Product Customer Research Articles

Proactive Return Prediction in Online Fashion Retail Using Heterogeneous Graph Neural Networks

Online fashion retailers face enormous challenges due to high return rates that significantly affect their operational performance. Proactively predicting returns at the point of order placement allows for preemptive interventions to reduce potentially problematic transactions. We propose an innovative inductive Heterogeneous Graph Neural Network tailored for proactive return prediction within the realm of online fashion retail. Our model intricately encapsulates customer preferences, product attributes, and order characteristics, providing a holistic approach to return prediction. Through evaluation using real-world data sourced from an online fashion retail platform, our methodology demonstrates superior predictive accuracy on the return behavior of repeat customers, compared to conventional machine learning techniques. Furthermore, through ablation analysis, we underscore the importance of simultaneously capturing customer, order, and product characteristics for an effective proactive return prediction model.

Digital Twin-Driven Multi-Factor Production Capacity Prediction for Discrete Manufacturing Workshop

Traditional capacity forecasting algorithms lack effective data interaction, leading to a disconnection between the actual plan and production. This paper discusses the multi-factor model based on a discrete manufacturing workshop and proposes a digital twin-driven discrete manufacturing workshop capacity prediction method. Firstly, this paper gives a system framework for production capacity prediction in discrete manufacturing workshops based on digital twins. Then, a mathematical model is described for discrete manufacturing workshop production capacity under multiple disturbance factors. Furthermore, an innovative production capacity prediction method, using the “digital twin + Long-Short-Term Memory Network (LSTM) algorithm”, is presented. Finally, a discrete manufacturing workshop twin platform is deployed using a commemorative disk custom production line as the prototype platform. The verification shows that the proposed method can achieve a prediction accuracy rate of 91.8% for production line capacity. By integrating the optimization feedback function of the digital twin system into the production process control, this paper enables an accurate perception of the current state and future changes in the production system, effectively evaluating the production capacity and delivery date of discrete manufacturing workshops.

Features level sentiment mining in enterprise systems from informal text corpus using machine learning techniques

ABSTRACT This study explores feature-level sentiment analysis of Hindi blog reviews in enterprise systems, a significant area in the Indian context yet understudied. By applying machine learning techniques like SVM across unigram, bigram, trigram, and n-gram models, and combining Lexicon-based methods with machine learning algorithms, we aim to enhance sentiment classification for better customer relationship management and product development. Contrasting with document-level approaches, our method focusing on bigrams achieves a test accuracy of 75%, offering a scalable model for enterprises to extract detailed customer insights from informal text, thereby aiding informed decision-making in a multicultural environment.

Roadblocks in Integrating Lean Six Sigma and Industry 4.0 in Small and Medium Enterprises

To survive in highly competitive markets, industries focus on enhancing product quality and customer satisfaction. One specific iteration of this approach sees industries striving to optimize processes to improve product quality while generating minimum waste. This has encouraged companies to begin adopting management practices such as lean six sigma (LSS) and kaizen alongside their implementation of digital technologies of automating processing. This study aims to identify and analyze the barriers to integrating LSS practices with Industry 4.0 technologies for small and medium enterprises. Upon shortlisting fifteen barriers from an exhaustive literature review, the Grey-Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to analyze those shortlisted barriers and establish causal relations between barriers and outcomes. The findings identify the lack of available infrastructure, lack of training on LSS, and lack of consultants in the field as the most dominant barriers in the cause group. Furthermore, the influence map of barriers produced demonstrates the relationship between cause-and-effect barriers. This study’s findings will ensure that small and medium enterprises formulate business strategies that mitigate the barriers to integrating LSS with Industry 4.0. This study offers insights into overcoming the identified challenges by proposing strategies to enhance product quality and the accuracy of enterprise decision-making, thereby facilitating a successful integration and fostering sustainable growth in SMEs.

A joint sustainable order-packing vehicle routing optimisation for the cold chain e-fulfilment

AbstractDue to the new normal caused by the pandemic, consumer behaviour has now shifted to online shopping not only for general commodities but also for food and other perishable products. Therefore, e-commerce fulfilment is now integrated with cold chain capabilities to satisfy stringent requirements on time-criticality and product quality, leading to the concept of cold chain e-fulfilment. In the cold chain e-fulfilment process, perishable orders are packed in thermal packaging solutions and delivered to consumers before the quality preservation time window. To secure a sufficient time buffer during last mile delivery, excessive use of thermal packaging materials is applied, which creates an adverse environmental impact on our eco-system. Aligning with low-carbon business practices, this study proposes a novel joint optimization model, namely the Joint Optimization of Sustainable Order Packing and Multi-Temperature Delivery Problem (JOSOPMDP), for order packing and vehicle routing decisions, where the sustainable use of thermal packaging materials is promoted without negatively influencing product quality and customer satisfaction. To evaluate its viability and performance, three sets of computational experiments are subsequently conducted. It is found that the proposed model is feasible to strike a balance between order packing and vehicle routing decisions. Compared with the traditional strategy, the average total cost and satisfaction level are improved by 3.26% and 47.88%, respectively. Consequently, this research fosters sustainable thinking in the cold chain e-fulfilment process, minimizing environmental impact.

