning

algorithms based on neuronal networks. For model training, usually these kinds of algo-

rithms demand a huge volume of data and graphics processing unit (GPU) time. There-

fore, these kinds of scenarios are scaled out to SAP Data Intelligence to keep the load in

the transactional SAP S/4HANA system low. For this side-by-side machine learning

approach, the requested data—images, audio, text documents, historical data, applica-

tion logs—are typically stored not in SAP S/4HANA but rather in the business data lake.

In general, the consumption of a trained model is based on remote interfaces. However,

for mass processing those interfaces must be bulk-enabled, or local deployment of infer-

ence models must be provided.

Predictive analytics uses machine learning to automate data preparation, insight dis-

covery, and insight sharing for business users, operational workers, and data scientists.

SAP Analytics Cloud provides predictive analytics features, also known as smart fea-

tures. These features are dedicated to uncovering insights, understanding root causes,

SAP S/4HANA

SAP HANAmachine learning

Applicationdata

SAP Cloud Platform

Data integrationfor model training

Machinelearning service

consumptionMachine learning

application

Intelligent SAP Fiori/SAP Conversational AI/Explorational UI

SAP Data Intelligence

Pipelineengine

Deeplearning/

GPU

Datascience

tools

Monitor/operate

Application content: machine learningscenarios/pipelines

SAP Analytics Cloud

Smartpredict

Smartdiscover

Smartinsights

Searchinsight

Application content: models/stories

Business logic Predictiveanalytics

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and predicting what’s likely to happen in the future. This ensures decision-makers have

access to the right information at the right time to influence the best possible out-

comes. The focus here is on smart predict, which is an explorative approach for unfore-

seen, exceptional, and irregular needs for machine learning methods.

A conversational UI for natural language interaction with SAP S/4HANA is enabled by

SAP Conversational AI. This is a self-learning solution using machine learning func-

tionality to gain knowledge based on historic data and experience. Machine learning

requires additional visualization capabilities in the UI, such as for illustrating confi-

dence intervals or forecasting charts. Thus, to embed machine learning capabilities in

UIs, intelligent SAP Fiori elements are used.

In the following sections, we’ll provide more details about embedded machine learn-

ing, side-by-side machine learning, and predictive analytics architecture. The focus

here is on the development architecture; the data science tasks are not within the scope

of this book.


Embedded machine learning is an appropriate approach for use cases like ranking, cat-

egorization, and prediction, in which classic algorithms such as classification, cluster-

ing, or regression are sufficient for the implementation. The SAP S/4HANA platform

contains with SAP HANA machine learning libraries that can be used for the develop-

ment of embedded machine learning scenarios without the need to move application

data. For a given key question to be solved with machine learning, data scientists per-

form experiments and explorations to determine the required algorithms and the data

attributes for model training. Those are the input for the development of the machine

learning use case that is the focus of this section.

As illustrated in Figure 4.6, the solution is based on two main architecture decisions:

using CDS views and making use of the machine learning techniques provided by SAP

HANA.

The data persistency in SAP S/4HANA is represented by application database tables. On

top of these tables, a virtual data model (VDM) is implemented using CDS views. One

purpose of these VDM views is to hide the cryptic database model and to provide a

reusable semantic layer that can be consumed in different scenarios, such as analytics,

planning, search, or transactions. At runtime, the CDS views are consumed via the ana-

lytical engine or the Service Adaptation Description Language (SADL), which is part of

the ABAP application server. Those frameworks evaluate the metadata of the CDS views

in terms of annotations to enable the required functionality for the business process,

such as hierarchy handling or analytics capabilities. Out of the ABAP CDS views, the SAP

HANA SQL views are generated so that SQL operations can be pushed down to SAP

HANA for optimal performance.

4 Architecture

98

The algorithms for embedded machine learning can be performance-intensive as

high volumes of application data must be processed. Thus, for performance optimi-

zation, the algorithms should be processed close to the application data. SAP HANA

provides PAL and APL to offer statistical and data mining algorithms. In addition,

specific algorithms can be implemented if required. As shown in Figure 4.6, those algo-

rithms are invoked and orchestrated by the modeling and administration component,

which is explained later in this section.

Figure 4.6 Embedded Machine Learning Architecture

The algorithms require application data as input for model training. The VDM, with its

SQL views and the application tables, can be reused for this purpose. However, in con-

trast to the ABAP CDS views, not all metadata is available. This drawback can be elimi-

nated once the ABAP CDS views can be transformed to SAP HANA CDS views. The

trained models are exposed to business processes by wrapping them with CDS views.

The details of this integration are described in the next section. The CDS views for

machine learning can be combined with other VDM CDS views and can then be exposed

to the consumers. By consuming machine learning models through CDS views, existing

content (e.g., VDM views) and concepts (e.g., authorization, extensibility, UI integration)

are reused. This results in a simple and very powerful solution architecture.

The modeling and administration tool illustrated in Figure 4.6 was previously known as

the Predictive Analytics integrator (PAi). As of the 2020 SAP S/4HANA release, it is re-

branded as the intelligent scenario lifecycle management (ISLM) framework. Its purpose

InA, MDX

Built-inconsumer

SAP S/4HANA

SAP HANA

Applicationtable

Explorativeconsumer

SQL view

Machine learningmodel

SAP HANA machinelearning library: PAL/APL

CDS view/AMDP classfor machine learning

CDS view

Analytical engine/SADL

Ma

chin

e le

arn

ing

serv

ices

Training andmonitoring

OData

99


4

is to provide a common interface for the consumption of machine learning models inde-

pendent of the underlying predictive engine. The intention is to harmonize the manage-

ment of machine learning models and to provide a simple common interface to allow

applications to interact with different types of supported machine learning libraries

without the requirement for applications to develop machine learning engine-specific

code. Consumer applications interface only with application programming interfaces

(APIs) and do not interact with low-level machine learning libraries.

The ISLM architecture is shown in Figure 4.7. The ISLM framework contains information

about the installed SAP HANA libraries. It provides a repository for machine learning

models that includes, for example, information concerning model types (e.g., regres-

sion, classification, time-series), model data sources (e.g., tables, views), model training

data, and model quality figures to allow for assessing metrics and supporting model

comparison. The ISLM framework also provides a pluggable infrastructure and adapters

for APL and PAL. In addition to hiding the complexity of machine learning libraries

from application development, the ISLM framework supports the lifecycle manage-

ment of the involved artifacts in terms of transport within the system landscape, deliv-

ery, and upgrade mechanisms. Furthermore, it provides configuration capabilities for

model training based on the customer data in their development landscape. This frame-

work is going to be explained in detail in Chapter 5.

Figure 4.7 ISLM Architecture

Machine learning models are stored in SAP HANA and exposed by generated SQL script

procedures or scripted calculation views. But how are those SAP HANA entities con-

sumed from the ABAP application server?

Various techniques are provided by the ABAP application server to access the underly-

ing database system. As already mentioned, existing content and concepts are reused

SAP HANA


Modeling andadministration

Otherembedded

engines

ISLM engine adapter ISLM repository

APLembedded

engines

SAP HANA API(SQLScript procedure/scripted calculation view)

4 Architecture

100

wherever possible. Therefore, CDS views and CDS table functions are the appropriate

technologies to consume the machine learning models stored in SAP HANA, as shown

in Figure 4.8. SQL script procedures are used to wrap the machine learning models on

the SAP HANA level. Those SQL script procedures can’t be invoked by standard CDS

technology but can be with CDS table functions. These are used for breakout scenarios.

They are data dictionary objects and are defined via data definition language (DDL)

sources, but in contrast to CDS views, they are not database-independent; they repre-

sent native SQL script functions. These functions are implemented using the tech-

niques of ABAP Managed Database Procedures (AMDP)—that is, via ABAP with an

implementation in SQL script. On top of the machine learning models exposed via SQL

script procedures, corresponding CDS views for machine learning are defined by using

CDS table function technology. The CDS table function represents the input and output

structure of the SQL script procedure that is called by the AMDP implementation of the

CDS table function. The provided CDS view, and respective CDS table function, can be

combined with other VDM views and consumed accordingly. For this purpose, OData

services can be generated automatically. Existing CDS annotations can be used.

Figure 4.8 ABAP Integration

SAP S/4HANA

SAP HANA

Application table

SQL view


ISLM: repository, engineadapter and API

CDS tablefunction

CDS view

SQL scriptprocedure

ABAP class

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4

APL has built-in data science steps like feature engineering and detection of adequate

algorithms. Therefore, this library is very easy to use und results in very low TCO and

TCD. However, the algorithms are restricted to classification/regression, clustering,

time series analysis, and recommendation models. Thus, for breakout scenarios in

terms of using other algorithms or fine-tuning the models, PAL is used; it provides

more than 100 machine learning algorithms. Figure 4.9 illustrates how this library is

integrated for embedded machine learning.

Figure 4.9 PAL Integration

For development efficiency, for each PAL algorithm, a template in the form of an AMDP

class is provided. This class implements predefined interfaces and offers standardized

methods for training and consumption of models. For extensibility, business add-in

(BAdI) enhancement points are provided for those methods. Application developers

copy and adopt those templates for specific machine learning scenarios. CDS views as

inputs for model training are defined. Consumption and integration of the machine

learning models into the business processes is based on the method of the AMDP class.

Concrete machine learning model instances are available once the customer triggers

training via the training application.

The following qualities are provided by this solution architecture using PAL:

� Seamless integration into the SAP S/4HANA programming model

� Reuse of existing SAP S/4HANA concepts (e.g., authorization, UI integration), CDS

view content, and tools

� Powerful extensibility based on enhancement points and BAdIs from start due to

reuse of the transactional programming model

� Rich set of PAL algorithms and usage of SAP HANA text analysis

� Supports breakouts per definition, such as orchestration of multiple algorithms or

data transformations

SAP S/4HANA

SAP HANA

PAL library

TemplateAMDP class

Input CDSview

Applicationdata


Training app

Train

Read

Machine learninglogic AMDP class

Machine learningapplication

Consume

Copy

4 Architecture

102

� Lifecycle management and operations with SAP S/4HANA ABAP tools and concepts

� GDPR compliance by design; no data transfer

� On-premise and cloud deployment ensured due to one-code-line approach

Lifecycle management and ABAP integration of PAL models are also based on intelli-

gent scenarios, as depicted in Figure 4.10. The details of lifecycle management will be

described in Chapter 5, Section 5.5.1.

Figure 4.10 PAL Model Training

As already mentioned, PAL algorithms are integrated via AMDP technology from SAP

HANA into ABAP. The AMDP class implements an interface with methods for training

and consumption. The intelligent scenario is created for the PAL-based use cases, too.

An intelligent scenario is a business application-specific machine learning design time

artifact that includes metadata about an application data source for training (e.g., table

or view), an algorithm type (e.g., regression or classification), and a consumption API.

The AMDP class for the PAL algorithms is registered in an intelligent scenario. Because

the generic training application is based on intelligent scenarios, it triggers the training

method of the AMDP class and saves the trained model. Machine learning applications

can consume the trained model via the AMDP class.

