How ERP improves your predictive analytics capabilities

As the manufacturing industry charges headfirst into Industry 4.0, predictive analytics has quickly transitioned from a convenience to a necessity for manufacturers of all sizes. Enterprise Resource Planning (ERP) solutions have already become commonplace in the manufacturing sector, thanks to their ability to provide a slew of data in real-time, yet not enough manufacturers are taking full advantage of ERP to improve predictive analytics for their businesses.

ERP solutions not only collect and present data in real-time, but can also combine that information with pre-existing analytics to create a more accurate portrait of a company’s efficiency, productivity, maintenance requirements, and even financial health.

By applying past data to real-time analytics, an ERP system can create forward-looking models to guide decision makers. Here’s how manufacturers can maximize their ERP solutions to advance their predictive analytics in Industry 4.0.

Leverage Big Data to guide decision making

An ERP solution acts as a gigantic repository of learned information and operational data. By incorporating entire data sets full of information instead of sampling, this Big Data allows ERP systems to find patterns and derive meaning from those patterns, supporting decision making with more accurate information. From the high level decision makers to the staff on the production floor, employees can tap into the organization’s cloud-based ERP repository to identify new opportunities to meet deadlines, improve efficiency, and surpass business goals.

The sources for Big Data may be call center interactions, social media pages, customer comments on websites, warranty histories, purchase details, or general social demographic analysis — to name just a few examples. ERP utilizes Big Data to create predictive, causal models, as well as descriptive and decision-based models.

The rewards gained from Big Data interfacing with ERP systems are multifold — not only does it collect and analyze enterprise-wide data in one place, but decision makers can access the data they need at a moment’s notice, allowing a company to become more agile in a fast-changing market.

Leverage estimated times of arrival to manage supply chains

Knowing where and when an individual ship, truck, or product is at any moment is essential for keeping your supply chain running smoothly.

Traditional Automatic Identification Systems (AIS) only provide a location within a few hours’ margin of error, which can cause undue delays. Bill McBeath, chief research officer at ChainLink Research, explains that “there are a lot of blind spots for people […] and slowness in getting information.” He continues, “So what happens is they don’t find out that something’s late until it doesn’t arrive, and then they start making phone calls.”

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ERP systems can provide near-exact estimated times of arrival, enabling a company to know where and when its goods and products will arrive — a significant improvement upon existing AIS technology. Transmitters and Internet of Things (IoT) sensors can now send information on things like temperature, shock, and ambient light to an ERP system; all of which then become part of the data stream fed into algorithms to further predict supply chain patterns.

The improved efficiency in supply chain management means a greater reduction of wasted products and time. The improved security of products via sensor data is of particular value to manufacturers in high-value industries, such as pharmaceuticals, petrochemicals, and oil.

Leverage predictive maintenance to reduce downtime and repair costs

Predictive maintenance further increases profitability by avoiding costly production stops and downtime. An ERP system can alert staff to something as simple as a single piece of machinery requiring maintenance after its one-millionth rotation. With this alert, a supervisor can schedule maintenance on the manufacturer’s terms, i.e. scheduled production downtime, and avoid the risk of extended downtime due to that piece breaking sometime in the future. This also gives staff time to plan ahead for the necessary downtime of a particular machine, and adjust production work accordingly during that time.

IoT devices monitor equipment in real-time and send information to ERP systems, providing condition-based maintenance. This saves organizations significant amounts of cash by addressing issues prior to breakdown; instead of relying on rule-based maintenance, which could miss issues between scheduled maintenance intervals.

Predictive maintenance can save between 10-40% on typical maintenance costs and reduces capital investments in new or replacement equipment by 3-5%. A McKinsey & Company report stated that, in the manufacturing industry alone, “these savings have a potential economic impact of nearly $630 billion per year in 2025.”

Perhaps most importantly, predictive maintenance can make for a healthier workforce by catching faults before they occur. Human injuries are lessened as machinery is maintained at the proper, pre-crisis moments.

ERP takes predictive analytics to the next level

Industry 4.0 is raising the bar for predictive analytics, and manufacturers must rise to meet that bar. With the increasing intelligence and connectedness of technology, competitive manufacturers are already strengthening their predictive analytics to make highly calculated and informed decisions with quick turnaround.

The digital tools for this are already available in the form of cloud-based ERP solutions and IoT devices. With improved predictive analytics, manufacturers can identify patterns and outliers in customers and products; foresee and account for disruptions or changes in the supply chain; and manage assets to optimize production schedules and limit equipment downtime. Utilizing cross-organizational platforms with user-friendly dashboards allows for internal collaboration and informed decisions that can increase output exponentially. Considering the rapid pace at which digital transformation is occurring, manufacturers cannot thrive in Industry 4.0 without reliable and real-time predictive analytics to stay ahead of the curve.