Customer Demand Research Articles

Physical internet enabled sustainable and resilient production-routing problem with modular capacity

PurposeModular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.Design/methodology/approachWe develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.FindingsThe experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.Originality/valueTo the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

Developing Blueprints for Sustainable Excellence: The Integration of Green Intellectual Capital, Green Transformative Leadership, Entrepreneurial Orientation, and Cost Leadership Strategy

The need for sustainability in the hospitality sector is growing more important due to environmental issues and changing customer demands. This research utilized RBV theory to evaluate the role of green transformational leadership, green intellectual capital, entrepreneurial orientation, and cost leadership strategy in achieving sustainability. Accordingly, entrepreneurial orientation was examined as a mediator between green intellectual capital, green transformational leadership (two independent variables) and sustainable performance (dependent variable), whereas, cost leadership strategy was investigated as a moderator on the relationship of entrepreneurial orientation and sustainable performance. Data was collected from 230 senior executives in the hospitality sector, and was analyzed by using SMART PLS 4.0.9.9. The findings indicated that green intellectual capital had a favorable influence on sustainable performance, while the effect of green transformational leadership was found to be insignificant. Additionally, entrepreneurial orientation mediated the link between green intellectual capital and sustainable performance, and also between green transformational leadership and sustainable performance. Moreover, cost leadership strategy positively moderated the link of entrepreneurial orientation and sustainable performance. Finally, the study highlighted significant recommendations for policymakers and for research scholars.

Application of FUZZY Methods for Measuring Leanness of Manufacturing Industries

Manufacturing plays a vital role in the growth of any economy. In India also the manufacturing is growing rapidly and passing through transforming stage. To maintain the customer demands, quality, profitability, and sustainability it is necessary to adapt good manufacturing practices. This paper emphasizes the adaptation of lean manufacturing and development of model for assessing the lean status of different manufacturing sectors. For assessing leanness, manufacturing sectors are evaluated on 29 lean criterions by using Fuzzy TOPSIS. Fuzzy TOPSIS can deal with a variety of questions. Lean criterions are identified, linguistic ratings for different criterion are selected and scores are assigned, and finally lean status is compared, and it is found that automobile sector is leaner as compared to textile and heavy industries. This paper will help the industry, academia and lean practitioners to assess the lean status of their industry and to strengthen their knowledge towards various benefits of lean manufacturing in the industry.

Assessing the problems encountered by companies using e-logistics via a Polytopic fuzzy methodology: A case of Giresun province

Manufacturing companies are always competing because of globalization, both in terms of sectoral level and meeting customer demands. Companies that want to gain competitive advantages want to offer the best quality products and services at the most affordable cost and aim to deliver them to the end consumer by creating added value. This requirement and flexibility level can only be achieved through logistics applications. Businesses that provide efficiency and speed in logistics applications take a few steps forward in market conditions and respond to customer needs on time. It has become a necessity to use electronic-based systems in logistics applications, especially to meet customer requirements. Information communication-based electronic business has also changed the methods and models of doing business for its users. Internet technology has caused businesses to change their business models and practices, along with their production and service structures. Developments in e-commerce and technology have brought e-logistics applications to the forefront and made it necessary to emphasize them. Because e-logistics applications are important for facilitating business operations and increasing performance and customer satisfaction, they also eliminate distances in world trade and offer access to markets that are difficult to access. There are various problems in e-logistics applications and it is important to identify them. In this context, the problems faced by businesses using e-logistics become critical and one of the components that need to be meticulously emphasized. According to the results of the study, it is understood that the most important e-logistics barriers in enterprises are "lack of skill to manage digital technologies to support the system" and "need for qualified personnel". It is obvious that the results will guide business managers.

Logistics distribution scheduling algorithm based on artificial intelligence

In order to improve the quality of urban logistics distribution services and meet customer needs to the greatest extent, improve the utilization rate of service resources and reduce the cost of logistics and distribution services, the author proposes an urban logistics distribution scheduling algorithm based on artificial bee colony. Uncertain dynamic customer demands often occur during the execution of logistics distribution services, resulting in the inability to achieve optimal cost by executing the distribution service according to the original plan, in response to such problems with dynamic customer needs, the author conducted a problem analysis, the corresponding mathematical model is established and transformed into a static problem for rescheduling. The result shows: Using the scheduling algorithm to solve the example 100 times, when the vehicle 1 and vehicle 3 can meet their own load, they can serve customers with new needs, which can make the delivery cost the lowest. In the case of a small number of customers, the algorithm can achieve high accuracy, and when the number of customers reaches a certain scale, the stability of the algorithm will decrease slightly. ConclusionThe algorithm achieves the purpose of promptly responding to changes in customer demand and quickly adjusting distribution services.