4.4 Side-by-Side Machine Learning

The journey toward the intelligent enterprise is enabled by the interaction among

three main pillars: data, intelligent algorithms, and business processes. Nowadays, en-

terprises have big and diverse enough data assets to feed intelligent algorithms, which

SAP S/4HANA

Machine learninglogic AMDP class

Intelligent scenario

Training app


Register

Consume

Trained model

Train

PAL library

Consume

103


4

can be used to innovate critical business processes, which will then produce even more

fine-grained contextualized data, feeding a virtuous cycle. Embedded machine learn-

ing targets scenarios in which the business and machine learning logic reside in the

SAP S/4HANA platform, but the term side-by-side machine learning is used in the fol-

lowing use cases:

� SAP S/4HANA machine learning based on SAP Cloud Platform

The SAP S/4HANA application and the according business logic are based on SAP

Cloud Platform. Such applications should consume the required machine learning

services directly from SAP Data Intelligence and SAP HANA machine learning, fol-

lowing the rule of bringing the algorithms to the data.

� SAP S/4HANA machine learning based on ABAP application server

The SAP S/4HANA applications and the related business logic are based on the SAP

S/4HANA platform. However, the required machine learning capabilities are not

available on SAP S/4HANA—for example, image and language recognition, or senti-

ment analysis. These features are consumed from SAP Data Intelligence remotely or

based on an exchange of trained models.

Side-by-side machine learning fits use cases like image recognition, sentiment analysis,

or NLP that require deep learning algorithms based on neural networks. These kinds of

algorithms are very demanding regarding consumption of system resources. They usu-

ally require huge volumes of data and GPU time for model training. Thus, to keep the

load in the transactional SAP S/4HANA system low and ensure acceptable runtime for

the business processes, side-by-side machine learning scenarios are scaled out to SAP

Data Intelligence. This infrastructure also complements the overall solution in which

specific algorithms are not provided on the SAP S/4HANA platform, classic methods

(e.g., regression, classification) consume too many resources of the transactional sys-

tem, or huge volumes of external data (e.g., Facebook, Twitter) are required for model

training. In particular, SAP S/4HANA extension applications should consume SAP Data

Intelligence capabilities because the application data and business processes are founded

on SAP Cloud Platform. Thus, the golden rule of bringing the algorithms to the data is

followed.

So because side-by-side machine learning scenarios are based on SAP Data Intelligence,

the question arises: How do we integrate this technology into SAP S/4HANA in terms of

model training and inference? The answer to this question is given next.

SAP Data Intelligence is an important component of the side-by-side machine learning

architecture. It’s designed for the cloud and deployable in any cloud, hybrid, or on-

premise landscape that addresses the entire end-to-end lifecycle for creating value out

of data, combining enterprise AI with intelligent information management, and

enabling enterprises to effectively operationalize machine learning and data science

on complex and real enterprise landscapes. We introduced SAP Data Intelligence in

Chapter 3, Section 3.3.1.

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SAP Data Intelligence handles how customers discover, refine, govern, orchestrate, and

scale efforts in getting intelligence out of data assets they own or those to which they

need to connect. This covers all the data management use cases that deal with different

kinds of data (structured, unstructured, streaming), different integration approaches

(batch, real-time, near-real-time), or different processing patterns (offline, online, lamb-

da). Key capabilities include the following:

� Data connectivity and orchestration

Leverage central connection management to connect to data wherever it resides—

on premise or in the cloud—regardless of the data type—structured, unstructured,

streaming—and integrate it with flexible data pipelines. Orchestrate data process-

ing across highly distributed and heterogeneous landscapes, executing any SAP or

non-SAP processing engines close to the data sources to minimize the amount of

data to be moved or replicated.

� Data governance and cataloging

Access an advanced metadata management system/catalog, enabling data lineage,

data quality, profiling, data discovery, and searching of datasets to ensure auditabil-

ity and governance. This gives IT team members the flexibility and control they

require to ensure trusted and accurate data is easily discoverable by the teams that

need it, all integrated within a single solution.

� End-to-end lifecycle management of machine learning models

Streamline data science and machine learning projects, from modeling and develop-

ment to operations, across all enterprise data assets to manage the end-to-end life-

cycle. A central repository enables versioning and a tailored lifecycle management

process for machine learning projects.

Support data discovery, access and preparation, and experimentation in Jupyter

Notebook; leverage a library of pretrained models for the most common functional

services; and support deployment, (re)training, serving, and monitoring of all mod-

els. Access ready-to use, adaptable business content in terms of operators and tem-

plates. Finally, SAP Data Intelligence provides an environment for model deploy-

ment and operation, a means to integrate results back into an application or employ

delayed consumption, and continuous testing and maintenance of all models in

production.

� One integrated solution

SAP Data Intelligence includes data pipelining, orchestration, machine learning, and

metadata cataloging in a single solution. This is very valuable: all hyperscalers have

different services for these functionalities, which have to be integrated, while the

main pure players and niche players focus only on a subset of these areas.

� Hybrid and multicloud deployment

SAP Data Intelligence is available both as a service in SAP Cloud Platform and as a

bring-your-own-license product. It has been natively built on Kubernetes since its

initial design, allowing it to deploy the very same solution in any hyperscaler, pri-

vate cloud, or on-premise data center.

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4

� Native integration capabilities

Besides reusing all relevant open-source technologies and open standards, SAP Data

Intelligence is also capable of natively integrating and reusing SAP data sources and

engines. SAP Data Intelligence pipelines can natively integrate into ABAP applica-

tions; orchestrate SAP Business Warehouse (SAP BW) process chains, SAP Data

Services jobs, and SAP HANA flowgraphs; execute SAP HANA streaming analytics

scenarios; and integrate into all SAP Cloud Platform applications.

Many of these capabilities may exist today in a customer’s information management

landscape, but they are typically offered in a myriad of different ways across several

disparate toolsets that require different skills and different frameworks, whereas SAP

Data Intelligence provides a single, comprehensive way to manage all data types cohe-

sively and intelligently.

SAP Data Intelligence is designed to be used by different user profiles throughout the

enterprise. From a business user with technical affinity to developers and data scien-

tists, there are modules, services, and tools for all levels. The typical lifecycle SAP Data

Intelligence supports comprises the following phases:

1. Data management

With a comprehensive set of intelligent information management capabilities, it’s

possible to manage the data that drives the entire AI process, making it possible for IT

enterprise architects, data engineers, and data management professionals to elimi-

nate data silos and ensure that the data needed for data science teams is made avail-

able in a governed manner. With tools to help profile, prepare, and merge data, data

science teams can rapidly get to the datasets they need to move on to the next phase.

2. Experimentation

For data scientists developing models, SAP Data Intelligence provides the tools they

are experienced with, like Jupyter Notebook, and the frameworks they require, like

R, Python, or TensorFlow. Once provisioned, data science teams can work in a Jupy-

ter Notebook environment to develop models using open-source frameworks and

SAP frameworks (like APL and PAL), pretrained services, and a visual pipeline GUI to

orchestrate data ingestion, training steps, or multiple models. These experiments

for a specific business problem are put under version control so teams can explore

various potential solutions and models and easily test and iterate until they land on

a model they would like to pursue in production.

3. Productization

Models can rapidly be delivered into production by packaging the required assets

(like pipelines and model assets) into a machine learning scenario. In production, a

team responsible for machine learning operations can easily take over and connect

production data to the new scenario, (re)train, deploy models to model servers, inte-

grate with business applications, and start generating insights. Once in production,

models can be centrally managed from a single cockpit in which the ongoing testing,

retraining, and quality of those models can be monitored.

4 Architecture

106

Models and pipelines can be reused, recombined, and traced through the entire process

and to jumpstart new scenarios. This includes reuse of datasets as well. The machine

learning functionality of SAP Data Intelligence is used directly by data scientists

through a set of tools run as web applications either inside SAP Data Intelligence or

indirectly by applications that call representational state transfer (REST) APIs of SAP

Data Intelligence. Both tools and backends providing REST APIs are based on the sys-

tem application server, which helps to delegate aspects like user authentication and

authorization to the platform. Tools include design-time tools for data scientists, like

the open-source JupyterLab component and the SAP Data Intelligence pipeline mod-

eler. Moreover, there are tools for managing the lifecycle of a machine learning project

and the lifecycle of datasets used in a machine learning context.

As illustrated in Figure 4.11, SAP Data Intelligence provides a data lake for business data.

Thus, application data can be extracted from SAP S/4HANA for training machine learn-

ing models.

Figure 4.11 Side-by-Side Machine Learning Architecture

For pre- and postprocessing of the application data, the pipeline engine offers a graph-

ical programming model to create pipelines. A pipeline orchestrates data ingestion,

training, and inference activities. It consists of operators and data flows connecting

those operators. Operators can be predefined connectors to integrate into data sources

leveraging proven SAP technologies from SAP Data Services, SAP HANA smart data inte-

gration, SAP Landscape Transformation Replication Server (via an ABAP agent), freely

programmable options (like a Python operator), or serving operators for exposing a

REST endpoint. For machine learning cases, SAP Data Intelligence provides specific ma-

chine learning operators that call functional services (like for image classification) or

core services (for model serving and training) provided by SAP Data Intelligence or by

hyperscaler services. With the operator concept, SAP Data Intelligence is completely

SAP S/4HANA

SAP HANAmachine learning

Applicationdata

SAP Data Intelligence

Data integrationfor model training


Pipelineengine

Deeplearning/

GPU

Datascience

tools

Monitor/operate

Business data lake

Application content: machine learning scenarios/pipelines

Intelligent SAP Fiori/SAP Conversational AI

SAP S/4HANA machinelearning app

Machine learningservice consumption

Machine learningservice consumption

107


4

open to third-party frameworks a data scientist would like to work with. There is also

specific support for SAP HANA-embedded machine learning; that is, a connection to any

SAP HANA, PAL, or APL data stored in SAP HANA can be used from an SAP Data Intelli-

gence context.

The pipeline engine orchestrates complex data flow pipelines, is based on scalable

infrastructure provided by SAP Cloud Platform/Kubernetes, supports heterogenous

execution runtimes (e.g., R, Python, Spark machine learning), and enables connectivity

to SAP S/4HANA. Based on the application data, exploration and feature engineering is

performed by data scientists to define machine learning models. For this, common

data science tools like Jupyter Notebook and Python are supported. For deep learning

scenarios, a framework is provided that enables training on a GPU infrastructure.

To implement machine learning use cases, applications must define machine learning

scenarios and model pipelines. SAP Data Intelligence organizes each machine learning

use case by the artifact machine learning scenario. This bundles all design-time entities

to solve a machine learning business question. In addition, it is the bracket keeping

track of consumed and produced artifacts, like datasets and machine learning models,

as well as the metrics reported by these, and is thus the basis for machine learning sce-

nario lifecycle management. Thus, the machine learning scenario artifact contains all

development entities that are required for the implementation of a specific machine

learning use case.

Inference and training processes are developed as pipelines comprising sequential and

parallel tasks. In particular, for each machine learning scenario, a training pipeline is

provided, which receives the training data from SAP S/4HANA and processes the data

to train the algorithms for a specific use case. Structured data is handled by a CDS oper-

ator and stored, such as in SAP HANA, whereas unstructured data is managed by an

object store and big data storage. Often, saved application data is deleted after a train-

ing run. However, there are use cases with high retraining frequency, in which, after an

initial upload, the deltas periodically must be received and stored for the next training

run. The application data can be persisted. Alternatively, streaming based on continu-

ous data transfer without persistency can be applied. The training and inference pipe-

lines are exposed by REST services. Those are invoked by SAP S/4HANA applications

remotely and integrated into the business processes and UIs. Thus, machine learning

capabilities are provided as built-in functionality to the right person, in the right place,

and at the right time.

