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Deep learning and statistical models for forecasting transportation demand : a case study of multiple distribution centers

Mamede, Fábio Polola; Silva, Roberto Fray da; De … - In: Logistics 7 (2023) 4, pp. 1-19

Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It … statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated … transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study …

Persistent link: https://www.econbiz.de/10014454624

Effective demand forecasting in health supply chains : emerging trend, enablers, and blockers

Subramanian, Lakshmy - In: Logistics 5 (2021) 1/12, pp. 1-21

of scarce resources and loss of lives. A chain is only as strong as its weakest link, and demand forecasting is one of … impede the accurate forecasting of demand, and without the ability to forecast demand with certainty, the stakeholders cannot … of risks, leading to the inability to match demand and supply. For this purpose, a systematic literature review was …

Persistent link: https://www.econbiz.de/10012488577

The integration of extended supply chain with sales and operation planning : a conceptual framework

Rokonuzzaman, Md. - In: Logistics 2 (2018) 2, pp. 1-21

This research is an effort to present the emergence of ways to enable marketers to attain high sensitivity and visibility in the supply chain network. It also aims to facilitate better multi-criteria decisions throughout the extended supply chain. It is a qualitative study considering 31...

Persistent link: https://www.econbiz.de/10012122765

Exploring the potentials of automation in logistics and supply chain management : paving the way for autonomous supply chains

Nitsche, Benjamin - In: Logistics 5 (2021) 3, pp. 1-9

The world of logistics is changing and entering a new era. The advance of digitalization and technologization enables new business models, increased process efficiencies, novel planning approaches, and much more but, on the downside, there is also the risk of being lost in the maelstrom of...

Persistent link: https://www.econbiz.de/10012618271

Scope of using autonomous trucks and lorries for parcel deliveries in urban settings

Kassai, Evelyne Tina; Azmat, Muhammad; Kummer, Sebastian - In: Logistics 4 (2020) 3/17, pp. 1-24

Courier, express, and parcel (CEP) services represent one of the most challenging and dynamic sectors of the logistics industry. Companies of this sector must solve several challenges to keep up with the rapid changes in the market. In this context, the introduction of autonomous delivery using...

Persistent link: https://www.econbiz.de/10012264903

A model for demand planning in supply chains with congestion effects

Venkatadri, Uday; Wang, Shentao; Srinivasan, Ashok - In: Logistics 5 (2021) 1/3, pp. 1-24

This paper is concerned with demand planning for internal supply chains consisting of workstations, production … subsequently plan to fulfill demand. We first formulate a linear aggregate planning model for demand management that incorporates … the model in demand planning and capacity planning to help a supplier firmly respond to requests for quotations. We extend …

Persistent link: https://www.econbiz.de/10012426156

Logistics sector turnover : forecasting for Turkey, EU27 and EA19 under effects of COVID-19

Balkan, Dursun; Akyuz, Goknur Arzu - In: Logistics 7 (2023) 2, pp. 1-22

Background: The logistics sector is the backbone of today's global trade, and is vital for the continuity of goods and services. The sector is gaining increased importance as logistics operate under the extreme conditions the world is passing through (COVID-19, earthquakes, wars). Methods: A...

Persistent link: https://www.econbiz.de/10014370475

Travel time prediction in a multimodal freight transport relation using machine learning algorithms

Servos, Nikolaos; Liu, Xiaodi; Teucke, Michael; … - In: Logistics 4 (2020) 1, pp. 1-22

Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine...

Persistent link: https://www.econbiz.de/10012262182

Textual data science for logistics and supply chain management

Treiblmaier, Horst; Mair, Patrick - In: Logistics 5 (2021) 3, pp. 1-15

Background: Researchers in logistics and supply chain management apply a multitude of methods. So far, however, the potential of textual data science has not been fully exploited to automatically analyze large chunks of textual data and to extract relevant insights. Methods: In this paper, we...

Persistent link: https://www.econbiz.de/10012618298

A predictive maintenance system for reverse supply chain operations

Gayialis, Sotiris P.; Kechagias, Evripidis P.; … - In: Logistics 6 (2022) 1, pp. 1-14

Background: Reverse supply chains of machinery and equipment face significant challenges, and overcoming them is critical for effective customer service and sustainable operation. Maintenance and repair services, strongly associated with the reverse movement of equipment, are among the most...

Persistent link: https://www.econbiz.de/10012818081

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