DEMOGRAPHIC AND PSYCHOGRAPHIC STUDY AND DEMAND ANALYSIS OF RURAL CONSUMERS IN POST LIBERALISATION ERA

The economic situation of rural residents has changed since economic liberalization, and they are no longer considered impoverished individuals. Rural residents income has increased significantly during the past few years. The information revolution and rural residents rising educational levels have made them aware of many of the newest product offerings. Cultural disparities with their urban counterparts began to fade in a market that was semi-homogeneous for many durable and non-durable businesses. Reduce and attitude had significantly shifted. Teenagers from rural areas try to act like their metropolitan counterparts. A significant shift in lifestyle has occurred, with a growing number of people becoming self-conscious. Social class lines become less distinct. Rural consumers could now easily get commodities thanks to the extensive distribution network of various businesses, which contributed to the rise of rural consumerism. In summary, the paper covers various psychographic and demographic studies as well as an analysis of rural customers demand throughout the post-liberalization era. KEY WORDS: Demand Analysis, Economic Liberalization, psychographic and demographic studies, Rural Consumer

Coordinated last-mile deliveries with trucks and drones: A comparative study of operational modes

Throughout the recent decade, drone delivery technology has been developed to address problems such as last-mile delivery and recurrent traffic jams in metropolitan regions. To resolve the relatively short travel range and limited capacity issues of drones, researchers have proposed to further enhance the utility of drones by deploying them with ground vehicles (e.g., trucks) in tandem. This new concept leads to a new transportation planning problem, i.e., coordinated delivery of trucks and drones (CDTD). In this paper, we conduct a comparative study of three representative operational models: the flying sidekick traveling salesman problem (FSTSP), the traveling salesman problem with drone (TSP-D), and the parallel drone scheduling traveling salesman problem (PDSTSP). Metrics including delivery efficiency and vehicle utilization rate are evaluated for the three models under various scenarios, to extract insights on which operational model is advantageous for which scenario. We also benchmark the algorithms for CDTD by various metrics including result quality, runtime and scalability, and conduct sensitivity analysis to assess the performance of the system under various system parameters, including drone speed, drone range and customer geographic distribution. Finally, a case study is conducted to demonstrate the superior performance of CDTD model as compared to classical TSP model based on real-world data. Experimental results demonstrate that the FSTSP and TSP-D highlight superior performance over PDSTSP and truck-only TSP when the majority of customers are out of the flight range of drone and the customer demand is clustered, as a result of the synchronization and compensation for limitations between the two types of vehicles.

Adaptive stochastic lookahead policies for dynamic multi-period purchasing and inventory routing

We explore a problem faced by agri-food e-commerce platforms in purchasing different, perishable products and collecting them from multiple producers and delivering them to a single warehouse, aiming to maintain adequate inventory levels to meet current and future customer demand, while avoiding waste. Customer demand and suppliers’ purchase prices and supply volumes are uncertain and revealed on a periodical basis. Every period, purchasing, inventory, and routing decisions are made to satisfy demand and to build inventory for future periods. For effective decisions integrating all three decision components and anticipating future developments, we propose a stochastic lookahead method that, in every period, samples future scenarios for demand, supply volumes, and prices. It then solves a two-stage stochastic program to obtain the decision for the current period. To make this approach computationally tractable, we reduce the routing decision in the two-stage program and use an approximate routing cost instead. Given the reduced decision, we then create the final decision via a conventional routing heuristic. We learn the routing cost approximation adaptively via repeated training simulations. In comprehensive experiments, we show that all three components, stochastic lookahead, routing cost approximation, and adaptive learning, are very effective individually, but especially in combination. We also provide a comprehensive analysis of the problem parameters and obtain valuable insights in problem and methodology.

A survey of potential and frequency used of local feedstuffs in the concentrate formulations for beef cattle feedlot in Indonesia

Abstract In Indonesia, the most popular system of beef cattle farming is a feedlot system, in which cattle are fattened for 3-6 months before being marketed. For a feedlot system to be successful, the availability of feed throughout the production process is important. Local feedstuff is commonly utilized as concentrate feed for beef cattle. This study aimed to identify the types of local feedstuff, the factors affecting their selection and availability, and the supply chain, particularly in feedlots across Indonesia. The study involved surveys, through questionnaires, interviews, and observation. Farm, feed mill, and supplier of feed visits were made and the management were interviewed regarding the feed commonly used in the farm and feed mills. Descriptive analysis was used to describe the local feedstuff’s availability and potential in the feed supply. The research results show that the local feedstuff most frequently used in sequence were palm kernel meal (11.97%), solid tapioca waste/onggok (9.83%), molasses (8.97%), dried cassava chips (8.55%), coffee husks (7.26%), copra meal (7.26%), bran pollard (6.84%), rice bran (5.13%), rice husks (5.13%), and corn cobs (5.13%). The important factors for selecting the types of feedstuffs used were demand (26.3%) and availability (26.3%). The most important factor impacting the availability of feedstuff was the harvesting season (26%). Feedstuff suppliers consist of four types: producers, distributors, agents, and retailers who fill customer demand, with distributors being the most dominant choice.

