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The latest SAP S/4HANA Cloud update is now available. We’re pleased to announce more intelligent functionality in machine learning, in-memory analytics, in-context collaboration, and much more.

New features will bring impact to the entire enterprise, including all-new abilities for finance, procurement, sales, manufacturing, and professional services. Here’s how your feedback shaped this release and the next generation of intelligent cloud ERP.

Crowdsourcing Improvement Requests Using Customer Influence

Starting February 12, customers can influence product innovation for SAP S/4HANA Cloud by submitting improvement requests using the Customer Influence site. In addition to submitting new requests, users can explore existing improvement requests and vote on them on the site. This engagement shows SAP which requests are of the most interest for their customers. A comment section enables customers to interact with each other as well as with SAP’s Product Management. Furthermore, SAP is planning to integrate the Customer Influence site to SAP S/4HANA Cloud software during the first quarter of 2018 by using SAP CoPilot, the company’s digital assistant software. Customers would then be able to submit improvement requests within the application.

Finance

Gathering, analyzing, and posting A/R payment information advice from files is time consuming and prone to errors. The new automated payment advice processing is powered by machine learning and  SAP Leonardo. You can now turn documents into structured data in your cloud ERP with automated extraction of payment information from PDF documents.

If you’re in a company that wants to monetize IoT, cloud services, or digital content through on-demand payment, meet the SAP S/4HANA Cloud for contract accounting and invoicing. It offers compliant features to automate payments, manage high-volume convergent invoicing, and embedded analytics — all of which optionally integrates with the SAP Hybris Revenue Cloud.

For the first time, accountants can look forward with predictive accounting for incoming sales orders. This automates an update each time a new sales order is created, posting pipeline revenue to a predictive ledger.

Sourcing and Procurement

A new situation handling tool for purchase order confirmations and professional purchases requisition proactively alerts users to risks and automates proactive customer communications when delivery dates are at-risk. You can also benefit from one-click insight into risks and opportunities, and start requisition activities the moment new contracts are created.

The central SAP S/4HANA procurement hub centralizes your purchasing activities with features for contracts, requisitions, purchases, and returns. You can now create and distribute purchases among subsidiaries in a connected ERP backend system, improving conditions with on-demand, local purchases. You’ll also get a graphical overview of documented down payments on purchase orders, which allows drill-down insights to optimize your liabilities and intelligently plan cash flow. Organizations can improve the grip on purchase order spend with a single dashboard in the SAP Analytics cloud.

Sales

Sales managers can use machine learning to understand probable orders and predicted sales volume for better forecasting with the predictive quotation conversion rates calculator.

We’ve also added two new features for organizations with sales subsidiaries that don’t manage inventory on-site. Advanced shipment notifications (ASNs) eliminate a lot of manual paperwork by automated purchase orders to headquarters and client invoicing.

New two-tier ERP purchasing feature allows HQ to perform more efficient inventory management by creating automated sales orders when purchase orders are sent by subsidiaries.

Asset Management

The new maintenance planning overview allows you to plan work, monitor time-sensitive processes, and analyze trends time. It’s an all-in-one environment to understand maintenance health and time-sensitive follow-up needs with real-time insight into reports, maintenance orders, and potential bottlenecks.

The technical object damages app provides a 360-degree view of frequent damages and root causes, with both key performance indicators (KPIs) and drill-down features. It’s built to show you which assets have become a bottleneck, and most importantly, help you take corrective action.

Building on release 1708, we’ve also added all-new technical objects for task lists and task lists operations. Say goodbye to manual data entry and meet simpler task management with filters, advanced search, and layout management.

Manufacturing and Supply Chain

SAP S/4HANA Cloud for demand driven replenishment is now certified for compliance with the Demand Driven Institute for demand driven material requirements planning (DDMRP) implementations. Users can protect material flow with features to monitor buffer status and recommendations on replenishment actions. There’s also flexibility when you need to account for seasonality, new product releases, or discontinued products.

There are fewer surprises in store, thanks to the release of new features to track production yield, scrap, and quality in real time. You’ll also gain the ability to confirm complete or partial production, manage quantities with batched production, and record the use of components.

We listened when you said you need more simplicity and transparency over product allocation, including visibility into your sales documents. You can now check allocation against orders, change sales orders, plan quantities, and see quantities assigned to your sales orders — all without charge.

