Neural network controller with on-line inventory feedback data in RFID-enabled supply chain

Hong, Seong Rok; Kim, Shin Tae; Kim, Chang Ouk

doi:10.1080/00207540903564967

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…Hong et al developed an online neural network controller that optimized a three-stage supply chain. With the inventory data feedback from an RFID system, the neural network controller minimized the total cost of the supply chain rapidly while satisfying a target order fulfillment ratio [6]. Some of these studies further proved that the neural network technique exceeded the traditional statistical technique in forecasting inventory level [7].…”

Section: Literature Reviewmentioning

confidence: 99%

An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013

Discrete Dynamics in Nature and Society

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Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013

Discrete Dynamics in Nature and Society

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“…Traceability technology also provides accurate pointof-sale data that can subsequently provide an effective avenue for the reconciliation of inventory records with actual inventories within the firm (Bottani, Montanari, and Volpi 2010;Hong, Kim, and Kim 2010;Fatorachian and Kazemi 2021). This level of traceability enhances reliability and security by facilitating the swift transfer of inventory information across the SC to decrease inventory errors and eventually mitigate the bullwhip effect in the SC (Bottani, Montanari, and Volpi 2010;Cui et al 2017;Pournader et al 2020).…”

Section: Impact On Firm and Sc Performancementioning

confidence: 99%

Supply chain traceability: a review of the benefits and its relationship with supply chain resilience

Razak

Hendry

Stevenson

2021

Production Planning & Control

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There has been considerable recent growth in supply chain (SC) traceability research due to increased Industry 4.0 solutions and the potential of traceability systems to enable SCs to bounce back from a crisis, thereby having a long-term impact on firm/SC performance. However, to date, the relationship between SC traceability and SC Resilience (SCRes) has not been fully explored. Using a systematic literature review, this paper first provides a comprehensive state-of-the-art understanding of traceability to enable an appreciation of the inherent benefits of its implementation and its role in the improvement of SCRes. Building on this understanding, a conceptual framework is developed showing that there is a direct relationship between traceability benefits, such as improved risk awareness, and SCRes. The framework also demonstrates indirect relationships between these benefits and four enablers of SCRes: flexibility, velocity, visibility and collaboration. Finally, a future research agenda is proposed, including further development of this conceptual framework.

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“…Coleman (2010) discusses the role of RFID and describes a set of guiding principles designed to help supply chain practitioners define inventory and ordering policies collaboratively; an automotive case study illustrates the approach, which requires supplying and buying firms to model their supply chains together. Hong et al (2010) propose a neural network‐based controller architecture coupled with data‐mining techniques to handle uncertainties for on‐line control of a supply chain that utilizes item‐level RFID‐tagged inventory.…”

Section: Literature Reviewmentioning

confidence: 99%

RFID‐based baggage‐handling system design

Saygin

Natarajan

2010

Sensor Review

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PurposeThe purpose of this paper is to investigate the impact of radio frequency identification (RFID) deployment at an airport baggage‐handling system (BHS).Design/methodology/approachThe impact of number of RFID readers at different power levels with varying conveyor (i.e. baggage‐handling conveyors) speeds on timely delivery of baggage is studied via simulation. The layout of the BHS at the Hong Kong International Airport and data pertinent to its RFID deployment in 2005 are used to build the simulation model. The RFID read logic is based on the equations defined as a function of the number of tags and the time the tags spend in the interrogation zone for each reader in order to capture possible read‐rate issues realistically.FindingsThe identification capability of the BHS studied in this paper is a result of its combined ability to identify tags via RFID technology on straight and circulating conveyors, as well as at the manual recovery station for unidentified bags on circulating conveyors. Overall, timely delivery of bags to gates, as a performance metric, increases as the identification capability is improved. The controllable factors that affect the identification capability are the conveyor speed, which determines the time a tag stays in the interrogation zone; the reader antenna power level, which determines the size of the interrogation zone; and the number of reader antennas in the system that increases the likelihood of not missing tags. This paper shows that “the higher the number of reader antennas and the higher the power level on them, the better” approach is not correct.Originality/valueUnlike typical simulation studies related to RFID deployment where read‐rate issues are considered to be non‐existent, this paper captures read rate in a realistic manner in the simulation model by incorporating the effect of number of RFID tags in the interrogation zone and time that RFID tags spend in the interrogation zone due to baggage conveyor speed. Such a simulation approach can be used as a system design tool in order to investigate the impact of RFID‐specific parameters on system‐level performance.

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Neural network controller with on-line inventory feedback data in RFID-enabled supply chain

Cited by 16 publications

References 20 publications

An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

Supply chain traceability: a review of the benefits and its relationship with supply chain resilience

RFID‐based baggage‐handling system design

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