The operation and monitoring of machine learning models are managed with different

administration applications:

� The machine learning operations cockpit provides a view of currently deployed

models and their runtime KPIs and produced artifacts. It allows for manual activa-

tion of machine learning scenarios and calling the associated pipelines. It also sup-

ports landscape management, model configuration, and provisioning of machine

learning scenarios to other tenants.

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108

� Scenario scheduling allows automated calls to the pipeline API.

� The debrief cockpit provides data scientists with KPIs for the created inference pipe-

lines/models during the exploration and retraining phases.

Figure 4.12 drills into the details of the integration between SAP Data Intelligence and

SAP S/4HANA. On the SAP S/4HANA side, an intelligent scenario is the artifact corre-

sponding to the machine learning scenario on the SAP Data Intelligence side. An intel-

ligent scenario is also a design-time artifact that represents a machine learning use case

and contains metadata like the name and description of the use case. In particular, it

encompasses the ABAP class, which implements the consumption API of the machine

learning model.

Figure 4.12 SAP Data Intelligence Integration into ABAP

As already mentioned, in SAP Data Intelligence, a pipeline for model training and con-

sumption is provided for each machine learning use case. The training pipeline reads

the necessary application data based on the CDS extraction view, which is provided by

the SAP S/4HANA application. The pipelines are exposed via REST services and can be

invoked from SAP S/4HANA. The training REST service has a standardized signature

and can be called generically the ISLM framework. This component is in charge of the

lifecycle management of the machine learning models and provides capabilities like

scheduling of training jobs or monitoring. The signature of the REST services for model

consumption is scenario-specific (e.g., forecast revenue for sales orders or predicting

debt default risk) and is handled by the machine learning logic ABAP class shown in Fig-

ure 4.12. This ABAP class basically wraps the REST service into an ABAP API that can be

consumed by machine learning applications in order to integrate inference results into

business processes and UIs. Optionally, inference results can be cached for scenarios in

which performance optimization is required.

Figure 4.13 illustrates the details of the integration with a machine learning logic class

CL_ML_LOGIC.

SAP S/4HANA SAP Data Intelligence

Machinelearning scenario

Trainingpipeline

Inferencepipeline

Operators

Machinelearning library

CDS extractor

Intelligentscenario ISLM framework


Application data

Machine learninglogic ABAP class

Trainedmodel

R

Consume

R

R

Train, deploy,monitor

Register

R

Consume

R

Extractdata

109


4

Figure 4.13 Design of ABAP Machine Learning Logic Class

As described, the machine learning logic ABAP class is registered to an intelligent sce-

nario on the SAP S/4HANA side. To have a harmonized programming model across all

machine learning use cases, the ABAP class is standardized by implementing inter-

faces. Side-by-side machine learning scenarios have to register the machine learning

logic ABAP class in the ISLM framework via an intelligent scenario. During develop-

ment, changes to the intelligent scenario are expected. Therefore, the artifact is saved

as a draft initially. The draft status controls the transportation of the scenario registra-

tion content in the ISLM framework.

Once the intelligent scenario is finalized, it has to be published. The intelligent scenar-

ios need to implement the IF_ISLM_INTELLIGENT_SCENARIO marker interface to be identi-

fied as an intelligent scenario in the ISLM framework. This is a mandatory procedure for

side-by-side machine learning scenarios for implementation of the GET_SCENARIO_GUID

method. This method returns the corresponding machine learning scenario of SAP

Data Intelligence. Thus, the development artifacts between SAP S/4HANA and SAP Data

Intelligence are connected. The IF_ISLM_INTELLIGENT_SCENARIO interface defines the re-

quired behaviors of an intelligent scenario in the ISLM framework. This enables the

operational part of the intelligent scenario to be more concrete and stable. This imple-

mentation will be validated during the registration process.

+ CONSUME_INFERENCE ( )

CL_ML_LOGIC

+ GET_INFERENCE_CLIENT:IF_ISLM_INFERENCE_HTTP_CLIENT

+ CL_LMFRW_INFERENCE_PROVIDER (IF_ISLM_INTELLIGENT_SCENARIO)

CL_ISLM_INFERENCE_PROVIDER

Get inferenceclient

+ GET_SCENARIO_GUID : GUID

…

<<interface>>IF_ISLM_INTELLIGENT_SCENARIO

+ ADD_REQUEST_BODY (STRING)+ ADD_REQUEST_HEADER (HEADER_TABLE)+ SEND_AND_RECEIVE () : RESPONSE_DATA

…

CL_ISLM_INFERENCE_CLIENT

Handle inferencerequest

Implementscenario marker

+ GET_CHECKS_METADATA+ GET_CHECKS_PARAMETERS : PARAMETERS+ PERFORM_CHECKS : RESULT

…

<<interface>>IF_ISLM_PREREQUISTE_CHECKImplement

checks

4 Architecture

110

The CONSUME_INFERENCE method wraps the REST service for model consumption as an

ABAP API that is exposed to machine learning applications. The input consists of one or

more records for which inference results are provided based on the underlying

machine learning model.

It is difficult for customers to understand which technical and business prerequisites

are required to train and consume machine learning scenarios. For example, sufficient

data volume must be in place for training of machine learning algorithms. Also, under-

lying business processes must be activated and configured in order to have a meaning-

ful foundation for the training process. With an increasing number of machine

learning scenarios, such evaluation can no longer be performed by the customers man-

ually due to high TCO and huge complexity. Therefore, a prerequisite check capability

is required to automatically validate for each machine learning scenario whether the

necessary prerequisites for training and consumption are met. Intelligent scenarios

provide the necessary prerequisite checks by implementing the IF_ISLM_PREREQUISITE_

CHECK interface. Thus, the readiness and consistency of the machine learning use case

can be checked, such as whether enough data is available for model training. Those

checks are performed by the ISLM framework to evaluate whether the prerequisites for

model training are fulfilled.

To enable the ABAP class for customer extension, enhancement spots are provided.

Thus, customers can enhance the consumption logic or add specific transformations as

examples. Reusable routines are provided by the CL_ISLM_INFERENCE_PROVIDER and CL_

ISLM_INFERENCE_CLIENT utility ABAP classes. These classes support methods like getting

the currently activated model for inference, receiving the URL endpoint of a REST ser-

vice provided by SAP Data Intelligence, creating an ABAP REST client object based on

the REST service URL, triggering training for a machine learning scenario, or reading

monitoring data for a trained model. The interactions among those classes in the con-

text of training and inference processes are shown in Figure 4.14.

The training process is triggered by the ISLM framework for a selected intelligent sce-

nario. Because an ABAP class is registered for each intelligent scenario, the ABAP imple-

mentation for the selected intelligent scenario can be determined. Thus, the corre-

sponding machine learning scenario on the SAP Data Intelligence side can be read.

With the ID of the machine learning scenario, the URL for the training REST service can

be determined by invoking the necessary reuse routine of the machine learning utility

class. Once the URL for the training REST service is available, an ABAP REST client object

can be created based on the URL. This proxy object is used to call the training method

from ABAP and run the necessary job. The status of the training—whether the job was

successful or failed—is displayed by the ISLM framework for the end user.

The inference process is usually triggered by the machine learning application, as

shown in Figure 4.15. The API for the inference calls is provided by the machine learning

logic ABAP class, which is known for the machine learning application during design

time. Like the training process, the URL for the inference REST service is determined by

111


4

reusing the CL_ISLM_INFERENCE_PROVIDER machine learning utility class. Based on the

URL, a REST client object is created in ABAP. This proxy object is used to call the REST

service for inference. The input parameters for the inference call are passed by the

machine learning application. The calculated results are also processed by the machine

learning application—for example, integrated into business processes or illustrated in

the UI.

Figure 4.14 Training Sequence

The REST inference call is handled by the CL_ISLM_INFERENCE_CLIENT machine learning

utility class. As a first step, an HTTP client is created based on the URL of the REST ser-

vice. This proxy object is used to send the request and receive the according response.

The request message must be compiled by setting header and body attributes. The

response message must be transformed into ABAP structures for consumption by

business processes.

Scenario_ID

Get_Scenario_ID

Training_URL

Training_URL

Get_Endpoint

REST_Client

Model_Version

REST_Client.Train

Get_Endpoint (Scenario_ID, Training)

Create_REST_Client (Training_URL)

CL machinelearning logic

ISLM frameworkCL machine

learning utilitySAP Data

Intelligence

4 Architecture

112

Figure 4.15 Inference Sequence

Now that we’ve introduced the basic structure of the REST client, the question of how

to manage dynamic changes to the REST endpoints can be addressed. Machine learning

models must be retrained regularly to learn from changes to the data environment.

Each retraining results in a new machine learning model version with a new REST ser-

vice for inference. However, consuming machine learning applications usually breaks

if the underlying inference REST service is changed. The mitigation of this issue is cov-

ered by the machine learning utility class. From a technical point of view, new infer-

ence REST services result in new URL endpoints. The CL_ISLM_INFERENCE_PROVIDER

machine learning utility class dynamically determines the inference REST endpoints

and provides a stable interface with the machine learning applications. For this, the

request and response structure of the inference REST service must remain stable or

be enhanced compatibly.

As mentioned earlier, on the SAP Data Intelligence side, the machine learning scenario

and pipeline artifacts must be defined. The training and inference pipelines cover the

necessary machine learning logic. Pipelines are modeled graphically based on opera-

tors for transformation, validation, or incorporation of algorithms. The machine learn-

ing scenario constitutes a link among all development artifacts in SAP Data Intelligence

to resolve lifecycle management. The training and inference pipelines are exposed by

REST services to the SAP S/4HANA platform. The machine learning logic ABAP class is

required for wrapping those REST services and making them consumable by ABAP


CL machinelearning logic

CL machinelearning utility

SAP DataIntelligence

Result

Consume (Input)

Result

Inference_URL

Get_Endpoint

(Scenario_ID, Inference)

Inference_URL

Get_Endpoint

REST_Client

REST_Client.Consume (Input)

Create_REST_Client (Inference_URL)

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4

methods. Figure 4.16 illustrates the basic steps for the training and inference pipelines,

which can vary per use case.

Figure 4.16 Design of Training and Inference Pipelines

Readtraining data

(CDS extractor)

Applydata filter(optional)

Transformtraining data

Savetraining data

(optional)

Trainalgorithm

Savetrained model

Providemodel metrics

Deletetraining data(if no deltas)

Readtraining model

Applyinferencerequest

Provideinferencemetrics

Return inferenceresults (including

metrics data)

Droptrained model

(optional)

Training Inference

CreateREST listener

4 Architecture

114

4.5 Side-by-Side Predictive Analytics

In a side-by-side setup with SAP Analytics Cloud, smart assist expands the conversation

beyond just visualizations, charts, and data connectivity, offering easy-to-consume

machine learning capabilities embedded deeply within SAP Analytics Cloud, alongside

and integrated with analytics and planning. The smart features within SAP Analytics

Cloud make insight discovery faster by bringing data science to the masses. Intelligent

machine learning technology drives the analytics process while reducing human bias,

ensuring that decision makers can act with confidence. Figure 4.17 depicts the architec-

ture of the predictive analytics services provided by SAP Analytics Cloud, including sev-

eral smart assist features.

Figure 4.17 Predictive Analytics Architecture

As depicted in Figure 4.17, the smart assist functionality is covered by the following

components (which we introduced in Chapter 3, Section 3.4.2):

� Smart predict

Augments existing business intelligence capabilities by learning from your historical

data to predict what is most likely to happen in the future. With classification, re-

gression, and time series forecast algorithms, smart predict creates high-performing

and stable models to help you optimize operations and drive strategic decisions for

growth. Smart predict makes machine learning accessible by automatically handling

complex data science procedures. This allows business analysts to focus on imple-

menting insights to influence existing business processes.