Speaking of visibility, a new statistical process control makes it simpler than ever to monitor processes using quality control charts. We’ve built a lot of freedom and flexibility into features to document, analyze, and monitor with graphs and charts. You can define KPIs, track changes in values over time, and both calculate and set warning limits for upper and lower thresholds, in addition to tools for Shewhart and acceptance stats like mean values, standard deviation, and qualitative characteristics.

Other features in this release include support for remotely monitoring of picking activities — simple, real-time insight into order status and tools for corrective action when there are deviations, delivery cancellations, or goods issues. Finally, you’ll communicate smarter with public APIs for return deliveries and automated messaging for advanced shipping notifications.

Portfolio and Project Management

Project financial controllers need visibility across teams of project managers to monitor these projects’ financial health. With the new overview page, you can understand the present and future. Controllers can easily compare planned and actual costs to date by cost component, understand commitments over time, and access an overview of planned projects.

Professional Services

Professional service firms often elect to offer non-billable services to accommodate use cases like onboarding, client support, meetings, or training. Resource managers now have transparency around non-billable services in the release billing proposal application. You can flexibly plan and verify non-billable hours in work packages and proposals, and automatically write-off these hours before billing the client.

SAP S/4HANA Cloud User Community

The SAP S/4HANA Cloud User Community is an interactive platform available to all SAP S/4HANA Cloud customers, partners, and SMEs to exchange information with one another about products, solutions, and best practices. Customers can gain real time access to information, crowdsourcing for Q&A, and self-service knowledge base, as well as gather advice from peers, partners and experts. Empowering customers to be successful with a new leading-edge product, community members have access to content, support, and resources to drive success through knowledge sharing and collaboration. Users can access community from SAP S/4HANA Cloud using web assistant.

Conclusion

1802 is the first of four releases scheduled for 2018. With three more updates, there’s much more value in store. To learn more, visit the SAP S/4HANA Cloud website or check out our high-level 1802 release videos on the SAP S/4HANA Cloud playlist on YouTube.

Christian Pedersen is chief product officer for SAP Cloud ERP. http://bit.ly/2CR8mkY #SAP #SAPCloud #AI

In case you are catching the train running, here is the link to the introduction blog of the Machine Learning in a Box series which allow you to get the series from the start. At the end of this introduction blog you will find the links for each elements of the series.
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Before we get started, a quick recap from last week

Last week, we saw how a project methodology could help you become successful with your Machine Learning projects.

Here is a link to a quick recap Machine Learning in a Box week 2 recap, I wrote before starting this one about Algorithms Learning Styles. You will find some personal thought about the CRISP-DM methodology.
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Welcome to week 3 of Machine Learning in a Box!

Algorithms Learning Styles

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When I started my journey at KXEN, I didn’t have a pedigree in data mining or data science. I have a tech support and programmer background. So, I have an understanding of what the word algorithm means in term of programing, and I discovered that for data science there is no difference.

When we were at school, we all solved algebra problems like “Find the equation of the line that passes through the points (-1 , -1) and (1 , 2)” or “Find the minima and maxima of the function f(x)=x4−8×2+5 and f(x)=x4−8×2+5”. And we did that manually… by applying an algorithm we learned during our classroom study.

And there are plenty of algorithms to help you solve a single type of problem, which in our Machine Learning project is usually represented by our data mining goal.

So, we need a way to organize our toolbox of algorithms. There are many ways to organize and group algorithm together, and here I will use something called the “Learning style”.

Using the “learning style” helps you think about how you will be preparing and using your data to build your model. Ultimately, you will try and pick the most appropriate algorithms to test and compare results.

Let’s take a look now at the main learning styles for machine learning algorithms, and the associated sub-categories.
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Supervised Learning

With supervised learning, you will infer a function using a set of labeled data where the outcome (the target) is known. This dataset is also known as the training data set.

The training dataset can be represented as a pair consisting of an input vector of features (or variables, dimensions) and the associated output value,

Therefore, the goal of a supervised learning algorithm is to analyze the training data and produces a function that can score new input vector of features and get the predicted output value.

This will require the algorithm to generalize patterns (in the inferred function) from the training data in order to correctly determine the output value for any new and unseen input vector of features in a “reasonable” way.

There is a wide range of supervised learning algorithms, and they all come with their strengths and weaknesses. This implies there isn’t a “magic” algorithm that can address all supervised learning problems.