A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

Improving aggregate production planning considering maximum inventory area and service level with demand uncertainty: a nearshoring context in Mexican companies

ABSTRACT Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studies in aggregate planning did not consider the possibility of allowing rental of extra warehouse space if needed with a concurrent uncertainty in demand. Therefore, to address this gap in the research, an improved optimization model for multi-product and multi-period aggregate planning with uncertainty in demands given an area of inventory space is proposed. A set of cases based on real industry data were solved and compared to a model where no warehouse space is allowed to be rented. The results are improvements related to production costs and customer demand satisfaction. In addition, the model will help managers of manufacturing companies to determine the minimum inventory space required to effectively meet demand.

Reshaping 3PL Operations: Machine Learning Approaches to Mitigate and Manage Damage Parameters

In the third-party logistics (3PL) environment, it is very important to reduce damage parameters, increase operational efficiency and reduce costs. This study aims to develop strategies for reshaping 3P operations by analyzing the parameters involved in damage control with machine learning. The logistics sector is gradually growing in the world and the potential of the sector is better understood over time. Damage to products in the logistics sector, especially during transportation and storage, not only causes financial losses but also affects customer productivity and operational efficiency. With the use of artificial intelligence techniques, it is possible to determine consumer expectations, predict damage losses, and develop innovative strategies by applying machine learning algorithms. At the same time, options such as driverless vehicles, robots used in storage and shelves, and the easy use of big data within the system, which have emerged with artificial intelligence, minimize errors in the logistics sector. Thanks to the use of artificial intelligence in the logistics sector, businesses are more efficient. This study includes an estimation study in the field of error parameters for the logistics service sector with machine learning methods. In the application, real data of a 3PL company for the last 5 years is used. For the success of 3PL companies, warehousing and undamaged delivery of products are of great importance. The fewer damaged products they send, the more they increase their value. The company examined in the study kept its damage data and wanted it to be analyzed so that it could take precautions accordingly and follow a more profitable path. For this reason, the study focuses on data on errors and damages. This study shows what kind of problems can occur in such a company and how the 3PL company can evaluate the problems to increase customer service quality and cost efficiency.

Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application

ABSTRACT The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Method of improving the production manufacturability of the product design

Due to the customization of production, it becomes especially important to test the design of the product for manufacturability at all stages of its creation. A method is proposed to improve the manufacturability of the product design at the stage of developing the working documentation of the product, based on its evaluation by summing the coefficients of manufacturability. The method consists in constructing a regression series of product design characteristics according to the degree of reduction of their impact on the complexity of the design and establishing the sequence of its improvement. It is recommended to improve manufacturability and reduce the labor intensity of manufacturing a product, to pay attention from the beginning to methods for achieving accuracy during assembly, as well as the use of the maximum number of purchased parts in assemblies; then on the repeatability of the types of connections of parts in the design, reducing the number of types and laboriousness of making connections, and finally on the repeatability of parts in the overall design, the maximum use of borrowed parts. It is shown that the typification of processes and the unification of structures can be effectively dealt with at the level of large associations (holdings, concerns) and especially industries.

Assessment of Design Values in Designing Product Standards: A Design Study on a Sport PPE

Participation in sports requires using Personal Protective Equipment (PPE) to preserve athletes’ well-being. Although equipment may negatively impact performance, governing bodies often impose standards on participants subject to their jurisdiction. Although the vast majority of new products are incremental innovations, radical innovation still attracts design researchers' and professionals' attention. The design of complex systems needs the expertise of various kinds: designers are fundamental in conceiving innovations in response to developing ways of product use and customer needs, despite they must face product standards, which often act against product innovation. Despite product innovations benefiting consumers in many markets, they do not necessarily benefit consumers in sports PPE markets. In this paper, the author reflects on an ethnographic case study and the importance of the involvement of end and co-dependent users in the design of sports PPE. For this study, the design of the Australian safety vests for jockeys, frequently overlooked, was examined for incremental innovation and its relationship with its standards. This was inspiring to consider an assessment of design values in designing product standards in the sports field. As co-design is increasingly applied across multiple sectors, there is a huge need for practical design guides to support product development and co-designing with users. Further research in this field is highly recommended.