� Smart insights

Reveals the top contributors behind a specific data point or variance. This feature is

particularly useful when we are looking at aggregated data, such as revenue. Smart

SAP S/4HANA

Automatedpredictive library

Applicationdata

SAP Analytics Cloud

Data integration

Predictiveanalytics

Application

Smartpredict

Smartdiscover

Smartinsights

Searchinsight

Business data lake

SAP Fiori UI Explorative UI

Smartservices

Application content: models/stories

115


4

insights helps us to understand revenue contribution trends at a more granular

level, such as by region or product. With the click of a button, machine learning algo-

rithms run in the background to analyze all the data relevant to the selected infor-

mation. First, suggested visualizations ranked in order of relevance are presented.

Then, these visualizations can be added to an SAP Analytics Cloud story along with a

text explanation thanks to the natural language generation engine.

� Smart discovery

Acts as a digital business analyst. It automates the data exploration process to reveal

information that is statistically relevant. Trained machine learning models that are

specific to customer data generate an overview of significant patterns, outliers, key

drivers, and influencers. Then, an interactive what-if simulation is created based on

the model, allowing you to explore the possible results of changing factors and vari-

ables. With the autogenerated story from smart discovery, the data speaks for itself.

Intelligent algorithms remove the need for analytics to be fully human-led, reducing

bias to ensure that important trends are not missed.

� Search to insight

Makes content creation easier with the power of natural language query. To use

search to insight, simply ask a question about the data, and SAP Analytics Cloud will

intelligently create relevant visualizations that can be added to stories.

A dataset is an SAP Analytics Cloud entity representing a collection of homogenous

data (basically a database table) that can be produced and consumed in different SAP

Analytics Cloud workflows. Typical workflows are data acquisition, consisting of creat-

ing a dataset from data located in an external system, and story building, consisting of

creating a dashboard using a dataset. The physical location of data held by the dataset

is complemented by a dataset description that comprises information for each column

like the data type, length, format, and semantic information (e.g., latitude, longitude,

country), plus more predictive-specific information like the category (e.g., nominal,

ordinal). The data integration infrastructure of the SAP Analytics Cloud platform pro-

vides two data access modes for the smart assist features:

1. Data-acquisition mode

In this mode, data is either transferred from external systems like SAP S/4HANA or

uploaded from the end user environment to the SAP Analytics Cloud platform and

processed. This offline approach safeguards the resource consumption of the exter-

nal systems and allows a scale-out of predictive processing on SAP Analytics Cloud.

Its drawback is that the offline data can become outdated with time and must be

updated on a regular basis.

2. Live-connection mode

In this mode, data stay on external systems like SAP S/4HANA and are processed

there as well. In the context of smart assist, usually the SAP HANA database with APL

installed is required. With the live connection, real-time access to data is provided so

4 Architecture

116

that the latest state of the application data is always considered. However, for pro-

cessing APL directly on the online data, greater CPU time and RAM in the external

system are consumed. Currently, live-connection mode is implemented for access

to application data on the database system but not on the application server level.

Datasets can have one of two origins. They can be either the result of a data acquisition

or can be the result of a predictive model application. This relates to the role a dataset

can have:

� Training dataset

A dataset can be used as the training dataset for a predictive model. In this context,

the dataset contains valid values for the model’s explanatory variables and target

variables. The data will be used to optimize the model fit.

� Apply-in dataset

A dataset can be used as the input for the application of a predictive model. In this

context, the dataset contains values for the model’s explanatory variables but not

for the target variables, the goal being precisely to evaluate the value of that target

variable using a trained model.

� Apply-out dataset

A dataset can be used as the output for the application of predictive model. In this

context, at the end of the application phase, the dataset will contain the estimated

value of the target variable for all the records of the apply-in dataset.

Smart assist can be consumed in SAP Analytics Cloud stories and from there integrated

into SAP S/4HANA UIs. A story is a presentation-style document that uses charts, visu-

alizations, text, images, and pictograms to describe data. Once a story is created or

opened, pages, sections, and elements can be added and edited. The story toolbar is

divided into different categories such as File, Insert, Data, and Tools to help users find

options and perform tasks more efficiently. Owners of stories can share them with

other users and grant permissions for these stories. After stories are shared, users with

view permissions can analyze the data by navigating within the stories.

We’ll focus mainly on the smart predict architecture, which enables the implementa-

tion of predictive analytics generically. The remaining smart assist features are pro-

vided as built-in functionality in the SAP Analytics Cloud infrastructure for direct

usage. Another reason for concentrating on smart predict is that most of the smart

assist functions are based on this service. Figure 4.18 illustrates the detailed architec-

ture of smart predict.

In SAP Analytics Cloud, predictive features essentially fall into smart predict—which

provides for managing model authoring and storage and for training models and

applying them—and smart assist—which is an umbrella for a number of predictive fea-

tures built on top of the predictive cloud services, like smart discovery and smart

insight. Smart predict bundles two different authoring experiences, a purely form-

based and easy-to-consume automated UI geared toward the nontechnical user and, in

117


4

the future, it is planned to provide a pipeline-based editor targeting the maverick data

scientist. In addition to providing support for managing model specifications, smart

predict core services implement the concept of a task, typically encapsulating model

training or applying work and a mechanism for executing such tasks in a regulated

fashion, preventing the overload of underlying automated engines. Tasks pertaining to

models authored through the automated UI and handled by the automated engine are

exposed as a Cloud Foundry application. The associated composer models are pro-

cessed by an embedded composer engine by delegating the execution to the APL in

SAP HANA. Execution of automated tasks involves interaction with the automated

engine through a synchronous and stateful KxCommonInterf/Thrift protocol.

Figure 4.18 Smart Predict Architecture

The smart predict core services provide for automated and composer models for author-

ing, debriefing, entity management, and task management services. The automated

engine supports a native mode in which the engine itself executes the predictive algo-

rithms based on the data it fetches over open database connectivity (ODBC) from the

SAP HANA instance. In a delegated mode, the automated engine hands over the pro-

cessing of the predictive algorithms to APL running in the SAP HANA instances, pre-

venting the data from being moved from the database to the engine. The SAP HANA

instance of SAP Analytics Cloud stores the predictive models (physical model settings,

scoring equations, and model raw statistics), the predictive model debriefing informa-

tion (model statistics), and the physical data that are accessed and produced during the

execution of the predictive workflows.

SAP HANA

SAP HANA index server

SAP Analytics Cloudagent

SAP HANA script server


Live businessdata

Live predictivemodels

Live debriefingdata

R

SAP Analytics Cloud/SAP Cloud Platform

Browser

Smart predictclient

Smart predictcore services

Predictiveservices

Task manager service

SAP HANA

SAP HANA XS

R

Smart predict processing server

Composerengine

Automatedengine

SAP AnalyticsCloud predictive

Smart assist UI

SAP S/4HANAapplication server

R

SAP HANA index server

Acquired businessdata

Predictivemodels

Debriefingdata

SAP HANA script server


4 Architecture

118

4.6 Summary

In this chapter, we explained what makes SAP S/4HANA intelligent and how this is

related to automation. We gave a brief overview of technical challenges that must be

solved in the context of SAP S/4HANA regarding machine learning and predictive ana-

lytics. For understanding the structure of the already delivered scenarios, we shared

the application patterns that have been identified so far. To enable you to use the exist-

ing SAP S/4HANA machine learning and predictive scenarios, we explained the solu-

tion architecture behind them.

This knowledge is the foundation for the next chapter, in which we will describe how to

implement your own machine learning and predictive analytics use cases step by step

according to the SAP S/4HANA programming model. We will also share with you how

to resolve advanced aspects like lifecycle management, GDPR, and model degradation,

which are crucial in the context of enterprise applications such as SAP S/4HANA.

7

Contents

Preface ....................................................................................................................................................... 13

1 Introduction to Predictive Intelligence 19

1.1 The Intelligent Enterprise .................................................................................................. 19

1.2 How Predictive Intelligence Is Evolving at SAP ........................................................ 22

1.3 Connected End-to-End Scenarios ................................................................................... 24

1.3.1 Modular Approach ................................................................................................. 25

1.3.2 Example Scenarios ................................................................................................. 25

1.4 Analytics of the Future ........................................................................................................ 32

1.4.1 Unified Analytics ..................................................................................................... 33

1.4.2 Business Problems and Business Needs ......................................................... 34

1.4.3 Trends and Technologies ..................................................................................... 35

1.4.4 User Roles with Analytics .................................................................................... 35

1.5 Summary ................................................................................................................................... 37

2 The Evolution of Predictive Analytics and Machine Learning at SAP 39

2.1 Predictive Analytics and Machine Learning before SAP S/4HANA ................. 39

2.2 Technologies and Methodologies .................................................................................. 40

2.2.1 Automated Analytics ............................................................................................. 41

2.2.2 Expert Analytics ...................................................................................................... 44

2.3 Best Practices ........................................................................................................................... 45

2.4 Summary ................................................................................................................................... 48

3 Tools, Technologies, and Services 49

3.1 Machine Learning and Predictive Analytics Approaches ..................................... 49

3.2 Embedded Machine Learning and Predictive Analytics ....................................... 51

3.2.1 Overview .................................................................................................................... 51

Contents

8

3.2.2 Embedded Machine Learning with the SAP HANA Automated

Predictive Library .................................................................................................... 53

3.2.3 Embedded Machine Learning with the SAP HANA Predictive

Analysis Library ....................................................................................................... 55

3.3 SAP Cloud Platform ............................................................................................................... 57

3.3.1 SAP Data Intelligence ............................................................................................ 57

3.3.2 Hybrid Machine Learning Models ..................................................................... 62

3.4 SAP Analytics Cloud .............................................................................................................. 63

3.4.1 Overview .................................................................................................................... 63

3.4.2 Smart Assist Services ............................................................................................. 65

3.4.3 Smart Predict Services .......................................................................................... 67

3.5 SAP Intelligent Robotic Process Automation ............................................................ 70

3.6 SAP Internet of Things ......................................................................................................... 76

3.7 Summary ................................................................................................................................... 80

4 Architecture 83

4.1 Introduction ............................................................................................................................. 83

4.1.1 Technical Challenges in SAP S/4HANA ........................................................... 83

4.1.2 How to Operationalize Intelligence ................................................................. 84

4.2 Architecture Overview ........................................................................................................ 90

4.2.1 Machine Learning Application Patterns ......................................................... 90

4.2.2 Guiding Principles for Solution Architecture ................................................ 94

4.2.3 Solution Architecture ............................................................................................ 95

4.3 Embedded Machine Learning ........................................................................................... 97

4.4 Side-by-Side Machine Learning ....................................................................................... 102

4.5 Side-by-Side Predictive Analytics ................................................................................... 114

4.6 Summary ................................................................................................................................... 118

5 Technical Implementation 119

5.1 Approach Comparison ......................................................................................................... 119

5.2 Implementing Embedded Machine Learning Applications ................................ 122

5.2.1 Generated Approach Based on the SAP HANA Automated Predictive

Library ......................................................................................................................... 123

9

Contents

5.2.2 Coded Approach Based on the SAP HANA Predictive Analysis

Library ......................................................................................................................... 131

