A decision support system for improved resource planning and truck routing at logistic nodes

Hill, Alessandro; Böse, Jürgen W.

doi:10.1007/s10799-016-0267-3

Cited by 21 publications

(11 citation statements)

References 7 publications

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“…Regarding the geographical location of authors' affiliations, 35 per cent were in the USA, 18 per cent in the UK and 13 per cent in China. It should be highlighted though that, when we explored references cited in the studies retrieved, we found a large body of literature that studied the use of AI on specific supply chain processes or activities, such as transportation, predictive maintenance and demand forecasting (Lee et al, 2011;Bogataj et al, 2017;Cozar et al, 2017;Hill and Bose, 2017;Klumpp, 2017;Yang et al, 2017). Nevertheless, only a handful of these papers refer to AI from a supply chain perspective.…”

Section: Artificial Intelligence and Scmmentioning

confidence: 99%

The self-thinking supply chain

Calatayud

Mangan

Christopher

2019

SCM

166

150

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Purpose An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive capabilities, bringing significant efficiency gains in an increasingly complex and uncertain environment. This paper aims to both bridge the gap between the practitioner and academic literature on these topics and contribute to both practice and theory by seeking to understand how such developments will help to address key supply chain challenges and opportunities. Design/methodology/approach A multi-disciplinary, systematic literature review was conducted on relevant concepts and capabilities. A total of 126 articles were reviewed covering the time period 1950-2018. Findings The results show that both IoT and AI are the technologies most frequently associated with the anticipated autonomous and predictive capabilities of future supply chains. In addition, the review highlights a lacuna in how such technologies and capabilities help address key supply chain challenges and opportunities. A new supply chain model is, thus, proposed, one with autonomous and predictive capabilities: the self-thinking supply chain. Originality/value It is our hope that this novel concept, presented here for the first time in the academic literature, will help both practitioners to craft appropriate future-proofed supply chain strategies and provide the research community with a model (built upon multidisciplinary insights) for elucidating the application of new digital technologies in the supply chain of the future. The self-thinking supply chain has the potential in particular to help address some of today’s key supply chain challenges and opportunities.

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Section: Artificial Intelligence and Scmmentioning

confidence: 99%

The self-thinking supply chain

Calatayud

Mangan

Christopher

2019

SCM

166

150

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“…Fourth, the field of transport organization focuses on optimizing routes or logistical processes. One example is a decision support system developed by Hill and B€ ose (2017), which provides forecasted truck arrival rates to logistics nodes (e.g. warehouses, hubs or ports) and predicts truck gate waiting times at these nodes for the truck companies based on historical data.…”

Section: Ai Applications In Transport Logistics: Research Gap and Pos...mentioning

confidence: 99%

Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics

Loske

Klumpp

2021

IJLM

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PurposeTechnological advances regarding artificial intelligence (AI) are affecting the transport sector. Although fully autonomous delivery, or self-driving trucks, are not operating currently, various AI applications have become fixed components of cargo vehicles. Since many research approaches primarily concentrate on the technical aspects of assistance systems (ASs), the economic question of how to improve efficiency is seldom addressed. Therefore, the purpose of this paper is to apply an efficiency analysis to measure the performance of truck drivers supplying retail stores.Design/methodology/approachFor this comparative study, 90 professional truck drivers in three groups are compared with (1) trucks without AS, (2) trucks with AS that cannot be turned off and (3) trucks with AS that can be turned off. First, we build a model investigating the impact of performance expectation, effort expectation, social influence and facilitating conditions on the behavioural intention to use AS. Second, we explore the impact of truck drivers' behavioural intention on actual technology use, misuse and disuse; operationalize these constructs; and merge them with our behavioural constructs to create one econometric model.FindingsThe human–AI system was found to be the most efficient. Additionally, behavioural intention to use ASs did not lead to actual usage in the AI-alone observation group, but did in the human–AI group. Several in-depth analyses showed that the AI-alone group used AS at a higher level than the human–AI group, but manipulations through, for example, kickdowns or manual break operations led to conscious overriding of the cruise control system and, consequently, to higher diesel consumption, higher variable costs and lower efficiency of transport logistical operations.Research limitations/implicationsEfficiency analysis with data envelopment analysis is, by design, limited by the applied input and output factors.Originality/valueThis study represents one of the first quantitative efficiency analyses of the impact of digitalization on transport performance (i.e. truck driver efficiency). Furthermore, we build an econometric model combining behavioural aspects with actual technology usage in a real application scenario.

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“…Lack of transparency and predictability of the traffic situation (especially for trucks) (Zhao and Goodchild 2010;Hill and Böse 2017):…”

Section: Information Issues In Port-related Truckingmentioning

confidence: 99%

Toward implementing a fully automated truck guidance system at a seaport: identifying the roles, costs and benefits of logistics stakeholders

et al. 2019

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The seaport-hinterland access infrastructures become more saturated leading to extra unpredictable costs for logistics operators. To keep their services competitive, infrastructure managers (such as port authorities) seek new approaches in managing and delivering information to their users. Starting from existing technological advancements and exploring the declared opportunities enabled by (near) future ones, a range of solutions (e.g. real-time information platforms, dynamic information boards or digital on-request notification solutions) can be pursued. This research investigates how an advanced truck guidance system (TGS) leverages on technology and leads to more efficient resource (infrastructure, equipment and labour) usage. The current paper merges two perspectives. The first conducts interviews with representatives of different logistics stakeholders to highlight their data needs. The second perspective is given by a review of information communication technology (ICT) innovative trends. These two perspectives provide an up-to-date overview of both needs (demand) and opportunities (supply) that challenge logistics stakeholders. This study proposes a TGS architecture that merges the two perspectives and identifies a stepwise approach to implement it. Therefore, the roles, costs and benefits brought to the logistics stakeholders are highlighted for each step. The key findings of this study show that road transport operators would benefit from operational savings and increased performance, while the other supply chain stakeholders can benefit from setting up a more reliable planning, better managing their infrastructure and developing added-value services for clients. To enable these benefits, the port administration could take the facilitator role and cover the TGS's development costs.

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A decision support system for improved resource planning and truck routing at logistic nodes

Cited by 21 publications

References 7 publications

The self-thinking supply chain

The self-thinking supply chain

Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics

Toward implementing a fully automated truck guidance system at a seaport: identifying the roles, costs and benefits of logistics stakeholders

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