Wyniki wyszukiwania - BazTech

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Methodology of an interpretive structural map construction for social commerce success

Pijo Hazel, Aguilar Aethyl, Catarongan Mitzie Ann, Orioque Janet, Atibing Nadine May, Wenceslao Charldy, Evangelista Samantha Shane, Aro Joerabell Lourdes, Maturan Fatima, Yamagishi Kafferine, Ocampo Lanndon

Engineering Management in Production and Services

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2023

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Vol. 15, no. 3

101--114

EN

The factors influencing consumer purchase decisions in electronic commerce platforms and the interrelationships of each element are prevalent in the domain literature. However, a comprehensive analysis of the complex interrelationships among the success factors remains unexplored, especially in a social commerce context. To address the gap, this work evaluates the relationship structure and determines the critical factors using interpretive structural modelling (ISM). On the other hand, the Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) is introduced to analyse the interaction of the factors and recognise the most relevant among them. In demonstrating the ISM-MICMAC analysis, this work performed a case study evaluating 13 factors of social commerce success for food products derived from a previous study. The findings of this work suggest that timeliness, data privacy policy, and Internet connectivity drive most other factors. Thus, focusing the resources on augmenting these factors consequently improves other factors. These findings suggest that sellers must streamline their overall service chain to maintain timeliness in their transactions, safeguard consumers’ data privacy, and uphold consumer communication efficiency to maximise Internet connectivity. These insights provide useful information to help decision-makers in the food industry allocate resources and encourage more consumers for social commerce. Several managerial insights were discussed.

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Heuristic comparative assessment of non-conventional warehouse designs

Esmero Antonio T., Branzuela Queenie Rose S., Paypa Jessilyn T., Rojo Sharmaine Myka S., Sacay Jr. Eduardo S., Selerio Jr. Egberto F., Ocampo Lanndon

Engineering Management in Production and Services

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2021

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Vol. 13, no. 1

89--103

EN

In the unit-load warehouse (UW) design, the aisle design problem dealing with storage space layout is the first among the three main problems. Several conventional and non-conventional designs have been proposed in the literature. In general, the assessment of UW designs is commonly carried out using analytical approaches. However, such an approach may be inadequate due to assumptions or approximations, making results unrealistic. Aiming to bridge this gap, this research develops an assessment framework that employs the FlexSim software for simulating the conventional, Flying-V and Fishbone designs based on a real case from a Philippine manufacturing company. Using a computer simulation, this research investigates factors not yet tractable with present analytical methods. The factors employed for the comparative assessment are “picking run-time”, “travel distance”, and “capacity”. The results suggest that the Fishbone design provides the most advantage compared to the Flying-V and other conventional designs. With the proposed Fishbone design, the company is expected to save, on average, 52.39% of picking run-time, 32.25% travel distance, and increase storage capacity by 7.5%. The research findings are compared to previous studies based on analytical approaches.

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Warehouse location selection with TOPSIS group decision-making under different expert priority allocations

Ocampo Lanndon, Genimelo Gianne Jean, Lariosa Jerome, Guinitaran Raul, Borromeo Philip John, Aparente Maria Elena, Capin Teresita, Bongo Miriam

Engineering Management in Production and Services

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2020

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Vol. 12, no. 4

22--39

EN

Warehouses are crucial infrastructures in supply chains. As a strategic task that would potentially impact various long-term agenda, warehouse location selection becomes an important decision-making process. Due to quantitative and qualitative multiple criteria in selecting alternative warehouse locations, the task becomes a multiple criteria decision-making problem. Current literature offers several approaches to addressing the domain problem. However, the number of factors or criteria considered in the previous works is limited and does not reflect real-life decision-making. In addition, such a problem requires a group decision, with decision-makers having different motivations and value systems. Analysing the varying importance of experts comprising the group would provide insights into how these variations influence the final decision regarding the location. Thus, in this work, we adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to address a warehouse location decision problem under a significant number of decision criteria in a group decision-making environment. To elucidate the proposed approach, a case study in a product distribution firm was carried out. Findings show that decision-makers in this industry emphasise criteria that maintain the distribution networks more efficiently at minimum cost. Results also reveal that varying priorities of the decision-makers have little impact on the group decision, which implies that their degree of knowledge and expertise is comparable to a certain extent. With the efficiency and tractability of the required computations, the TOPSIS method, as demonstrated in this work, provides a useful, practical tool for decision-makers with limited technical computational expertise in addressing the warehouse location problem.

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Defining stages of the Industry 4.0 adoption via indicator sets

Himang Celbert, Ocampo Lanndon, Obiso Jun-Jun, Bongo Miriam, Caballes Shirley Ann, Abellana Dharyll Prince, Deocaris Custer, Ancheta jr Rosein

Engineering Management in Production and Services

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2020

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Vol. 12, no. 2

32--55

EN

As Industry 4.0 offers significant productivity improvements, its relevance has grown across various organisations. While it captures the attention of both the industry and the academia, very few efforts have been made to streamline useful indicators across stages of its implementation. Such work facilitates the development of strategies that are appropriate for a specific stage of implementation; therefore, it would be significant to a variety of stakeholders. As a result, this paper aims to establish an indicator system for adopting Industry 4.0 within the context of the three stages of the innovation adoption: (i) pre-adoption, (ii) adoption, and (iii) post-adoption. First, a comprehensive review was performed with a search expanding into the literature on innovation and technology adoption. Second, the resulting indicators were filtered for relevance, redundancy, description, and thorough focus discussions. Finally, they were categorised by their stage of adoption. From 469 innovation adoption indicators found in the literature, this work identified a total of 62 indicators relevant for the Industry 4.0 adoption, in which 11, 14, and 37 of them comprised the three stages, respectively. Case studies from two manufacturing firms in the Philippines were reported to demonstrate the applicability of the proposed indicator system. This work pioneers the establishment of an indicator system for the Industry 4.0 adoption and the classification of such indicators into three stages — pre-adoption, adoption, and post-adoption — which would serve as a framework for decision-makers, practitioners, and stakeholders in planning, strategy development, resource allocation, and performance evaluation of the Industry 4.0 adoption.