5.3 Implementing Side-by-Side Machine Learning Applications ............................ 137

5.3.1 Required Development in SAP Data Intelligence ......................................... 137

5.3.2 Required Development in ABAP ........................................................................ 144

5.4 Implementing Side-by-Side Predictive Analytics Applications ......................... 148

5.5 Application Management Processes ............................................................................. 155

5.5.1 Lifecycle Management ......................................................................................... 155

5.5.2 Data Integration ..................................................................................................... 169

5.5.3 Data Protection and Privacy ................................................................................ 183

5.5.4 Configuration .......................................................................................................... 194

5.5.5 Extensibility .............................................................................................................. 200

5.5.6 Model Degradation ................................................................................................ 215

5.5.7 Explanation of Results .......................................................................................... 221

5.5.8 Workload Management and Performance .................................................... 229

5.5.9 Legal Auditing .......................................................................................................... 237

5.5.10 Model Validations .................................................................................................. 244

5.5.11 User Interface Design ............................................................................................ 252

5.6 Summary ................................................................................................................................... 260

6 Business Implementation 261

6.1 Overview of Intelligent Scenarios .................................................................................. 261

6.1.1 Creating a Purchase Requisition as an Employee ........................................ 262

6.1.2 Processing a Purchase Requisition as an Operational Purchaser ........... 263

6.1.3 Monitoring the Spend as a Strategic Purchaser ........................................... 264

6.1.4 Creating Sales Inquiries as a Sales Manager ................................................. 264

6.1.5 Recording Financial Transactions as an Accounts Receivable

Manager .................................................................................................................... 265

6.2 Configuration Basics ............................................................................................................ 266

6.2.1 SAP Best Practices Explorer ................................................................................. 267

6.2.2 SAP Help Portal ........................................................................................................ 271

6.3 Finance ....................................................................................................................................... 272

6.3.1 SAP Cash Application ............................................................................................ 273

6.3.2 Accounting and Financial Close ......................................................................... 280

6.3.3 Financial Planning and Analysis ........................................................................ 285

6.3.4 Governance, Risk, and Compliance .................................................................. 289

6.3.5 Detect Abnormal Liquidity Items ...................................................................... 293

Contents

10

6.4 Sourcing and Procurement ................................................................................................ 295

6.4.1 Contract Consumption ......................................................................................... 296

6.4.2 Propose Resolution for Invoice Payment Block ............................................ 297

6.4.3 Supplier Delivery Prediction ................................................................................ 299

6.4.4 Proposal of New Catalog Item ........................................................................... 300

6.4.5 Proposal of Material Group ................................................................................. 301

6.4.6 Proposal of Options for Materials without Purchase Contract ............... 303

6.4.7 Image-Based Buying .............................................................................................. 305

6.4.8 Intelligent Approval Workflow .......................................................................... 306

6.4.9 Blockchain-Verified RFQ Processing ................................................................. 307

6.5 Inventory and Supply Chain ............................................................................................. 308

6.5.1 Stock in Transit ........................................................................................................ 309

6.5.2 Demand-Driven Replenishment ........................................................................ 311

6.5.3 Defect Code Proposal with Text Recognition ............................................... 313

6.5.4 Early Detection of Slow and Nonmoving Stock ............................................ 315

6.6 Sales ............................................................................................................................................. 317

6.6.1 Predict Conversion of Sales Quotations to Sales Orders ........................... 318

6.6.2 Predict Sales Forecasts .......................................................................................... 319

6.6.3 Predict Delivery Delay ........................................................................................... 322

6.7 Research and Development/Engineering .................................................................. 323

6.7.1 Project Cost Forecasting ....................................................................................... 324

6.7.2 Digital Content Processing .................................................................................. 325

6.8 Industries ................................................................................................................................... 327

6.8.1 Professional Services ............................................................................................. 327

6.8.2 Component Manufacturing ................................................................................ 336

6.8.3 Retail ........................................................................................................................... 342

6.8.4 Utilities ....................................................................................................................... 352

6.8.5 Consumer Products ............................................................................................... 357

6.8.6 Insurance ................................................................................................................... 360

6.8.7 Telecommunications ............................................................................................. 364

6.8.8 Banking ...................................................................................................................... 368

6.8.9 High-Tech .................................................................................................................. 370

6.8.10 Sports and Entertainment ................................................................................... 372

6.8.11 Public Services ......................................................................................................... 373

6.9 Summary ................................................................................................................................... 374

11

Contents

7 Services on SAP Cloud Platform 375

7.1 Key Trends and Capabilities .............................................................................................. 375

7.2 SAP Data Intelligence .......................................................................................................... 382

7.3 Machine Learning .................................................................................................................. 383

7.4 Internet of Things .................................................................................................................. 386

7.5 Blockchain ................................................................................................................................. 387

7.6 Summary ................................................................................................................................... 388

8 The Road Ahead and Further Learning 389

8.1 Upcoming Features and Functionality ......................................................................... 389

8.1.1 Embedded Predictive Models ............................................................................. 389

8.1.2 Machine Learning Models on SAP Cloud Platform ...................................... 390

8.1.3 SAP Analytics Cloud ............................................................................................... 391

8.1.4 Extensions of Existing Approaches .................................................................. 392

8.2 Blogs for Continuous Information ................................................................................. 392

8.3 Summary ................................................................................................................................... 393

The Authors ............................................................................................................................................. 395

Index .......................................................................................................................................................... 397

397

Index

A

A/B testing ............................................................... 245

ABAP application server ...................... 97, 99, 159

side-by-side model ........................................... 103

ABAP CDS Reader .................................................. 180

ABAP class ............................................. 108, 120, 162

enable for customer extension ................... 110

prerequisite checks .......................................... 164

utility ..................................................................... 168

ABAP Class Builder ..................................... 133, 145

ABAP development .............................................. 144

ABAP editor ............................................................. 213

ABAP environment .............................................. 377

ABAP in Eclipse ...................................................... 131

ABAP lifecycle management ............................... 55

ABAP Workbench .................................................. 247

Accounting .................................................... 280, 284

Accounts receivable clerks ................................ 275

Accounts receivable manager ...... 265, 273, 289

Accruals .................................................................... 283

Accruals management ........................................ 282

Accruals prediction .............................................. 282

Accuracy KPIs ......................................................... 216

Action execution ..................................................... 86

Affinity analysis .................................................... 346

Algorithm exchange ............................................ 201

technical implementation ............................ 208

Algorithms ................................................... 19, 46, 95

APL ......................................................................... 101

basic ......................................................................... 22

categorization ...................................................... 93

embedded machine learning ........... 23, 52, 98

matching ................................................................ 91

operator ............................................................... 141

PAL ......................................................................... 102

predictions ............................................................. 93

ranking .................................................................... 92

recommendations .............................................. 92

SAP Analytics Cloud ........................................... 63

self-learning .......................................................... 13

side-by-side machine learning ............. 59, 103

strategy ................................................................... 43

AMDP class .......................... 55, 100–102, 120, 132

extensibility ........................................................ 209

implement ........................................................... 133

register ................................................................. 133

Analyze demand ...................................................... 28

Analyze spend .......................................................... 28

Anomaly detection .............................................. 364

Application logs .................................................... 237

Application management ................................. 155

Application patterns .............................................. 90

Apply-in dataset .................................................... 116

Apply-out dataset ................................................. 116

Architecture ............................................................... 83

embedded model ................................................ 97

guiding principles ............................................... 94

machine learning ............................................... 90

SAP Analytics Cloud ....................................... 114

SAP S/4HANA ....................................................... 95

side-by-side model .......................................... 102

Artifacts ........................................................... 119, 158

configuration and extensibility ................. 195

custom ................................................................. 214

Artificial intelligence (AI) ..................................... 21

evolution ................................................................ 22

Association rules ..................................................... 42

Assortment lists .................................................... 345

Assortment planning ......................................... 345

Attachment service ............................................. 182

Attended automation ............................................ 71

Audit execution .................................................... 243

Audit planning ...................................................... 243

Audit preparation ................................................ 243

Auditing ................................................................... 238

entities ................................................................. 238

phases .................................................................. 242

Auditors ................................................................... 239

Augmented analytics ................................... 67, 391

Auto classification .................................................. 45

Automated analytics .............................................. 41

principles ................................................................ 42

Automated decision-making .................... 15, 185

Automated Predictive Library (APL) ........ 22, 50,

52–53, 96, 122

approach comparison ................................... 120

extensibility .............................................. 208, 213

integration ......................................................... 101

maintain ............................................................. 125

technical implementation ........................... 123

Automated techniques ......................................... 88

Automation ............................................................... 71

Index

398

B

Back orders ................................................................. 31

Backflush issues .................................................... 340

Bad debts .................................................................. 335

BAdI implementation ......................................... 209

Banking ..................................................................... 368

financial services data platform ................ 370

upcoming ideas ................................................ 369

Behavioral insights .............................................. 374

Best practices ............................................................. 45

Bid management .................................................. 328

Biometric bridge ................................................... 373

Blockchain

mobile theft ........................................................ 365

payment fraud prevention ........................... 365

RFQ processing .................................................. 307

SAP Cloud Platform ............................... 379, 387

Blocking ................................................. 184–185, 190

Bonded loans management ............................. 369

Buffer levels ............................................................ 312

Business add-ins (BAdIs) .................................... 209

Business administrators .......................... 157, 161

Business customer analytics ............................ 368

Business entity recognition ............................. 386

Business experts ................................................... 157

Business feature .................................................... 246

Business implementation ................................. 261

banking ................................................................ 368

component manufacturing ......................... 336

configuration ..................................................... 266

consumer products .......................................... 357

finance .................................................................. 272

high-tech .............................................................. 370

industries ............................................................. 327

insurance ............................................................. 360

inventory and supply chain ......................... 308

professional services ....................................... 327

public services ................................................... 373

R&D/engineering ............................................. 323

retail ...................................................................... 342

sales ....................................................................... 317

sourcing and procurement .......................... 295

sports and entertainment ............................. 372

telecommunications ....................................... 364

utilities .................................................................. 352

Business integrity screening ............................ 290

Business services ........................................... 58, 376

Business users ....................................... 24, 157, 159

Business validation .............................................. 249

C

Capacity allocation ............................................... 351

Cash and liquidity management .................... 293

Cash collection ......................................................... 32

Catalog items .......................................................... 300

Categorization ................................................ 93, 294

Category influence ............................................... 228

Change data capture approach ........................ 176

Change indicator ................................................... 259

Claims management ............................................ 359

Claims severity ....................................................... 361

Classification ... 42–43, 45, 68, 93, 291, 294, 299,

302, 314, 325

Classification scenario ........................................ 148

Cloud Data Integration operator .......... 141, 180

configuration ..................................................... 180

parameters .......................................................... 143

Cloud factory ...................................................... 73–74

Cloud Foundry ........................................................ 117

Cloud scalability .................................................... 376

Clustering ................................................................... 44

