• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.2
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• pp.1006-1015
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• 2015

This study proposed a model to examine the interorganizational relationship factors on the technology diffusion in supply chain networks whereby a firm's adoption decision is influenced by information from physical and social proximity with others as well as its own attributes. To test several hypotheses developed in this context, this paper analyzed the data set of US consumer packaged goods companies adopting an inventory tracking technology in a retail supply chain and found that a potential adopter's decision is largely influenced by the social proximity with prior adopters in a network over time, while a firm's likelihood of adoption at the initial period is mainly determined by its own attributes.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.439-448
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• 2001

Data Alignment is achieved when all trading partners information systems are maintained automatically synchronising with the suppliers information systems on a continuing basis. Electronic catalogues facilitate the ongoing synchronisation of data between trading partners and large retailers in United States and Canada use electronic catalog in order to get rid of non-value added paperwork and manual reconciliation. Data Alignment will dramatically improve the effectiveness of E-Commerce and Supply Chain initiatives including electronic Marketplaces, Collaborative Planning and Forecasting and continuous replenishment processes.

• Journal of Distribution Science
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• v.13 no.11
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• pp.5-13
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• 2015

Purpose - The purpose of this study is to understand how retailers' green practices influence customer environmental values and their organic food purchasing intention. Research design, data, and methodology - Data were collected from randomly intercepting retail shoppers (n=719) departing from 33 retail stores selling organic food products located in Florida, USA. U.S. Data were analyzed using descriptive statistics, CFA and Hierarchical regression analyses. Results - Results documented that customer environmental values (social-altruistic values and biospheric values) were determinants of organic food purchasing intention. Retailers' green practices representing'green self-governance'were found to significantly enhance the effects of customer environmental values upon organic food purchasing intention. Conclusions - This study successfully demonstrated that customers'willingness to purchase eco-friendly products can be greatly increased when having a positive perceptions toward retailers'green practices such as environmental friendly waste management, environmental improvement of packaging, taking back packaging and recovery of the company's end-of-life products.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.26 no.1
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• pp.11-17
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• 2020

This study conducted a case study and a questionnaire analysis in parallel. In the case study, a study was conducted on domestic manufacturer A by analyzing pallet loading efficiency of RRP(Retail Ready Packaging) products and pallet loading efficiency of MWC(Membership Wholesale Club) delivered products. As a result of the pallet loading efficiency simulation of 50 RRP products of Manufacturer A, it was 80.0% based on the T-11 type pallet and 84.3% based on the T-12 type pallet. It was found It refers that the route of producing the product from the manufacturer A and delivering it to the MWC A in the form of RRP resulted in the decrease of the pallet loading efficiency through the change of the loading pattern and the adjustment of the number of loads. As a result of analyzing the questionnaire about whether the overall efficiency of the supply chain will be improved if the operation of the packaging system considering the SCM(Supply Chain Management) aspect is χ² = 178.500, there was a statistically significant difference at the significance level of 0.000. Manufacturers and logistics companies answered "yes" the most, but distributors answered "is average" the most, confirming that the packaging can be constructed with the highest operational efficiency. Therefore, as a result of confirming the impact of packaging design considering the SCM aspect on logistics efficiency, it indicates the importance of closer collaboration between manufacturers and distributors.

• Journal of Distribution Science
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• v.20 no.5
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• pp.105-117
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• 2022

Purpose: This research investigates ready-to-drink (RTD) coffee. Although the RTD coffee market is growing competitively, few studies have examined behavioral re-intention or repurchase intention in the context of this industry. Therefore, the objective of this study was to explore factors affecting the behavioral re-intention to purchase RTD coffee. Research design, data and methodology: Using the theory of planned behavior (TPB) as the underpinning theoretical framework, this study hypothesized that behavioral re-intention to purchase RTD coffee is influenced by the variables of the TPB and additional variables. A mixed-method research design was applied, starting with qualitative in-depth interviews and followed by a quantitative method. Data were collected using an online survey of coffee lovers. Multiple linear regression (MLR) was used to assess the hypothesized relationships in the proposed conceptual framework. Results: The results reveal that content sensory attribute beliefs are the strongest positive predictor of behavioral re-intention in Thailand, followed by perceived utilitarian value. In contrast, price signaling was negatively related to behavioral re-intention. Conclusions: The findings can help food and beverage companies to develop new coffee product lines to gain more market share, create integrated marketing communications to build brand awareness, and manage distribution channels and the supply chain.

