A Day in the Life of…. – Can Machine Learning Improve Employees’ Lives?
Why Machine Learning?
The digital economy is evolving, sometimes disrupting what was previously established as successful. New technologies such as machine learning, conversational capabilities, and predictive analytics have started to impact not only the way we work but also the way we live and interact socially.Companies increasingly understand and accept this:
They need to adapt, be faster, scale better, and innovate.
To stay ahead of their competition, companies must leverage the technologies mentioned above, and integrate them into next-generation processes that help them streamline the ways they accomplish their everyday tasks: They must transform into intelligent enterprises.We at SAP have understood this and do everything to support this transformation, now, but even more so in the near future.
Let’s take a quick, day-in-the-life look at an example of how the new business processes built with machine learning that we already delivered with SAP S/4HANA Cloud, and those nine net new ones that will come as part of the SAP S/4HANA Cloud 1805 release, might impact your day as an employee.
Imagine the following…
Many senior executives start and end their day with an eye on sales and revenue performance – reviewing pipelines, visiting customers, engaging in strategic sales opportunities. With the help of machine learning as part of your order-to-cash processes, you gain new intelligence for better decision making.
Sales Forecast for Sales Managers
A sales plan is a strategy that sets out sales targets and tactics for your business, and identifies the steps you will take to meet your targets. Creating such a sales plan is traditionally a very manual process requiring a lot of human expertise and judgement. Using machine learning and predictive analytics, SAP S/4HANA Cloud will allow sales managers to more accurately forecast total sales, set targets, and create the right sales plans to achieve their revenue goals. Better insight – through real-time comparison of planned, forecasted, and actual sales – allows companies to assess risks ahead and, consequently, double-down on successful strategies while changing or abandoning others.
Quotation Conversion Probability for Sales Managers
For many businesses, managing the pipeline of potential orders is a vital and time-consuming exercise. Typically, entering the probability that an order will close into the system is a manual effort, based, above all, on the seller’s expectation and estimates. Applying machine learning to historical sales pipelines drastically reduces these human calculation efforts, as the system is now able to predict the order conversion probability on its own. This obviously increases accuracy, as employees have less freedom to (mis-)interpret data or do guess work. Even doing real-time risk analyses is possible with machine learning, which helps businesses to stay on top of the game.
Of course, you can’t sell a non-existent product. To create or procure the products you need for your business, you first have to acquire the required raw materials or components.
Contract Consumption for Purchasers
Buying goods at the best possible price is key for a successful enterprise. Establishing contracts with your seller is an important instrument to fix the prices for the planned quantity of goods for a given time period. Harnessing the machine learning capabilities available with SAP S/4HANA Cloud, you and your procurement organization can receive proactive notifications that are based on actual and forecasted consumption. This allows you to negotiate your next supply contract far ahead of time, before the contracted quantity of goods is coming to an end. This is exactly the step ahead of your competition that you need to be to negotiate the best conditions.
Propose Creation of New Catalog Item for Purchasers
While many goods are usually well-defined, other items that a company procures may seem random. As your employees find the tools they need to do the job, they often enter free-text purchase requisitions, which (in contrast to purchases directly from a catalog) generate extra operational costs and, even more, often prevent efficient price negotiations. SAP S/4HANA Cloud will soon be able to evaluate purchasing practices by means of machine learning capabilities that help spot similar purchases and suggest the creation of new catalog items, when and as required. This will drastically reduce manual processing efforts and even errors, thereby helping companies to reduce their overall total spend.
Cash Discount at Risk for Accounts Payables Clerks
Organizations with a solid payment planning can optimize cash discounts based on their vendors’ payment terms. Purchase orders that do not match with invoices in the system can result in payment blocks and will, consequently, put the entire cash discount at risk. The Cash Discount at Riskmachine learning service automates the handling of such exceptions and prevents the blocking of payments, thus avoiding the loss of cash discounts. Machine learning helps accounts payables clerks save time and money for their organizations, helping them stay in the lead.
Cash Application for Accounts Receivables Clerks
As your organization fulfills its sales orders, you need to be able to collect and process payments efficiently and effectively. Here again, with machine learning, businesses can free their employees from repetitive manual activities, allowing them to focus on higher-value tasks. SAP S/4HANA Cloud already harnesses machine learning capabilities to match payments with invoices (see above). Unlike traditional solutions, though, the solution does this without relying on manually maintained and complex rulesets. Instead, our machine learning service uses historical clearing data that is stored in the system to automatically match it against other data.
Goods Receipt/Invoice Receipt Monitor Status Proposal for Accountants Payables Clerks
When it comes to paying for your procured goods and services, machine learning can dramatically reduce the number of manual tasks that need to be carried out, particularly in cases where the goods and invoices receipts do not match. This exception handling process used to involve several mundane routine manual steps. As of SAP S/4HANA Cloud 1805, machine learning speeds up this process by eliminating the manual collaboration process and proposing the best next action based on historical data. As such, time, cost, and efforts can be saved, and vendor relationships improved.
Demand-Driven Replenishment for Planners
With SAP S/4HANA Cloud, you will, by applying our machine learning technology, soon be able to benefit from demand-driven dynamic buffer level adjustments. Using historical lead times for stock transfers and your respective business context as a basis, the machine learning service can then help you continuously optimize your stock buffers. This will allow you to better serve your customers while also minimizing the amount of capital tied up in safety buffers.
Predicted Delivery Dates for Planners
Large enterprises manage complex supply chains within their organizations. Stock in transit deals with the shipments between units within an organization. With the new Predicted Delivery Datesmachine learning service, planners can harness SAP S/4HANA Cloud to predict the delivery dates of their shipments based on historical data using statistical calculations. This provides planners with more accurate delivery dates that allow them to anticipate delayed shipments and, as such, mitigate production risks.
Project Cost Predictions for Project Financial Controllers
Organizations of all sizes often have to deal with complex projects that need to be completed in time and without budget overruns. For this reason, project financial controllers need to spend much of their time calculating the costs forecasted for a project. The latest machine learning capabilities available with SAP S/4HANA Cloud now allow them to use historical project data to make accurate cost predictions for the planning and execution of future projects. The system is, in fact, so intelligent that calculations are automatized, which, obviously results in reduced error rates and more accurate data.
Summary: Machine Learning is at the Heart of an Intelligent ERP
Thanks to the power of machine learning and new business processes, intelligent cloud ERP has helped your entire company fit more than 30 hours of work into a 24-hour day. Rather than just improving your existing business processes, it is a complete rethinking of how business can be done. Perhaps just as important as machine learning, the SAP HANA in-memory platform allows you to have up-to-date intelligence and in-the-minute processes every minute of the day. No more waiting on weekly or overnight batch processes.
Today’s market place is moving fast. Tomorrow’s will be faster. Keeping up is no longer enough. You need to run ahead with SAP S/4HANA Cloud.