Collaboration .......................................................... 379

Collaborative enterprise planning ................. 391

Commercialization ............................................... 378

Compatibility matching ....................................... 91

Complexity .............................................................. 222

Component manufacturing ............................. 336

Composer engine .................................................. 117

Configuration ......................................................... 194

business implementation .............................. 266

business requirements .................................... 194

lifecycle management .................................... 194

model hyperparameter .................................. 199

multiple model support ................................. 197


Connection Management app ......................... 137

Consent ................................................. 184, 186, 190

embedded machine learning ....................... 188

side-by-side machine learning .................... 193

Consent management data ............................... 189

Consistency ............................................................. 172

Consumer demand forecast ............................. 352

Consumer products ............................................. 357

upcoming ideas ................................................. 360

Consumption API ........................................ 146, 211

Consumption API extension ............................ 201


Containers ................................................................ 236

Content recommendations ................................ 91

Continuous accounting ...................................... 284

Contract consumption ........................................ 296

399

Index

Controlling .............................................................. 286

Convergence speed ................................................. 42

Conversational AI .................................................... 93

Core data service (CDS) table functions ......... 53,

100, 130

Core data service (CDS) views .... 52, 64, 97, 100,

119, 130, 219

consumer-defined ............................................ 172

data extraction ................................................. 174

data integration ..................................... 171, 173

data source extension .................................... 205

extend ................................................................... 203

time-dependent ................................................ 221

Core data services (CDS) ........................................ 95

Cost-optimal ordering ........................................ 351

Criticality .................................................................. 222

Cross product allocation .................................... 349

Custom Business Object app ............................ 207

features ................................................................ 208

Custom CDS Views app ............................ 206, 213

Custom field extensions .................................... 202

Custom tables ......................................................... 207

Custom visualizations ........................................... 64

Customer Business Object app ........................ 213

Customer invoicing ................................................ 32

Customer profile from insurance .................. 363

Customer profitability analysis ............ 356, 364

Customer scenarios ................................................ 46

Customer service chatbot ................................. 367

Customers ................................................................ 326

Customer-specific tables ................................... 207

D

Data acquisition ....................................................... 86

Data actions monitoring ................................... 391

Data attribute recommendation .................... 385

Data change support ........................................... 173

Data connectivity .......................................... 35, 104

Data extraction ............................................ 171, 174

annotation schema ......................................... 177

steps ....................................................................... 175

Data governance ..................................... 60, 62, 104

Data integration .................................................... 169

business requirements ................................... 170

model .................................................................... 171

pipelines and operators ................................. 179

solution characteristics ................................. 171


Data landscape management ............................. 61

Data locking ............................................................ 391

Data management ...................................... 105, 377

Data mining ............................................................... 42

Data persistency ...................................................... 97

Data pipelining ............................................... 61, 106

Data Preview tool ................................................. 131

Data protection and privacy ................... 172, 183


embedded machine learning ...................... 187

side-by-side machine learning ................... 190


Data scientists ................................................ 67, 383

Data source extension ....................................... 205

Data-acquisition mode ...................................... 115

Datasets ........................................................... 115, 139

origins .................................................................. 116

Debrief cockpit ...................................................... 108

Decision tree ............................................................. 45

Decision-making ..................................................... 86

Decoupled extensions ........................................ 214

Defect code proposal .......................................... 313

Defect processing ................................................. 313

Defect recording ................................................... 339

Defects ...................................................................... 313

Degradation component .......................... 219–220

Delayed feedback ................................................. 218

Deletion ................................................. 184–185, 190

Delivery class A ..................................................... 176

Delivery delays ...................................................... 299

predict .................................................................. 322

Delivery insights ...................................................... 79

Delivery performance ............................... 317, 322

Delta handling ....................................................... 170

approaches ......................................................... 176

Delta loads ............................................................... 170

Demand Data Foundation (DDF) ................... 346

Demand signal management .......................... 358

Demand-driven replenishment ..................... 311

Deployment .................................................. 156, 166

test ......................................................................... 167

Deprecation ............................................................ 214

Desktop agents .................................................. 73–74

Desktop studio .................................................. 73–74

Developers ........................................... 157, 161, 240

DevOps ............................................................ 156, 378

Digital content processing ............................... 325

Digital transformation ................................... 20, 22

Digitization ............................................................. 326

Disruption ............................................................... 376

Dockerfiles .............................................................. 180

Document classification ................................... 384

Document information extraction ............... 385

Document management ................................... 379

Drift and skew detection .......................... 217, 219

Dynatrace service ................................................. 378

Index

400

E

Electricity supply .................................................. 354

Email to record ...................................................... 362

Embedded machine learning ............... 50–51, 97

ABAP integration ................................................ 99

algorithms ............................................................. 98

APL ........................................................ 53, 101, 123

approach comparison .......................... 119, 121

criteria ..................................................................... 53

data protection and privacy ........................ 187

inferencing .......................................................... 168

PAL ........................................................ 55, 101, 131


training ................................................................... 98

workload management and

performance ........................................ 230–231

Embedded machine learning service ........... 287

End user feedback ................................................. 239

End users ........................................................... 36, 240

End-of-purpose check ......................................... 185

End-to-end scenarios ..................................... 24–25

intelligent invoice-to-cash ..................... 31, 264

intelligent order-to-cash ............................... 274

intelligent procure-to-invoice .............. 26, 262

intelligent quote-to-order ................................ 29

intelligent sell-from-stock ............................... 30

intelligent source-to-contract .............. 27, 264

Energy providers ................................................... 354

Engineering ............................................................. 323

Enterprise portfolio and project

management ..................................................... 323

Enterprise readiness ..................................... 84, 155

Enterprise reporting ............................................ 391

Event-based scheduling ..................................... 232

Experimentation ................................................... 105

Expert analytics ........................................................ 44

Explainability ......................................................... 226

Explainable machine learning .............. 221, 253

backend processes ........................................... 226



training ................................................................ 227

user interface ..................................................... 223

Explanation levels ................................................ 223

user interface ..................................................... 224

Explanations ................................................. 222–223

component ......................................................... 226

elements ............................................................... 224

Explicit feedback ................................ 218, 220, 250

Extensibility ..................................................... 50, 200

algorithm exchange ........................................ 208

Extensibility (Cont.)

APIs ........................................................................ 211

artifacts ................................................................ 195


custom API extension ..................................... 210


machine learning logic extension ............. 208

new machine learning app ........................... 212


training data source extension ................... 202

upcoming features ........................................... 392

Extensibility lifecycle management .... 202, 214

Extensibility registry ........................................... 205

Extension includes ..................................... 202, 204

External ID ............................................................... 145

External nodes ....................................................... 388

F

Factory data ............................................................. 371

Feature toggle ......................................................... 246

Feedback loops ................................... 215, 217, 253

Finalizing action .................................................... 255

Finance ...................................................................... 272

Finance controllers ..................................... 325, 335

Financial close ........................................................ 280

Financial planning and analysis ...................... 285

Financial transactions ......................................... 265

Flood prediction and emergency

management ...................................................... 373

Forecasting ................................................................. 42

Free-text items ....................................................... 300

Full match .................................................................. 90

Functional services ................................................. 58

Further learning ........................................... 389, 392

blogs ....................................................................... 392

G

GDPR workbench ........................................ 191, 193

General Data Protection Regulation

(GDPR) ................................................................... 183


requirements ...................................................... 184


General ledger ........................................................ 289

Generalization capacity control ........................ 43

Global explanation ............................................... 226

Global international mobile equipment

identity storage and service ......................... 365

Globalization ........................................................... 378

Globally unique identifier (GUID) .................. 162

401

Index

Goods receipt ............................................................ 27

Governance, risk, and compliance (GRC) .... 289

GR/IR account reconciliation ........................... 281

H

Helm .......................................................................... 236

High-tech ................................................................. 370

Hiring managers ................................................... 331

Horizon-wide mean absolute percentage

error (horizon-wide MAPE) .......................... 152

Hybrid models .......................................................... 62

Hyperledger Fabric ............................................... 388

I

Idea to design ......................................................... 323

Identity Authentication service ...................... 382

Image-based buying ............................................ 305

Implausible meter readings ............................. 353

Implicit feedback ........................................ 217, 220

Implicit selection .................................................. 172

In-Arena Coaching app ....................................... 372

Industries ................................................................. 327

banking ................................................................ 368

component manufacturing ......................... 336

consumer products .......................................... 357

high-tech .............................................................. 370

insurance ............................................................. 360

professional services ....................................... 327

public services ................................................... 373

retail ...................................................................... 342

sports and entertainment ............................. 372

telecommunications ....................................... 364

utilities .................................................................. 352

Inference ........................................ 60, 107, 168, 230

API ....................................................... 158, 196, 242

consume .............................................................. 146

display results .................................................... 131

logic ....................................................................... 136

pipelines ............................................ 112, 140, 144

request and response ...................................... 238

side-by-side model ........................................... 110

Information analysis .............................................. 86

Initial loads .............................................................. 170

Input assistance .............................................. 91, 256

Inspection plans .................................................... 338

Insurance ................................................................. 360

upcoming ideas ................................................ 363

Intelligence ................................................................. 84

Intelligent Account Finder for Payments

app ......................................................................... 368

Intelligent approval workflow ........................ 306

Intelligent bad debts risk analyzer ................ 335

Intelligent bid management ............................ 328

Intelligent enterprise ............................................. 19

analytics ................................................................. 33

end-to-end scenarios .................................. 24–25

evolution ................................................................ 20

strategy ............................................................ 21–22

Intelligent invoice-to-cash ............... 31, 264–265

Intelligent matching ........................................... 254

Intelligent overdraft decisions ....................... 370

Intelligent procure-to-invoice ................. 26, 262

Intelligent project profitability

monitoring ......................................................... 332

Intelligent quote-to-order ................................... 29

Intelligent robotic process automation

architecture ........................................................... 73

bot making ............................................................ 75

deployment ........................................................... 73

overview ................................................................. 71

phases ..................................................................... 72

Intelligent rollover ............................................... 369

Intelligent scenario lifecycle management

(ISLM) framework ....................... 50–51, 53, 122

APL ............................................................................ 53

architecture ........................................................... 98

connect to SAP Data Intelligence .............. 147

deployment ........................................................ 167

inferencing ......................................................... 168

intelligent scenarios ....................................... 160

KPIs ....................................................................... 165

lifecycle management ................................... 159

model validation ............................................. 250

monitoring ......................................................... 169

PAL ............................................................................ 55

phases and roles ............................................... 161

pipelines .............................................................. 140

prerequisite check ............................................ 163

provide metadata ............................................ 134


smart predict ........................................................ 70

steps ......................................................................... 54

training ................................................................ 166

upcoming features .......................................... 389

Intelligent Scenario Management app ........ 123,

128, 147, 161, 233, 249

Intelligent scenarios ............... 102, 108, 123, 197

business implementation ............................. 261

create .................................................................... 161

create based on APL ....................................... 124

create based on PAL ....................................... 132

create based on SAP Data Intelligence ... 144

Index

402

Intelligent scenarios (Cont.)

input tab .............................................................. 126


multimodal ......................................................... 391

output tab ........................................................... 127

publish .................................................................. 109

train ............................................................. 128, 147

user interface ..................................................... 251

Intelligent Scenarios app ...... 123, 144, 161, 213

Intelligent sell-from-stock ................................... 30

Intelligent source-to-contract .................. 27, 264

Intelligent staffing and resource

management ..................................................... 331

Intelligent store management ........................ 351

Intelligent technologies ........................................ 21

Intelligent WIP project tracking ...................... 333

Intelligent workforce planning ....................... 330

International Mobile Equipment Identities

(IMEIs) ................................................................... 365

Internet of Things (IoT) ......................................... 76


use cases ................................................................. 78

Intrinsic interpretability .................................... 226

Inventory accounting ......................................... 280

Inventory management ..................................... 308

Inventory managers .................................. 309, 312

Invoice inspector .................................................. 363

Invoice object recommendation .................... 386

Invoice payment blocks ..................................... 297

Invoice payment forecasting ........................... 289

IT users ......................................................................... 36