• IE interfaces
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• v.25 no.2
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• pp.241-254
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• 2012

This paper studies the relationship between SCM(Supply Chain Management) metrics and corporate value (Tobin's Q) for manufacturing and wholesale/retail firms in Korea. Specifically, the multiple regression analysis is used to investigate the relationships 1) between inventory level, inventory turns, and days of inventory and Tobin's Q and 2) between cash-to-cash (C2C) cycle time including its components such as days of inventory, days sales outstanding, and days payable outstanding and Tobin's Q. The results indicate that there exist statistically significant negative relationships between inventory levels and days of inventory (DOI) and Tobin's Q. Also, we found that there exist commonly known negative correlations between days of raw materials inventory and days of work in process (WIP) inventory and Tobin's Q. For the C2C cycle time, we found that there -exists a statistically significant negative relationship between the C2C cycle time and Tobin's Q. Also, we found that there exist commonly known correlations between the two components of C2C cycle time and Tobin's Q such as the negative for DOI and days sales outstanding. This study clearly shows the negative relationship in general between inventory levels and corporate value and between C2C time and corporate value, and this kind of result has not been found by previous studies in Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.463-466
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• 2016

Sharing of product and process information with partners is a basic activity and key requirement which ensures success of distribution. ePedigree that encapsulates all of the event data from manufacturer to retail shop provides a flexible mechanism of storing and sharing traceable information to the partners of supply chain and credibility of shared information through digital signature based on Public Key Infrastructure (PKI). To generate the signature that can be authenticated through PKI, the partners of supply chain should pay for PKI certificates from Certificate Authority (CA). In case of agrifood cooperatives which consist of petty merchants or farmers, it is hard to pay for the PKI certificate for all members and is a big obstacle for the ePedigree to be applied to the supply chain. This paper proposes a private certification method of ePedigree for cooperatives. Cooperatives can apply the ePedigree using the proposed method to all the members at small cost and the proposed method can assure the credibility of information at the same level of the previous ePedigree.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.19 no.1
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• pp.7-10
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• 2013

A packaging system integrated in primary and secondary packages to deliver consumers fresh peach in the produce supply chain was designed and its effectiveness on quality preservation was tested. The master packaging system was designed to contain 6 individual polypropylene film (PP, $30{\mu}m$ thickness) packages of 300 g peach fruit inside $35{\mu}m$ thick low density polyethylene (LDPE) bag located in a corrugated paperboard box. As a variable to attain the desired package atmosphere around the fruit during cold storage and subsequent retail display at higher temperature, different numbers (1, 3 and 7) of microperforations in $59{\mu}m$ diameter were tested on the individual PP packages. As control treatment, six fruits were placed without wrapping in a corrugated paperboard box. During the storage at $5^{\circ}C$, the control and individual packages were periodically separated from the box or master package, moved to the simulated retail shelf conditions of $20^{\circ}C$ and then stored for 3 more days with package atmosphere and fruit quality being measured. The package with 7 microperforations was the best in the ability to attain beneficial MA of 6~10% $O_2$ and 11~19% $CO_2$ around the fruit during the chilled storage at $5^{\circ}C$ and simulated retail display at $20^{\circ}C$. Packages with smaller number of microperforations resulted in anaerobic atmosphere at the low temperature storage and/or the subsequent high temperature display. Compared to control, all the treatments with master packaging system gave better retention of fruit firmness with significantly less weight loss.