A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment

Lam, H. Y.; Choy, King Lun Tommy; Ho, G.T.S.; Cheng, Stephen W.Y.; Lee, C. K. M.

doi:10.1016/j.ijpe.2015.01.005

Cited by 54 publications

(31 citation statements)

References 39 publications

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“…Given the irregular order patterns, the limited time for processing orders and the short-term delivery schedules (same or next day) offered by e-commerce retailers now expected by customers, logistics providers must be extremely efficient in handling these orders and in managing the entire process of fulfillment [57]. During the formulation of a logistics strategy, ML systems can interactively monitor and optimize all logistics processes and even product characteristics (size, shape and weight) and order demand factors [58]. Here, ML calculates the likelihood that the trucks registered in the system will be on a certain route, their free capacity and the tour cost.…”

Section: Handing Out Goodsmentioning

confidence: 99%

A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing

Weber

Schütte

2019

BDCC

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Information technologies in general and artifical intelligence (AI) in particular try to shift operational task away from a human actor. Machine learning (ML) is a discipline within AI that deals with learning improvement based on data. Subsequently, retailing and wholesaling, which are known for their high proportion of human work and at the same time low profit margins, can be regarded as a natural fit for the application of AI and ML tools. This article examines the current prevalence of the use of machine learning in the industry. The paper uses two disparate approaches to identify the scientific and practical state-of-the-art within the domain: a literature review on the major scientific databases and an empirical study of the 10 largest international retail companies and their adoption of ML technologies in the domain are combined with each other. This text does not present a prototype using machine learning techniques. Instead of a consideration and comparison of the particular algorythms and approaches, the underling problems and operational tasks that are elementary for the specific domain are identified. Based on a comprehensive literature review the main problem types that ML can serve, and the associated ML techniques, are evaluated. An empirical study of the 10 largest retail companies and their ML adoption shows that the practical market adoption is highly variable. The pioneers have extensively integrated applications into everyday business, while others only show a small set of early prototypes. However, some others show neither active use nor efforts to apply such a technology. Following this, a structured approach is taken to analyze the value-adding core processes of retail companies. The current scientific and practical application scenarios and possibilities are illustrated in detail. In summary, there are numerous possible applications in all areas. In particular, in areas where future forecasts and predictions are needed (like marketing or replenishment), the use of ML today is both scientifically and practically highly developed.

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Section: Handing Out Goodsmentioning

confidence: 99%

A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing

Weber

Schütte

2019

BDCC

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“…Risk management in the supply chain refers to the concept of Supply Chain Risk Management (SCRM), which would be beneficial to the parties involved in terms of cost reduction and increase in profitability [10]. Management of risks is becoming the focal concern of the firms to survive in a competitive business environment.…”

Section: Supply Chain Risk Management (Scrm)mentioning

confidence: 99%

Ontology-based Operational Risk Identification in 3PL

Gómez¹,

Manotas-Duque²,

Garcia³

2018

RCS

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Supply chain risk management is an important activity in current supply chain management, and operational risk is one of the most important risks in supply chains. The risk identification process is one of the most important activities in supply chain risk management system. The participation of Third Party Logistics providers (3PL) in supply chains has been increasing, and it is important to consider how their presence affects risk management. On the other hand, ontologies define a common vocabulary to share information in a domain. Considering all these aspects, we propose an ontology approach to operational risk identification in supply chain that involves third party logistics providers. The ontology-based approach is oriented to improve communications about risks through the whole supply chain, achieving better results in risks management activities. The approach is validated in ground transportation activities.

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“…In the field of supply chains and logistics, CBR has been applied to transform warehouse operators' knowledge and experiences into cases so as to retain them in an organization. Choy et al [16] integrated CBR for supplier relationship management; Chow et al [17] and Lam et al [18] used CBR for warehouse resources management and risk mitigation respectively. As e-commerce and O2O logistics environment is so dynamic and complex that requires logistics practitioners to acquire years of experiences for efficient logistics and warehousing operations, CBR is viewed to be an essential tool to manage useful knowledge in a structured way for e-order fulfillment.…”

Section: Case-based Reasoning Techniquementioning

confidence: 99%

A Knowledge-based Decision Support Framework for Wave Put-away Operations of E-commerce and O2O Shipments

Leung¹,

Choy²,

Tam³

et al. 2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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Abstract-Warehouse put-away and storage operation is one of the typical internal operations in a warehouse or a distribution center, alongside receiving, picking, packing and shipping. Under today's e-commerce business environment, logistics practitioners are required to efficiently handle e-commerce shipments in warehouses. However, owing to the fundamental differences in the order nature and handling requirements between traditional orders and e-commerce orders, logistics service providers are struggled to handle e-commerce shipments competently. In this paper, a knowledge-based wave put-away decision support system is proposed, which incorporates cloud database for realtime data update and retrieval, and case-based reasoning technique for generating put-away solutions for e-commerce orders based on historical put-away knowledge. The proposed system is validated through a pilot study in a case company. The efficiency of order put-away operation is enhanced by grouping fragmented e-commerce orders at the inbound for performing put-away operations in a wave pattern, which enables logistics practitioners to yield cost-saving and operating efficiency in ecommerce order handling.

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A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment

Cited by 54 publications

References 39 publications

A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing

A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing

Ontology-based Operational Risk Identification in 3PL

A Knowledge-based Decision Support Framework for Wave Put-away Operations of E-commerce and O2O Shipments

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