J

Jupyter Notebook ....................... 62, 105, 139–140

JupyterLab ......................................................... 60, 106

K

Key performance indicators (KPIs) ................ 216

Key users ..................................................................... 36

K-nearest neighbors .................................. 315, 325

Kubelet ...................................................................... 236

Kubernetes .................................................... 236, 376

L

Lead time .................................................................. 312

buffer replenishment ...................................... 312

Legal auditing ......................................................... 237



Legal contracts .......................................................... 29

Lifecycle management ....................... 58, 104, 155


configuration ..................................................... 194

deployment ......................................................... 166

inferencing .......................................................... 168

monitoring .......................................................... 169

prerequisite check ............................................. 163


training ................................................................. 165

Liquidity items ....................................................... 294

Live-connection mode ........................................ 115

Local accounting principles .............................. 281

Local APIs .................................................................. 211

Local explanation ........................................ 226, 228

Location cluster sets ............................................ 344

Lockbox information ........................................... 277

benefits ................................................................. 278

Logging ............................................................ 237, 240

entities ........................................................ 238, 241

M

Machine learning ............................................. 13, 51

application patterns .......................................... 90

approaches ............................................................ 49

architecture .................................................... 83, 90

best practices ........................................................ 45


decision tree .......................................................... 50

embedded ................................................ 50–51, 97

ethical considerations ...................................... 14

evolution ................................................................ 39

explanations ....................................................... 222

hybrid models ...................................................... 62

model ..................................................................... 216

SAP AI Business Services ................................ 384

SAP Analytics Cloud .......................................... 63


scenario .................................................................. 62

side-by-side ........................................ 57, 102, 137

technical challenges .......................................... 84


technologies and methodologies ................. 40

upcoming features ........................................... 389

Machine learning logic extension .................. 201


Machine learning model validation .............. 247

Machine learning operations cockpit ........... 107

Machine learning scenario ... 108, 120, 158, 219

consume ............................................................... 110

create ..................................................................... 138

403

Index

Machine learning scenario (Cont.)

details ................................................................... 138

release ................................................................... 144

unique identifier ............................................... 139

Machine learning scenario manager ............... 62

Mapping rules ........................................................ 233

Master data .............................................................. 381

Matching ................................................. 90, 254, 276

user interface ..................................................... 254

Matching groups ................................................... 254

Material groups ..................................................... 301

Material tracing ..................................................... 372

Materials without a purchase contract ........ 303

Metrics component ............................................. 219

Misdirected payments ........................................ 368

ML Scenario Manager app ................................. 137

Mobile devices ....................................................... 366

Model accuracy KPIs ............................................ 130

Model degradation ..................................... 215, 220


solution architecture ...................................... 218


Model dispatcher .................................................. 197

Model hyperparameter ............................ 194, 199

user interface ..................................................... 200

Model validation ................................................... 244


solution architecture ...................................... 249


user interface ..................................................... 251

Model versions ...................................................... 129

Modeler app ............................................................ 137

Modular approach ................................................... 25

Monitoring ........................................... 157, 159, 169

module ................................................................. 219

MultiChain .............................................................. 388

Multicloud ............................................................... 388

Multiple deployments ........................................... 95

Multiple model support ........................... 194, 197

user interface ..................................................... 198

N

Natural language processing (NLP) ....... 65, 367,

383

Negative feedback ................................................ 217

Neural network ......................................................... 45

New machine learning app ............................... 202

extensibility ........................................................ 212

Node agents ............................................................ 236

Nonparametric models ......................................... 93

Notification API ..................................................... 192

O

ObjectStore .................................................... 183, 377

Off-contract spend ............................................... 303

Offline validation ................................................. 247

Online validation ................................................. 247

Open receivables ..................................................... 32

Open Service Broker API (OSBAPI) ................ 378

Operational data provider (ODP)

framework .......................................................... 174

Operational purchasers ..................................... 301

Operationalization ................................................. 84

business view ........................................................ 88

methodology ........................................................ 85

technology view .................................................. 88

Operators ........................................................ 106, 179

Order-to-cash process ............................... 274, 317

Outgoing payments ............................................ 279

Outliers ..................................................................... 152

Output batches ...................................................... 341

Output control ...................................................... 182

Output management ................................. 182, 380

Outsorted billing documents .......................... 356

Overdue Materials—Stock in Transit

app ................................................................ 309–310

P

Packaged solutions ................................................. 46

Parametric models ................................................. 93

Partial match ............................................................. 91

Payables line item matching ........................... 279

Payment advice information extraction .... 276

Payment fraud prevention ............................... 365

Performance ........................................................... 230

measurements .................................................. 232

Periodic valuation ................................................ 280

Personal data .......................................................... 184

Pipeline modeler ......................................... 106–107

Pipelines ................................ 61, 106, 120, 139, 179

create .................................................................... 140

extensibility ....................................................... 210

inference ..................................................... 112, 144

templates ............................................................ 140

training .................................... 107–108, 112, 141

Planning rules ........................................................ 345

Pods ........................................................................... 236

PostgreSQL .............................................................. 378

Predict conversion ............................................... 318

Predicted Delivery Delay app .......................... 322

Predicted store inventory ................................. 351

Prediction class ..................................................... 145

Index

404

Prediction confidence ............................... 130, 216

Prediction ledger ................................................... 285

Prediction power ................................ 130, 215–216

Predictions ....................................................... 92, 154

Predictions dashboard ........................................ 289

Predictive accounting ......................................... 284

Predictive Analysis Library (PAL) ...... 22, 50, 52,

55, 96, 123

approach comparison .................................... 120

architecture ........................................................ 101

criteria ..................................................................... 56

extensibility .............................................. 209, 213

integration .......................................................... 101

model training .................................................. 102


Predictive analytics ......................... 22, 32, 96, 282

approaches .................................................. 49, 119

architecture ................................................... 83, 90

best practices ................................................ 34, 45

business problems .............................................. 34

decision tree .......................................................... 50

embedded ............................................................... 51

evolution ................................................................ 39

SAP Analytics Cloud ........................................... 63

side-by-side .................................................. 57, 114

technical implementation .................. 119, 148

technologies and methodologies ................. 40

trends and technologies ................................... 35

unified ...................................................................... 33

user roles ................................................................ 35

Predictive Analytics integrator (PAi) ..... 51, 161

architecture ........................................................... 98

Predictive intelligence ........................................... 19

evolution ................................................................ 22

Predictive maintenance ..................................... 354

Predictive models .................................................... 40

Predictive scenarios ................................................ 65

create ................................................... 69, 148, 150

properties ............................................................ 150

smart predict ........................................................ 68

training ................................................................ 150

types ...................................................................... 148

Preferred proposal ................................................ 257

Prepacks .................................................................... 345

Prerequisite checks .................................... 110, 163

methods ............................................................... 164

Presentation variants .......................................... 259

Procure-to-pay process ....................................... 295

Product similarity scoring ................................ 347

Production and inventory planners ............. 315

Production operators .......................................... 338

Production planners ............................................ 341

Production planning ............................................ 341

Productization ........................................................ 105

Professional services ............................................ 327

Profit and loss analysis ....................................... 287

Progressive disclosure technique ................... 224

Project cost forecasting ...................................... 324

Project delivery ...................................................... 334

Project managers ...................... 324, 332, 334–335

Project profitability .............................................. 332

Project tracking ...................................................... 334

Promotion analysis .............................................. 350

Promotion Analysis app ..................................... 350

Promotion management ................................... 346

Promotions .............................................................. 350

Public services ........................................................ 373

Purchase order processing ................................ 300

Purchase orders ....................................................... 27

bots ........................................................................... 75

Purchase requisition .............................................. 27

create as an employee .................................... 262

image-based buying ........................................ 305

intelligent approval workflow ..................... 306

process by the operational purchaser ...... 263

Purchasers ...................................................... 298, 303

Purchasing managers .......................................... 300

Python ....................................................................... 137

Q

Quality engineers ........................................ 314, 337

Quality indicator ................................................... 255

Quality inspectors ....................................... 338, 372

Quality management analytics ....................... 371

Quality systems ..................................................... 245

Quality technicians .............................................. 314

Quorum ..................................................................... 388

Quota management ............................................. 231

Quote negotiation .................................................. 30

R

R visualizations ........................................................ 64

Ranking ............................................................. 92, 258

by inherent value ................................................ 92

by score ................................................................... 92

user interface ...................................................... 258

value ...................................................................... 258

Rating ........................................................................... 92

R-based logistic regression .................................. 45

Read access ............................................................... 172

Real-time sensor data ............................................ 24

405

Index

Receivables line item matching ...................... 274

lockbox information ....................................... 277

payment advice information

extraction ....................................................... 276

Recommendation pattern ................................... 91

Recommendations ........................................ 91, 255

catalog items ..................................................... 300

items ...................................................................... 256

user interface ..................................................... 255

Redis-as-a-service ................................................. 378

Regression ......... 44, 68, 228, 291–292, 297–299,

310, 312, 316, 319, 323, 356

Regression scenario ............................................. 148

Relationship matching .......................................... 91

Remittance Advice Extractor ........................... 274

Remote APIs ............................................................ 211

Reporting ................................................................. 244

Request for quotation (RFQ) ...................... 30, 303

blockchain-verified processing ................... 307

Requests ................................................................... 238

Research and development (R&D) ................. 323

REST APIs .................................................................. 161

REST inference call ............................................... 111

Resynchronization ............................................... 171

Retail .......................................................................... 342

upcoming ideas ................................................ 350

Retraining ...................................................... 112, 244

Revenue accounting and reporting ............... 280

Roadmap .................................................................. 389

embedded predictive models ....................... 389

extensions ........................................................... 392

machine learning on SAP Cloud

Platform .......................................................... 390

SAP Analytics Cloud ........................................ 391

Robot pricing .......................................................... 363

Robot underwriting ............................................. 363

Robotic process automation (RPA) ................... 71

Root cause analysis .............................................. 336

Rule-based automation ......................................... 13

Rule-based techniques ........................................... 88

R-visualizations ........................................................ 69

S

Sales ............................................................................ 317

planning .............................................................. 319

Sales forecasts ........................................................ 319

Sales inquiries ........................................................ 264

Sales managers ................................... 264, 319–320

Sales order fulfillment monitoring ............... 319

Sales order probability ........................................ 319

Sales orders ................................................................ 31

Sales performance ............................................... 317

Sales plan .................................................................... 87

Sales quotation ...................................................... 317

predict conversion ........................................... 318

Sales quotation conversion rate .................... 319

Sales representatives ................................. 318, 322

SAP AI Business Services ................................... 384

services ................................................................. 384

SAP Allocation Management .................. 343, 349

SAP Analytics Cloud ......... 33–34, 50, 63, 96, 289


architecture ........................................................ 114

connect ................................................................... 69

create predictive scenario ............................ 148

dashboard .......................................................... 289

data access modes .......................................... 115

display prediction results ............................. 154

features ................................................................... 64

model debriefing .............................................. 152

overview .......................................................... 35, 63

process flow .......................................................... 64

roles .......................................................................... 69

smart assist services .......................................... 65

smart predict services ....................................... 67

store results ........................................................ 153


training ................................................................ 150


workflows ........................................................... 115

SAP API Business Hub ........................................ 380

SAP API Management ......................................... 380

SAP Assortment Planning ....................... 343, 345

SAP Audit Management ........................... 241–242

SAP Best Practices ......................................... 34, 269

SAP Best Practices Explorer .............................. 267

configuration guide ........................................ 269

SAP Business Integrity Screening .................. 290

SAP BW/4HANA ................................................ 33–34

SAP Cash Application .......................................... 273

lockbox functionality ..................................... 278

payables line item matching ...................... 279

receivables line item matching .................. 274

SAP Cloud for Energy .......................................... 352

SAP Cloud Platform ................. 21, 23, 50, 57, 103

ABAP environment ......................................... 377

analytics .............................................................. 377

application runtime ....................................... 382

blockchain ................................................. 379, 387

business logic ....................................................... 58

capabilities ......................................................... 376

continuous integration and delivery ....... 378

data privacy and security ............................ 378

developer tools ................................................. 379

Index

406

SAP Cloud Platform (Cont.)

