Smart and sustainable urban logistic applications aided by intelligent techniques

[EN] CO2-free urban logistics is one of the 10 objectives to reach by 2030 as part of transport policy. What technologies can help to accomplish it? In this paper, we discuss the very complex situation that today¿s big and modern cities are facing with a tremendous environment of many urban logistics companies running in the same city. In the majority of cases, there is less or none coordination among them worsening traffic congestions. We believe that intelligent techniques are one of the key approaches that can aid to support smart and sustainable urban logistic applications. There are large open problems in the field of cooperative urban logistics that can greatly improve with the help of artificial intelligence. Some solutions are cited in this paper, but the overall conclusion is that there is still much work to be done.Giret Boggino, AS. (2019). Smart and sustainable urban logistic applications aided by intelligent techniques. Service Oriented Computing and Applications (Online). 13(3):185-186. https://doi.org/10.1007/s11761-019-00271-zS185186133Market reports (2019) Global last mile delivery market size, status and forecast 2019–2025. The Market reports. Report code : 1362721, pp 1–114Xiao Z, Wang JJ, Lenzer J, Sun Y (2017) Understanding the diversity of final delivery solutions for online retailing: a case of Shenzhen, China. In: World conference on transport research—WCTR 2016 Shanghai. Transportation Research Procedia, vol 25, pp 985–998, 2017. 10–15 July 2016Gonzalez-Feliu J, Semet F, Routhier JL (2014) Sustainable urban logistics: concepts, methods and information systems. Springer, BerlinMacharis C, Melo S (2011) City distribution and urban freight transport: multiple perspectives. Edward Elgar Publishing, CheltenhamPagell M, Wu Z (2009) Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. J Supply Chain Manag 45:37–56Morana J, Gonzalez-Feliu J (2015) A sustainable urban logistics dashboard from the perspective of a group of operational managers. Manag Res Rev 38(10):1068–1085Gunasekaran A, Kobu B (2007) Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int J Prod Res 45:2819–2840Griffis SE, Goldsby TJ, Cooper M, Closs DJ (2007) Aligning logistics performance measures to the information needs of the firm. J Bus Logist 48:35–56Alonso-Mora J, Samaranayake S, Wallar A, Frazzoli E, Rus D (2017) On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc Natl Acad Sci 114(3):462–467Gentile G, Noekel K (2016) Modeling public transport passenger flows in the era of intelligent transport systems. Springer, BerlinNeirotti P, De Marco A, Cagliano AC, Mangano G, Scorrano F (2014) Current trends in smart city initiatives: some stylised facts. Cities 38:25–36Chatterjee R (2016) Optimizing last mile delivery using public transport with multiagent based control. Master thesis, pp 1–59Skiver RL, Godfrey M (2017) Crowdserving: a last mile delivery method for brickand—mortar retailers. Glob J Bus Res 11(2):67–77Brüning M, Schönewolf W (2011) Freight transport system for urban shipment and delivery. In: IEEE forum on integrated and sustainable transportation systems, Vienna, pp 136–14

Supporting Data Collection in Complex Scenarios with Dynamic Data Collection Processes

Nowadays, companies have to report a large number of data sets (e.g., sustainability data) regarding their products to different legal authorities. However, in today's complex supply chains products are the outcome of the collaboration of many companies. To gather the needed data sets, companies have to employ cross-organizational and long-running data collection processes that imply great variability. To support such scenarios, we have designed a lightweight, automated approach for contextual process configuration. That approach can capture the contextual properties of the respective situations and, based on them, automatically configure a process instance accordingly, even without human involvement. Finally, we implemented our approach and started an industrial evaluation

Upstream Supply Chain Visibility and Complexity Effect on Focal Company’s Sustainable Performance: Indian Manufacturers’ Perspective

Understanding supply chain sustainability performance is increasingly important for supply chain researchers and managers. Literature has considered supply chain sustainability and the antecedents of performance from a triple bottom line (economic, social, and environmental) perspective. However, the role of supply chain visibility and product complexity contingency in achieving sustainable supply chain performance has not been explored in depth. To address this gap, this study utilizes a contingent resource-based view theory perspective to understand the role of product complexity in shaping the relationship between upstream supply chain visibility (resources and capabilities) and the social, environmental, and economic performance dimensions. We develop and test a theoretical model using survey data gathered from 312 Indian manufacturing organizations. Our findings indicate that supply chain visibility (SCV) has significant influence on social and environmental performance under the moderation effect of product complexity. Hence, the study makes significant contribution to the extant literature by examining the impact of SCV under moderating effect of product complexity on social performance and environmental performance