Imagine you own a fleet of trucks, one of which has low pressure in its left front tire. Is this a big deal? Should you call the truck in for a new tire right away or wait a day? An experienced fleet manager might have a “gut feeling” about the risk of a low-pressure tire. He or she might be mistaken, but familiarity with trucks helps guide the decision to repair or wait. Now, with SAP Predictive Analytics, the company can make an accurate prediction about how the tire’s safety using past data about other trucks’ tires.
Predictive analytics applies the disciplines of Artificial Intelligence (AI) and Machine Learning (ML) to predict future events. In some ways, predictive analytics mimics the “gut” of an experienced person—comparing multiple pieces of information to arrive at an informed opinion. However, unlike the subjective human gut, a predictive analytics model considers vast quantities of data and applies algorithms to get to a more precise interpretation of the situation.
Thus, while the fleet manager may think a problematic left tire is not a big deal, an SAP predictive model can compare the truck in question with thousands of other examples of tires that caused accidents. It can compare that tire to others along hundreds of data points like make, model, and age as well as other factors like whether or not the driver is known for having accidents in the past and so forth.
Predictive analytics helps business managers and strategists anticipate future behavior and outcomes. It guides better, more profitable decision making. Examples include diagnostic decision making in medicine and anticipating consumer purchasing patterns. Finance and HR can finally nail down cash flow issues, simulate budgets, run profitability and margin analysis and run employee retention numbers. Sales and Marketing can score sales leads, optimize campaigns, segment customers, and reduce churn using analytics.
SAP Predictive Analytics Software
SAP Predictive Analytics Software enables IT departments to create, deploy and maintain thousands of predictive models. It’s an on-premises product with complete connectivity to Big Data and third-party data sources. It enjoys native integration with the SAP ecosystem.
The software allows ERP teams to leverage machine learning data. By creating multiple models across the production ecosystem, analysts employ a browser-based scenario modelling tool that can be amended to reflect other data sets without going back to the modelling step. Teams can identify Key Performance Indicators (KPIs) for each line item in production to uncover a predictive lifecycle and thus make cost-effective and customer-facing decisions to support business strategy.
Best Practices for Making an Impact with SAP Predictive Analytics
The tool has great potential. Implementation can be spotty or non-existent, however. There are a variety of reasons for this, but most of them can be addressed through best practices. This is the time to focus on the issue. Companies are asking their IT teams to develop a workable, business-oriented predictive analytics capability as soon as possible.
Inhibitors of predictive analytics include siloed organizational structures and shortages of analytical talent. Having the predictive analytics tool is not enough. It takes people, people with highly sophisticated skills, to train a machine to make predictions. For instance, SAP roles within IT departments at small or medium businesses often have too few people who are dedicated to data analytics as a whole. Even large companies may not have the resources and skillsets necessary to make predictive analytics work effectively. Many industries resolve this dilemma by employing consultants to support their predictive analytics activities.
Working with many companies on data analytics programs, we recommend the following best practices to move forward with predictive analytics:
- Focus – Find the right ERP metrics that indicate success. This may include machine learning feedback to tighten the supply chain or ensure higher returns on AP numbers.
- Adopt – Cross-functional teams work the best with “an analytics center of excellence (COE)…to validate insights” and using those insights with job titles like “technology architect,” “data scientist,” and “business analyst” in high-performing “pods” of personnel that can move flexibly and provide the most analytic power to the business.
- Adapt – Streamline adaptation so organizational bottlenecks don’t prevent adoption. This means identifying areas that might hold up your SAP ERP team’s ability to respond. Predictive analytics is an opportunity to create more agile decision-making processes like those of a high performing organizations. Deploying a COE to ensure adaption is on track will help you better respond to inevitable challenges in adoption.
- Activate – Virtuous cycles enable organizations to learn from data output, as well as by doing. Course correction and process improvement programming will help your company lock in the type of learning that supercharges your predictive analytics programs to benefit your business.
Partnering with You for SAP Predictive Analytics Success
As our experience shows, predictive analytics comes to life when it is part of a broader data management ecosystem. We can work with you to implement SAP Predictive Analytics Software as an integral part of your SAP HANA, S/4HANA and SAP cloud environment. This way, you can leverage the data you’re already managing for optimal predictive benefit. Let’s talk. We can discuss how to get your SAP infrastructure to support business intelligence and predictive analytics appropriately.