integration .......................................................... 380

Internet of Things (IoT) ................. 80, 380, 386

machine learning ................................... 380, 383

master data ........................................................ 381

mobile services .................................................. 381

orchestration ..................................................... 382

rates and measures ......................................... 382

SAP Data Intelligence ..................................... 382

services ....................................................... 375, 377


user experience ................................................. 381

SAP Cloud Platform Integration ..................... 380

SAP Conversational AI ............ 72, 83, 94, 97, 223

SAP CoPilot .................................................... 224–225

SAP Customer Activity Repository ................ 343

DDF ........................................................................ 346

product similarity scoring ............................ 347

store clustering ................................................. 344

UDF ........................................................................ 343

SAP Customer Experience .................................... 25

SAP Data Intelligence ... 25, 50, 57, 96, 103, 161,

380

approach comparison .................................... 120

architecture ....................................... 60, 103, 107

benefits .......................................................... 60, 383

capabilities ......................................................... 104

components ........................................................... 61

connect to ISLM framework ........................ 147


deployment ........................................................ 104

evolution ................................................................ 24

extensibility .............................................. 210, 213

GUID ...................................................................... 162

integration .......................................................... 105

integration with SAP S/4HANA .................. 108

lifecycle management ............................. 58, 156

lifecycle phases .................................................. 105

modeler ................................................................ 180

SAP Cloud Platform ......................................... 382


training ................................................................ 107



performance .................................................. 236

SAP Demand Signal Management ................. 358

SAP Digital Boardroom ................................ 35, 373

SAP Enterprise Consent and Preference

Management ..................................................... 186

SAP ERP ......................................................... 22, 39, 47

SAP Financial Statement Insights .................. 286

SAP Fiori ................................................................... 381

SAP Fiori launchpad ............................................... 35

SAP governance, risk, and compliance

solutions .............................................................. 289

SAP HANA ................... 25, 39, 45, 52, 97, 100, 377

libraries ................................................................... 99

SAP HANA Enterprise Cloud ............................... 47

SAP HANA Optimization Function Library 349

SAP HANA Python Client API ........................... 137

SAP Help Portal ...................................................... 271

SAP Information Lifecycle Management

(SAP ILM) .................................................... 172, 185

SAP Integrated Business Planning for Supply

Chain (SAP IBP) .................................................. 350

SAP Intelligent Robotic Process

Automation .......................................................... 70

components .......................................................... 72

SAP Internet of Things .......................................... 76

benefits ................................................................... 77

overview ................................................................. 77

process flow .......................................................... 78

SAP Jam ..................................................................... 379

SAP Leonardo Machine Learning

Foundation ................................................. 59, 390

SAP Localization Hub .......................................... 378

SAP Lumira ................................................................ 22

SAP Merchandise Planning ............................... 343

SAP Predictive Analytics ...................................... 47

SAP Promotion Management .......................... 343

SAP RealSpend ........................................................ 287

SAP S/4HANA .............................................. 13, 25, 33

architecture .................................................... 83, 95


business logic ....................................................... 58

data extraction .................................................. 171

embedded algorithms ....................................... 23

embedded analytics .......................................... 33

embedded machine learning .................. 50, 97

guiding principles ............................................... 94

integration with SAP Data

Intelligence ..................................................... 108

methodology ........................................................ 85

technical challenges .......................................... 83

SAP S/4HANA Cloud ............. 21, 25, 69, 210, 282

extensibility .............................................. 212, 214

SAP S/4HANA Retail ............................................. 347

SAP S/4HANA Retail for merchandise

management ...................................................... 343

SAP S/4HANA Utilities ........................................ 352

SAP Sales Insights for Retail .............................. 346

SAP SportsOne ........................................................ 373

SAP Supply Base Optimization ........................ 372

SAP Tax Compliance ............................................ 292

407

Index

SAP Trade Management ..................................... 357

SAP Transactional Banking for

SAP S/4HANA .................................................... 368

SAP Translation Hub ........................................... 379

SAPUI5 ....................................................................... 381

Scalability ................................................................. 236

Scenario scheduling ............................................. 108

Scheduling ............................................................... 232

Scikit-Learn ........................ 140, 275, 280, 283, 326

Search to insight .............................................. 64–65

architecture ........................................................ 115

benefits .................................................................... 66

Security ........................................................................ 35

Segmentation ............................................................ 42

Self-learning techniques ....................................... 88

Semantic colors ..................................................... 259

Service request automation ............................. 362

Service Ticket Intelligence ...................... 362, 385

Side-by-side machine learning .................. 57, 96,

102, 273

ABAP development .......................................... 144

approach comparison .......................... 119, 121

criteria ..................................................................... 58

data integration ............................................... 169


inference process .............................................. 110

prerequisites ....................................................... 110

process flow ........................................................... 59


use cases .............................................................. 103


performance .................................................. 236

Signal analysis ........................................................ 152

Similarity matching ................................................ 91

Slow and non-moving stocks ........................... 315

Smart assist ................................................................ 65

architecture ........................................................ 114

consume .............................................................. 116

Smart discovery ................................................ 64, 66

architecture ........................................................ 115

benefits .................................................................... 67

Smart features ................................................. 96, 114

Smart grouping ................................................ 64, 69

Smart insights ................................................... 64, 66

architecture ........................................................ 114

benefits .................................................................... 66

Smart master data quality

improvements .................................................. 355

Smart meter data analytics ............................... 352

Smart pack sizes .................................................... 351

Smart predict ......................................... 67, 121, 328

architecture .............................................. 114, 116

predictive scenarios ........................................... 68

training ................................................................... 68

Snapshots ....................................................... 240–241

Social network analysis ......................................... 42

Solution proposals .................................................. 91

Sourcing and procurement .............................. 295

Spend monitoring ................................................ 264

Sports and entertainment ................................ 372

SQL script procedures ..................................... 53, 99

SQL views ................................................................. 119

Stability contracts ................................................ 173

Staffing and resource management ............. 331

Statement memory limit .................................. 234

Statement priority ............................................... 234

Statement thread limit ...................................... 233

Stock transport orders ....................................... 310

Stock-in-transit ..................................................... 309

Store clustering ..................................................... 344

Store video monitoring ..................................... 352

Strategic purchaser .............................................. 264

Structural risk minimization (SRM) ................ 41

Subpipelines ........................................................... 210

Supplier delivery prediction ............................ 299

Supplier invoices .................................................. 298

Supply chain management .............................. 308

Switch framework ................................................ 247

System logs ............................................................. 237

T

Target attribute ..................................................... 229

Tax compliance ..................................................... 292

Technical challenges .............................................. 83

Technical implementation ............................... 119

ABAP development ......................................... 144

APL ......................................................................... 123

application management processes ........ 155


configuration .................................................... 195


data protection and privacy ....................... 185

embedded machine learning ...................... 122

explanation of results .................................... 222

extensibility ....................................................... 202

landscape management ............................... 158

legal auditing .................................................... 238

model degradation ......................................... 215

model validation ............................................. 245

PAL ......................................................................... 131

SAP Analytics Cloud ....................................... 148


side-by-side predictive analytics ............... 148

user interface ..................................................... 253


performance ................................................. 231

Index

408

Telecommunications .......................................... 364

TensorFlow .... 277–278, 301–302, 304–306, 360

Testing ....................................................................... 245

Thing model ............................................................... 78

Ticket priority ......................................................... 220

Time series .................................................................. 64

Time series forecasting ............... 64, 68, 316, 321

view chart ............................................................ 152

Time series scenario ............................................ 149

Timestamp approach .......................................... 176

Top 5 influencing factors ................................... 306

Trade management .............................................. 357

Trade Promotions Management app ........... 359

Training ............................................ 52, 60, 165, 249

APL-based intelligent scenarios ................. 128

coding ................................................................... 141



embedded model ................................................. 98

explainability ..................................................... 227

filters ..................................................................... 128

lifecycle management .......................... 156, 159

logging ................................................................. 241

logic ....................................................................... 135

model versions .................................................. 129

pipelines ............................................ 108, 112, 141

SAP Data Intelligence ........................... 107, 147

scheduling ........................................................... 232

side-by-side predictive analytics ................ 150

smart predict ........................................................ 68

Training data source extension ...................... 201


Training dataset .................................................... 116

Transparency .......................................................... 222

U

UI theme designer ................................................ 381

Unattended automation ...................................... 71

Undeployment ....................................................... 168

Unified demand forecast (UDF) ....................... 343

Universal Journal ................................................... 285

User interface .......................................................... 252


explainability ........................................... 223–224

model validation .............................................. 251


User roles .......................................................... 35, 157

User stories ...................................................... 65, 116

Utilities ...................................................................... 352

Utilities Linear Asset Analytics tool ............... 355

Utility class .............................................................. 110

V

VC dimension ........................................................... 42

Vendor-initiated payments .............................. 279

Virtual data model (VDM) ............................. 52, 97

Volatility ................................................................... 222

W

Where-used list ............................................ 203–204

Workforce management ...................................... 25

Workforce planning ............................................. 330

Workload class ........................................................ 233

Workload management and

performance ....................................................... 229





First-hand knowledge.

Siar Sarferaz, Raghu Banda

Implementing Machine Learning with SAP S/4HANA408 Pages, 2020, $89.95 ISBN 978-1-4932-2011-3

www.sap-press.com/5158

We hope you have enjoyed this reading sample. You may recommend or pass it on to others, but only in its entirety, including all pages. This reading sample and all its parts are protected by copyright law. All usa-ge and exploitation rights are reserved by the author and the publisher.

Dr. Siar Sarferaz is a chief software architect at SAP. In this role he drives digital transformation by focusing on artificial intelli-gence and predictive analytics. He began his career as a method researcher at Siemens AG, before moving to SAP, where he has now worked for more than 20 years, holding various positions. He

is the lead architect for machine learning implementation in SAP S/4HANA and is in charge of all concepts for infusing intelligence into business processes. He studied computer science and philosophy and holds a Ph.D. in computer science.

Raghu Banda is a senior director of AI product strategy at SAP Labs, where he is responsible for infusing AI technologies in SAP S/4HANA. He began his career as a software developer and architect in India before moving to the US in 1997. He joined with SAP in 2001 and worked in various roles such as engineering de-

velopment, customer support and implementations, product marketing, and product management. He has worked with predictive analytics and machine learning since SAP entered this arena in 2012. He holds a Bachelor of Science in computer science and engineering and will soon graduate from the presti-gious INSEAD business school. He is the lead product manager for leveraging machine learning into SAP S/4HANA.

https://www.sap-press.com/implementing-machine-learning-with-sap-s4hana_5158/#utm_source=AWS&utm_medium=readingsamples&utm_campaign=Browse%20the%20Book&utm_content=2011

Documents

“Architecture” Contents Index The Authors€¦ · In this section, we explain th e challenges of applying machine learning in the context of SAP S/4HANA. Solving those challenges