Pengaruh Kualitas Produk, Customer Experience dan Citra Merek Terhadap Kepuasan Konsumen pada Produk Cimory Yogurt

This research aims to find out how much influence product quality, customer experience and brand image have on consumer satisfaction with Cimory yogurt products. The population used in this research were UPN Veteran Jawa Timur students who had purchased or consumed Cimory yogurt products. The sample in this study was 100 respondents, namely consumers of Cimory yogurt products for UPN Veteran Jawa Timur students. The sampling technique used in this research is a non-probability sampling technique with a purposive sampling approach. The type of research used in this research is associative research with a quantitative approach. Hypothesis testing used in this research includes Validity Test, Reliability Test, Classical Assumption Test, Multiple Linear Regression Test, t Test, f Test and Coefficient of Determination Test. The results of this research show that product quality, customer experience and brand image simultaneously have a positive and significant effect on consumer satisfaction by obtaining a fcount of 96.221. Meanwhile, partially, product quality has a positive and significant effect on consumer satisfaction by obtaining a t-count of 2.704, customer experience has a positive and significant effect on consumer satisfaction by obtaining a t-count of 8.385 and brand image has a positive and significant effect on consumer satisfaction for Cimory yogurt products among students. UPN Veteran Jawa Timur obtained a t-count of 2.066.

Hasten Industries, Inc.: Timing of Revenue Recognition (ASC 606)

ABSTRACT This case asks graduate and undergraduate accounting students to apply the current U.S. GAAP revenue recognition standard (ASC Topic 606) that became effective for public companies in 2018 and nonpublic companies in 2019. Students should grasp the new rules and understand how the nuances of manufacturing contracts (e.g., number of performance obligations, transaction price allocation, shipment options, product customization, and contract cancelation and compensation) can affect the recording and timing (over time or point in time) of revenue recognition. This case also encourages students to independently research the technical aspects of U.S. GAAP regarding the accounting treatment of these contracts.

Technology osmosis in augmentative and alternative communication: Opportunity, trend, and upshot

Abstract Technology has made a revolution in the realm of augmentative and alternative communication (AAC) possible. This paper presents fresh concepts and solid duties for creating a locally produced assistive technology. The study will also reverse and combine the fundamental specifications for AAC for people with communication problems. The study aimed to examine parents’ perceptions of AAC devices and how cutting-edge technology can meet their needs. Several significant issues are covered in this article, including (a) the difficulties associated with product repair, maintenance, and servicing; (b) familiarizing and educating stakeholders about the AAC devices; (c) product customization; (d) the integration of AAC into traditional educational systems; and (e) future research directions.

Effect of Brand Elements on Organizational Success: A Moderating Approach to the Brand Experience of Muslim and Non-Muslim Customers of Islamic financial Products in Pakistan

The primary purpose of this research is to analyze the impact of brand elements on the achievement of an organization. In addition, the study also examined the moderating effect of brand experience on Muslim and non-Muslim customers' perception of Islamic products in Pakistan. This research examines the significance of brand elements in the success of organizations and compares the reactions of Muslim and Non-Muslim customers of Islamic Products in Pakistan. The researchers used Keller's renowned CBBE model to examine the impact of brand elements on organizational success. This research used the convenience sampling technique to gather data from customers of Islamic products. Following the completion of the missing value analysis, only 124 out of 150 respondents provided their replies. The study analysis indicates that the brand resonance of both Muslim and non-Muslim customers of Islamic products has a positive effect on the success of the organization. Additionally, the relationship between these consumers and the organization is also crucial for achieving success or improved performance. The study shows that both Muslim and non-Muslim consumers in Pakistan have positive responses towards Islamic products offered by Islamic banks. This information can be used by managers to implement this strategy for the success of their organizations.

PENGARUH PRODUCT QUALITY AND PRICE PERCEPTION TERHADAP CUSTOMER LOYALTY DIMEDIASI OLEH CUSTOMER SATISFACTION (STUDI KASUS DI TOKO ONLIE PERSEWAAN ALAT-ALAT GRILL)

This research aims to examine the influence of product quality and price perception on customer loyalty which is mediated by customer satisfaction in an online grill equipment rental shop. A sample of 70 respondents had their data processed using the Smart PLS 4 program. The research results show that: product quality has a direct and significantly positive effect on customer loyalty. Price perception also has a direct and significantly positive effect on customer loyalty, price perception has a direct and significantly positive effect on customer satisfaction. Meanwhile, product quality has no significant effect on customer satisfaction, customer satisfaction has a direct and significant positive effect on customer loyalty, customer satisfaction can positively mediate the relationship between product quality and customer loyalty. Customer satisfaction does not mediate significantly between product quality and customer loyalty. This research enriches information/theory regarding the relationship between products, price perceptions, customer satisfaction and customer loyalty and offers a new approach to the relationship between variables.

