Part 2 in a 3-part series. Read Part 1 and Part 3.
The complexity facing business has never been greater. Information pours into databases at unprecedented speed and from sources unimaginable even just a few years ago.
Information on customer sentiment, employee performance, market movements, work-in-progress status, financial positions, project completion, and countless other sources is about to be dwarfed by the data generated by the Internet of Things (IoT). There’s gold in this data, but most businesses don’t have the tools to extract it.
In their 2016 Big Data Dilemma report, members of the U.K. House of Commons Science and Technology Committee wrote: “Despite data-driven companies being 10% more productive than those that do not operationalize their data, most companies estimate they are analyzing just 12% of their data.”
So, the question is not “should we operationalize our data?” Rather, it about “how can we reduce complexity to uncover the insight and advantage locked in the data?”
The machines have the answer
The answer is to leverage machine learning. It’s available now and allowing all functions to evolve processes, continuously innovate, and adjust planning and delivery so businesses can meet market changes and customer expectations.
So, how can you use the real-time context available to you via intelligent cloud ERP to deliver value? Here are five examples of how machine learning can do just that.
1. Finance: Accruals
Machine learning could help cut through the myriad factors finance teams consider when determining bonus accruals. Current headcount, salaries, and bonus plans are the starting point, and CFO teams try to forecast all key performance indicators in compensation plans. From there, they try to calculate the most accurate accrual (likely adding a buffer, to be safe). However, accuracy often ends up being a matter of luck more than anything else.
By applying machine learning to these calculations, predictive analytics could serve as a valuable tool to generate unbiased accrual figures, leaving finance teams more time during closing periods for other activities that require human review and judgment.
2. Procurement: Contract negotiation
Strategic procurement is a complex process involving a wide range of information and continuous supplier communication. Its costs go directly to the bottom line, so anything that improves efficiencies and reduces inventory will make a material difference.
Machine learning can mine historical data held in your system to predict contract lifecycles. It will forecast the point in time when a purchasing contract is expected to be consumed 100%. With this new innovation, you can make sure that you renegotiate purchasing contracts to suit actual needs – not take a best-guess approach – and adjust your business as required.
3. Sales: Project bidding
Assessing whether to bid for commercial projects means evaluating each opportunity individually based on the project characteristics – size, complexity, skills available, potential for overrun, and so on. To qualify this assessment, businesses depend on managers who have previously worked on similar projects. That can limit decisions to the individual perspective of those managers.
Machine learning could give sales and project teams the power to access decades worth of projects from around the world at the touch of a button. In leveraging these insights, teams could then develop a better-informed assessment, mapping the project against a much larger database of historical projects. Ultimately, machine learning can help firms decide whether to bid, what level to bid, and how to plan projects to ensure profitability.
4. Marketing: Getting closer to the customer
TDWI research indicates that marketing is often one of the first groups in a business to make use of more advanced technology to better understand customers. In marketing, machine learning is often used for customer segmentation and to provide customers with the “next best” offer.
A learning model can be trained on how customers with similar characteristics responded historically to an offer. Other use cases include up-selling, cross-selling, and operationalizing machine learning in recommendation engines.
5. Manufacturing: Increase production yields
Increasing production yields by the optimization of team, machine, supplier, and customer requirements is already happening, thanks to machine learning.
Using machine learning to connect the data from thousands of automated parameters all reporting in real time is a game changer. Ensuring that the quality of products meets ever-increasing customer expectations can increase sales as well as eliminate waste. At the same time, issues that could halt production can be anticipated and maintenance scheduled to fix problems before they arise.
The technology to rebuild processes across all functions already exists, so there’s every reason to leverage machine learning to your advantage today. Find out how SAP S/4HANA Cloud can help you.