Forecasts and performance indicators make forklift fleets more efficient

November 16, 2021 | In this interview, Christoph Hohmann explains how and why data is being collected and processes tracked using technology.

Christoph Hohmann’s role at the Fraunhofer Center for Applied Research on Supply Chain Services SCS was to develop solutions for Industry 4.0 processes. He collected data on forklift transports in order to make their use more efficient, identify weak spots, and pave the way for lead time forecasts. In this interview, Christoph Hohmann explains how all this works.


Your contact for this topic is now Moike Buck, head of the Process Analytics group.

Efficient organization of forklift fleets

Why is it important to optimize forklift routes?
Christoph Hohmann: Forklifts are a valuable resource in industrial operations. They ought to be used as efficiently as possible because they help determine whether or not operations run on schedule. To minimize the risk of accidents, vehicles shouldn’t cross paths too often or get in each other’s way.

What forklift data do you collect for this?

We start by collecting data on the status quo: where each forklift is at any time and what load it’s carrying. This allows us to determine how long it took to complete a job, what route it took and when it started the next job. We then generate performance indicators for all industrial trucks, which in turn enables us to ascertain what the utilization was and how efficiently the active time was actually used.

How do you collect data on an entire forklift fleet?
At Fraunhofer IIS, we’ve developed various positioning technologies. We equip the industrial trucks with mobile positioning systems and with sensors. Depending on the customer company’s requirements, we collect data on, say, load status. We track the journeys by running the positioning data through a piece of software that analyzes it to map the respective route and status.

What else can you learn from positioning data on industrial trucks?
The initial analysis of the data shows the utilization of each industrial truck over the entire acquisition period. More detailed observation of each individual vehicle reveals information such as if there were any variations between different days or shifts. If there were, we take an even closer look, applying the performance indicators to determine, for instance, if lead times vary greatly due to overcomplicated loading activities. The analyses depend on solid plausibilities and interpretations of the data collected.

What actions and forecasts can be derived from this and what benefit is there for companies?
First, the current system allows us to perform a comprehensive retrospective review and produce a number of analyses that companies can build on when planning what actions to take. Moreover, we’re currently also investigating how to create lead time forecasts based on real conditions. The data on past operations helps us identify and understand the factors that had an effect on lead times. This means that in the future, will we be able not only to forecast lead times, but also to use these forecasts to take action in time to optimize them.

 

About Christoph Hohmann

Christoph Hohmann worked at the Fraunhofer Center for Applied Research on Supply Chain Services SCS from 2013 until mid-2021, focusing on digitalization in manufacturing and logistics. His research ranged from how technologies are implemented to the qualitative and quantitative use of data for process management at industrial companies.

 

 

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