Analyses of Business Attributes and Facebook Utilization: Marketing Insights

Microenterprises have a significant impact on the nation's economic development. Given the current trends in marketing, numerous organizations have adopted social media marketing. This study analyzed Facebook's business attributes and perceived effectiveness as an advertising tool for microenterprises. The researchers employed a descriptive-causal research design through random sampling of 320 registered microenterprises that utilize Facebook as an advertising tool. Findings revealed that participants were mostly under sole proprietorship, having one to three employees with an estimated asset size of less than 1,000,000 pesos and being in operation for one to three years. Customer brand awareness, product promotion, engagement reach, and sales volume were found to be highly effective in the perceived effectiveness of Facebook use. Remarkably, the business profile length of operation showed significant differences in terms of volumes of sales, whereas the number of staff in terms of customer brand awareness, engagement reach, and volume of sales; average asset size in terms of customer brand awareness and product promotion indicated significant differences to the perceived effectiveness of Facebook utilization as an advertising platform. Hence, the researchers recommended that microenterprises focus on targeted advertising, allocating advertising investment, and optimizing Facebook's advertising features.

Factor Analysis on Product Quality that Contributes and Affects Customer Satisfaction (Case Study on Muslim Women's Fashion Clothing Brand in Malang Raya)

The purpose of this study is to determine the quality of Muslim Women's fashion products in Malang Raya when measured by the attributes of performance, specialty, reliability, conformity with specifications, durability, serviceability, and perceived quality; determine customer satisfaction of Muslim Women's clothing fashion product when measured by attributes of product quality, service quality, ease of getting products, prices, and how to advertise products, and determine the influence between product quality and customer satisfaction of Muslim Women's clothing fashion product in Malang Raya. The method used in this study was a quantitative descriptive method with data obtained through questionnaires with respondents of 78 Muslim female customers. The data analysis technique used in this study is multiple linear regression using the SPSS application. The result of this study is 7 attributes in the product quality variable including performance, privilege, reliability, suitability, durability, service ability and quality perceived by some respondents in this study have a high level and 5 attributes in the customer satisfaction variable include product quality, service quality, ease of getting products, prices, and how to advertise products most respondents in this study have a high level keep. Thus, it can be concluded that the research hypothesis that states there is a significant influence on the quality variables of Muslim Women's fashion products and variables related to customer satisfaction can be accepted and tested.

PENGARUH CUSTOMER RELATIONSHIP MARKETING, PERSEPSI HARGA DAN KUALITAS PRODUK TERHADAP KEPUTUSAN PEMBELIAN KEJU PROCHIZ PADA PERUSAHAAN PT SINAR MAYURI KLUNGKUNG

Understanding customer behavior when it comes to making purchases is a crucial step in business success. It helps companies better comprehend the market, optimize marketing strategies, and maintain a competitive edge. Managing customer relationships, aligning prices with perceived value, and offering high-quality products all contribute to optimizing customer purchases. This study aims to determine the influence of Customer Relationship Marketing, Price Perception, and Product Quality on the Purchase Decision of Prochiz Cheese at PT Sinar Mayuri, as well as to establish which factor dominates in optimizing the purchase decisions of Prochiz Cheese at PT Sinar Mayuri. Sampling was conducted using a saturated sample consisting of 78 respondents. Data collection methods included observation, interviews, literature review, and questionnaires. The data analysis technique used was multiple linear regression analysis. The study's findings indicate that there is a significant partial and simultaneous influence between Customer Relationship Marketing, price perception, and product quality on the purchase decisions of Prochiz Cheese at PT Sinar Mayuri Klungkung.

Chatbot Based Online Shopping Web Application

This study presents an innovative chatbot system aimed at transforming product recommendation and customization. It integrates feature-based input analysis and user-driven updates to provide tailored product recommendations. Users can input specific product features, leading to personalized suggestions that align precisely with their preferences. Utilizing a dynamic customization framework, the chatbot consistently refines recommendations based on user interests, ensuring a personalized and evolving user experience. The project seeks to reshape the realm of product recommendation systems by providing a seamless and adaptable interface, enhancing the exploration and acquisition of products that precisely match individual needs and preferences.