You can be further group supervised learning algorithms like this:

* Classification

This is applicable when your target is represented as a category or a class, like “true” and “false” or “A” and “B” for a binary classification, or “A”, “B” and “C” for a multi class classification.

The following diagram depict a simple classification example where each icon is positioned based on its input value (x1 & x2 axis) and colored based on the output value. The inferred function is the green line (linear function here), and each question mark are new input that the inferred function will assigned to one side or the other.

* Regression

This is applicable when your target is represented as a continuous number, like a financial revenue, a weight or a temperature.

The following diagram depict a simple regression example where each mark is positioned based on its input value (x axis) and the output value (y axis). The inferred function is the green line which can get you the “y” output value for any “x” input value.

* Time series forecasting

This is applicable when your training data set represent a signal or a series of value where you need to infer the next N values using the previous data.

Some people may argue that time series forecasting is a kind of regression, except that the inferred function for time series will produce a series of values instead of a unique value like in a regression.

In addition, the data set structure for time series requires an “order” column with unique values (usually a date, but could be an increment column in some cases).

The following example show a series of point at fix interval, the blue dots. The time series algorithm inferred function (the green line) represent a cosine function that can be used to predict the 5 next values (the red dots).

To summarize the big difference between a classification and a regression is the representation of the target variable (the output), where one is discrete (categories) and the other is continuous.
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Unsupervised Learning

As opposed to supervised learning, with unsupervised learning, you will infer a function using a set of unlabeled data (no defined outcome).

Therefore, the inferring function is meant to describe hidden underlying structure and patterns or distribution in the data. Unlike supervised learning, there is no real way to evaluate the accuracy or relevance of the found structures and patterns.

You can be further group unsupervised learning algorithms like this:

* Clustering

This type of algorithm is applicable when you need to define groups of entities (a.k.a. clusters) based on the “similarity” or “distance” of the entity attributes compared to the overall distribution. Each clustering algorithms have their own grouping strategy either based on distance to a center, the group density, the group distribution etc. just like some will allow or prevent overlap, or the presence of residual items.

In the following example, the algorithm has defined 5 clusters using the distance to the center.

* Association rules

You can apply this type of algorithm when using transactional dataset linking items together or users to items, and your goal is to extract rules about the relation. A common rule example can be that you buy X when you buy Y. Off course, rules can be longer where multiple items are involved or enforce a certain sequence.

In the below example, a set of rules is extracted from a series of user shopping transaction.

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Other Learning Styles

Semi-Supervised Learning

With semi-supervised learning, only a portion of the input data is labeled, which means that the algorithm must learn the structures to organize the data as well as make predictions.

It can become really expensive and time-consuming to label all your data, or worse they could be wrongly labeled.

If you take an image library as an example, only a small portion of the images will be labeled.

Therefore, both unsupervised and supervised techniques are leveraged to make the best use of unlabeled data by clustering them with labeled data or make best guess predictions, and use all that to build the model.

Reinforcement Learning

With reinforcement learning, the algorithm tries to find the “best ways” (a sequence of decisions or actions) to earn the greatest “reward”.

Typically, at every step a decision is taken in an environment that lead to a reward and a state. By performing this many times, the algorithm is able to learn how to improve its decisions and its ability to earn greater rewards.

References

To write this blog I leveraged several sources for inspiration, details, idea. I’ll try group them here by alphabetical order:

* Machine Learning 101 by Towards Data Science
* Machine Learning Explained by Ronald van Loon

* Wikipedia:

* Reinforcement learning
* Semi-Supervised Learning
* Supervised Learning
* Unsupervised learning

Conclusion

I hope that this blog help clear some lingo around Machine Learning, and help you understand that these algorithms are here to help you to produce the best “functions” using your training data (labeled or not) that you can then apply to new sets of data.

Next week, we will start looking at what to install to get started. So, get your internet connection to download SAP HANA, express edition and some additional components and tools.

Quick question to you guys:
Would you find it useful to use slack to discuss this blog series and engage?
Any other proposal is welcome!

(Remember sharing && giving feedback is caring!)

UPDATE : Here are the links to all the Machine Learning in a Box weekly blogs:

* Introducing “Project: Machine Learning in a Box”
* Machine Learning in a Box (week 2) : Project Methodologies
* Recap Machine Learning in a Box (week 2) : Project Methodologies
* Machine Learning in a Box (week 3) : Algorithms Learning Styles http://bit.ly/2F2oUMV #SAP #SAPCloud #AI