Expect the Unexpected: Predictive Analytics in Intralogistics

Many fairy tales feature a powerful sorcerer who gazes into a crystal ball. The resulting prophecies are usually rather vague and open to interpretation. Things are different in the world of intralogistics, where the rule of thumb is: The more data there is, the better the predictions. And you certainly don’t need a sorcerer or a crystal ball for that. Instead, you need sensors, systems, measurements, calculations, and empirical data.

The Linde H35-H50 series forklift trucks from KION subsidiary Linde Material Handling are a good example of this. The trucks are networked via a cloud server. This ensures transparency and efficiency, and provides real-time data about the forklift in use. “This data can be used to predict maintenance intervals based on previous usage patterns,” explains Jeremy Caddick, Vice President of Service for KION Industrial Trucks & Services EMEA. “This allows us to intervene before any disruptions can affect the customer’s business.”

Looking into the (intralogistics) crystal ball is no gimmick, but rather helps customers to plan more precisely and increase safety in the production process. And networking also plays a part when it comes sustainability, namely in the form of asset life cycle management. The KION Group wants to be emission-free by 2050, and most customers have similar goals. These sustainability efforts depend upon being able to efficiently use products and systems over longer periods of time, sometimes over several decades. Caddick says: “To achieve this, we need precise information on how much effort and what costs are required to maintain and operate a plant in a sustainable way. The more data we have about the plant’s life cycles, the more accurate the prediction becomes.”

For example, a more preventive approach to maintenance can be put in place. You can take a closer look and ask: Which components are causing problems? And when? And why? “Detecting failures within the life cycle allows us to adjust maintenance plans and strategies accordingly. We can replace the component before it fails, avoiding industrial truck downtime and business interruptions for the customer,” explains Caddick. “And better still, we can discuss this data with our suppliers and production, and improve the reliability of the components, or even switch to more sustainable alternatives with a longer service life.”

Instead of repeating past mistakes, they can be avoided in the future. Looking into the crystal ball shows where there is a need for action. This means that, in future, forklifts can be adapted to changing customer requirements over their entire life cycle—even to those requirements we don’t yet know about.

There’s a humming sound at the charging stations, which must mean it’s time for the lunch break! Several Linde X20 series Li-ION forklifts are connected so that they can be made ready for their next job. When it comes to ensuring that unforeseen power outages do not occur when charging so many trucks at the same time, the control software developed by the young company ifesca (which has entered into a strategic partnership with the KION Group) plays an important role. Peak power management refers to efficient management of internal company energy networks.

AI is also used to collect and merge data from various sources, meaning that it can forecast electricity generation and consumption two to three days in advance. This more tangible look into the crystal ball allows users to act in a more resource-efficient and forward-thinking way. Energy is this decade’s hot topic, and ifesca is making a huge strides toward using energy in a more professional way than is currently the case.

Making decisions based on outlooks relies on algorithms, high data quality, and the Internet of Things (IoT). This term describes the networking of physical and digital objects. The more comprehensively and accurately the individual assets in a warehouse communicate, the better. This principle lies at the heart of the iQ InSights software developed by KION brand Dematic. The software combines all operational and maintenance information into a digital action plan for customers. The goal here is clear: Optimizing warehouse management through predictive data analytics. This goes far beyond predictive maintenance planning. Predictive maintenance also makes it possible to anticipate operational bottlenecks—for example, during peak phases such as the run-up to Christmas—in good time.

Dematic iQ software detects all influences that could lead to an interruption in the flow of goods in advance. Real-time notifications keep warehouse managers up to date and enable them to put countermeasures in place. Dematic InSights can also be used in conjunction with a computerized maintenance management system (CMMS) and Enterprise Asset Management (EAM), forming an efficient system capable of independently creating maintenance schedules and work orders.

Until now, every KION subsidiary has been using its own crystal ball—but that is all about to change. Jeremy Caddick says: “The KION Group’s ERP systems are undergoing radical change on a global level. We are striving for comprehensive process harmonization.” ERP stands for Enterprise Resource Planning, used for group-wide planning and calculation of resources, capital, personnel, and tasks.

“Shared knowledge and collective experience, alongside common processes and data structures—that’s what we’re working towards in the long run,” explains Caddick. The goal is a data pool from which all KION Group subsidiaries can obtain important insights and information. In his view, the gradual harmonization of data in combination with powerful analytics and AI can bring about a global business transformation and, in turn, make full use of the potential that is already unfolding as a result of integrated systems and collective know-how.