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Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors
Södertörn University, Sweden.
Uppsala University, Sweden.ORCID iD: 0000-0003-0491-2122
Bestwood, Sweden.
Linnaeus University, Faculty of Technology, Department of Informatics.ORCID iD: 0000-0003-4301-478X
2019 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 11, no 23, article id 6708Article in journal (Refereed) Published
Abstract [en]

Increasing the fuel efficiency of industrial machines through digitalization can enable the transport and logistics sector to overcome challenges such as low productivity growth and increasing CO2 emissions. Modern digitalized machines with embedded sensors that collect and transmit operational data have opened up new avenues for the identification of more efficient machine use. While existing studies of industrial machines have mostly focused on one or a few conditioning factors at a time, this study took a complementary approach, using a large set of known factors that simultaneously conditioned both the fuel consumption and productivity of medium-range forklifts (n = 285) that operated in a natural industrial setting for one full year. The results confirm the importance of a set of factors, including aspects related to the vehicles’ travels, drivers, operations, workload spectra, and contextual factors, such as industry and country. As a novel contribution, this study shows that the key conditioning factors interact with each other in a non-linear and non-additive manner. This means that addressing one factor at a time might not provide optimal fuel consumption, and instead all factors need to be addressed simultaneously as a system.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 11, no 23, article id 6708
Keywords [en]
digitalization, complementarities, sustainability, fuel consumption, heavy-duty equipment, productivity, sensor data
National Category
Information Systems
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-90291DOI: 10.3390/su11236708OAI: oai:DiVA.org:lnu-90291DiVA, id: diva2:1373622
Available from: 2019-11-27 Created: 2019-11-27 Last updated: 2019-12-17Bibliographically approved

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