Logistic requirements, environment and costs

The main objective of LIMCO (Logistics, Environment and Costs) has been to generate knowledge that can improve the efficiency and reduce the environmental footprint of freight transport with trucks, by utilizing transport and logistics data that only a few years ago did not exist. The project involved data capture from freight vehicles over a period of nearly three years. Data capture started in January 2019 and increased throughout the project?s life both in terms of the number of transport firms and vehicles included, and, as a result, the number of GPS positions and vehicle days. Today, the database includes close to 250 million positions from ca. 1650 trucks and 200 vans. Within the project, methodological developments were carried out for converting GPS-positions into trips and stop observations. This work draws on existing methodology from the international literature but has been developed further to make it better suitable for city distribution, both with vans and with trucks. In cooperation with Statistics Norway, our methodology was validated against trips reported to its survey of trucks, for matching vehicles and periods for which both GPS-data and survey reports were available. The work has been extended by linking GPS-data to shipment data for one of the participating firms. This proved challenging both because we lack one-to-one links between vehicle and shipments, because data volumes are large, and because many shipments are delivered using vehicles not part of the vehicle-data capture. Further, time stamps on parcel deliveries can deviate from when vehicles were at the delivery location due to time stamp configuration errors in one of the databases. Our methodology was documented in a paper (presented at the 2020 NOFOMA conference), which was later developed further towards an article currently under review at a scientific journal. Through the project, we further developed detailed cost models for transport and logistics, in which results from GPS-data analyses are used as inputs. This makes the cost models as relevant as possible for specific firms, but also for different transport segments and for further use in a National freight model, a model used by Norwegian transport authorities in assessments towards the National Transport Plan. Firm-specific cost models have been used by participants as basis for developing more transparent transport contracts and continuous improvement of how they organize their transports. Data capture within the project also included data on fuel consumption and driver behavior. Fuel consumption information forms useful inputs for both firm-specific and more generic cost models (e.g. the National freight model). One challenge has been that the data do not include information on how much freight vehicles carry or how this influences fuel consumption. Nevertheless, the data have yielded good estimates on fuel consumption from real-world driving and on how fuel consumption varies with daily mileage, where driving takes place, engine size, number of axles, max. allowed total weight, etc. This constitutes information that so far has been lacking for real-world transport in Norway. Insights can therefore be used by firms that want to quantify their environmental footprint but buy transport services from other firms, and therefore lack information on actual fuel consumption. Towards the project?s end, Scania opened up the fleet management system?s API for dynamic vehicle weight, with data on loads on front and back axle on both vehicle and trailer, dependent on the vehicle owner?s subscription. We analyzed these data and found many sources of uncertainty. For example, there are many more positive than negative load changes, meaning that the vehicle?s weight increases over time instead of indicating also unloading. One of LIMCO?s sub-objectives was to investigate whether this type of BigData from truck transport can be used as basis for statistics generation for Statistics Norway. We conclude that GPS-data and dynamic weight data still yield too many challenges, e.g. by requiring many assumptions for converting data to trips and stops, but also because of variations in data frequency over extended periods of time. This has been the case for almost all vehicles from two brands, in different periods each. We considered other potential data sources and conclude that there lies a large potential in electronic freight documents. These cover all requirements for Statistics Norway?s survey of trucks, except distance driven. The latter can be calculated as long as origin and destination locations for a trip are known. We have therefore recommended to enable this type of reporting to the survey of trucks, for transport firms employing electronic freight documents.

Prosjektet har bidratt til å øke bevisstheten og forbedre utnyttelsen av kjøretøydata for frakt- og logistikkanalyse og planlegging i privat sektor. Transportkostnadsmodeller utviklet i prosjektet gir mer transparente transportavtaler og bedre grunnlag for forbedringsarbeid i bedriftene. Datainnsamlingen er blant annet implementert som bedre empiri om kjøretøyenes faktiske bruksmønster og drivstofforbruk. Dette gir bedre beslutningsverktøy for samferdselsinvesteringer. Det er i prosjektet vurdert alternative former for elektronisk rapportering til SSB som vil redusere oppgavebyrden for virksomhetene, samtidig som datakvaliteten forbedres på sikt. Det er utført to masteroppgaver og åtte bacheloroppgaver i logistikkfordypning ved Handelshøyskolen BI. Studenter har kunnet fordype seg i reelle problemstillinger, analysert virkelige data og samarbeidet med bedriftene. Resultater fra prosjektet er tatt inn i undervisningsopplegget på både bachelor og masternivå.

Developments in driving digitalization, logistics software systems, and enterprise resource planning (ERP) platforms have lifted the amount of data on transport and logistics to a new level. Despite these increases in the amount of generated data, a large untapped potential remains with regards to the utilization of these data for transport planning and optimisation. These data sources are normally kept in-house within companies, and are therefore not publicly available for research purposes. For this project proposal, we have established a close cooperation between various transport and logistics firms to ensure data entry and both scientific and commercial relevance of the project. By analysing data from multiple sensors installed in trucks, combined with data from logistics software- and ERP systems, LIMCO will generate new knowledge that will contribute to smarter logistics management and transport planning, more sustainable business models for leading Norwegian transport and logistics companies, and increased knowledge on how to utilize new types of data in an efficient manner. The project's risk related to getting access to data is considered manageable, as we have already discussed data acquisition with our industrial partners. Furthermore, the project will protect the anonymity of firms, individuals and stakeholder groups. Any issues relating to privacy will also be thoroughly discussed. The project output will intended to be included in the business models of the industrial partners and in the Norwegian transport authorities' guidelines for public planning relating to logistics and freight transport. Furthermore, LIMCO will also investigate the opportunities for electronic reporting of vehicle data to SSB, utilizing information from the first time data is registrated, which will reduce workload related to reporting, bureaucracy, increased data capture, quality and actuality of freight transport statistics.