Dynamic base station allocation for 6G wireless networks through narrow neural network

The 6G wireless communication system will utilize the terahertz (THz) frequency band (0.1-10 THz) to meet customer demand for increased data rates and ultra-high-speed communication in future applications. The exponential surge in data traffic, which is supported by dynamic resource allocation. To mitigate this challenge, the use of artificial intelligence-based methods, such as narrow neural network (NNN), can help to smooth the performance of the network. In this paper, an NNN-based approach for dynamic base station allocation for 6G wireless networks is proposed 14 different 6G parameters used to train the NNN model, initially achieving an accuracy of 89.5% and an F1 score of 0.72 for 200 users. Results demonstrate the efficacy of the proposed NNN approach for dynamic decision-making in 6G networks and its potential for application in other domains where similar problems exist. Moreover, the proposed narrow neural network model shows improved results with an increase in number of users and decrease in fully connected layers and regularization strength (lambda). The validation accuracy received is 98.9% and 99.6% for thousand users with single fully connected layer, none (linear) activation function and regularization strength lambda values of 0.01 and 0.001.

Leveraging Digitalization and Servitization to Improve Financial Performance: An Empirical Analysis

This research investigates the interplay between digitalization, servitization, and Corporate Social Responsibility (CSR) in influencing the financial performance of Pakistani banks. The study addresses a critical gap by examining how these factors combine to impact bank profitability in the contemporary landscape of digital transformation and evolving customer demands. A quantitative approach is employed, utilizing survey data collected from branch managers in Pakistani banks. Standardized scales measure digitalization, servitization, CSR, and bank performance. The findings demonstrate a positive direct effect of digitalization on bank performance. Furthermore, servitization strengthens this positive impact, suggesting that banks offering a comprehensive suite of services alongside traditional products achieve superior financial outcomes. Notably, the study reveals no significant short-term contribution of CSR to a bank's financial performance. Thus, the study sheds light on the crucial role of servitization in amplifying the positive effects of digitalization on bank performance. By strategically integrating services with products, banks can leverage digital technologies to become more data-driven and enhance their financial health.

Dynamic Pricing for Anomaly Detection in Online Market Places

Abstract: Online marketplaces have become integral parts of e-commerce, providing convenient platforms for buying and selling goods. Detecting fraudulent listings is crucial for maintaining trust and integrity within the marketplace. This project addresses this challenge by proposing a method for fraud detection based on predicting listing prices and identifying discrepancies betweenpredicted and listed prices and also updating prices based on current market conditions.By using machine learning techniques to predict listing prices based on working features,customer demand and market status, the aim is to uncover potentiallyfraudulent listings where the listed price significantly deviates from the predicted value. The project utilizes a dataset containinginformation about listings on the online marketplace, including features such as product category, description, and location, alongwith the listed prices. Data preprocessing techniques are applied to clean and prepare the dataset for analysis. Machine learning al-gorithms, including regression models and natural language pro- cessing techniques for textual data, are employed to predict listingprices based on their features.This also includes some methods offeature engineering. In conclusion, this project presents a novel approach to fraud detection in online marketplaces by leveragingmachine learning techniques for dynamic price prediction. By identifying discrepancies between predicted and listed prices, thedeveloped model effectively detects potentially fraudulent listings. The integration of this model into the marketplace platform enhances security and reliability, fostering a safer and more trustworthy environment for buyers and sellers. Moving forward,further refinement and optimization of the model can lead to evengreater accuracy and effectiveness in fraud detection.

Microstructural characterization of bimodal composite metal foams under compression with machine learning

Cellular materials are gaining popularity in today’s major sectors. The aim is to develop high-performance materials to meet customer and application demands. This study revolves around the beginning of the failure; computed tomography and statistical image analysis assisted with machine learning were employed to quantitatively characterize the occurring processes within the structure at the beginning of the well-known plateau effect. Simple techniques (e.g., easily obtainable shape parameters, forest decision, 2D image slices) were employed to sort the structural elements into seven classes based on their visual appearances. The presented method can provide additional information about the fracture mode of the different foams, possibly applying/extending them for other types of closed-cell (composite or not) foams.

Organizational Learning Culture and Firm Performance: The Mediating Role of Learning Agility

This article delves into the critical role of learning agility in the Indian context. It specifically explores how an organizational learning culture (OLCu) fosters an employee’s ability to learn and adapt—their learning agility—which ultimately translates into improved firm performance. Employing a cross-sectional research design, the study gathers data through self-administered surveys from over 313 employees working in hardware service firms within the Indian IT sector. Path analysis is then utilized to analyse the collected data and unveil the underlying relationships. The findings unveil a fascinating interplay between OLCu, learning agility and firm performance. The study suggests that OLCu acts as a catalyst, fostering a work environment that encourages continuous learning and development. This, in turn, fosters employee learning agility. However, the research also reveals that learning agility plays a mediating role, meaning it partially explains the effect of OLCu on firm performance. In simpler terms, while a strong OLCu is crucial, it is the employee’s enhanced learning agility that truly unlocks the door to improved firm performance. This research offers valuable insights that can empower organizations in the Indian IT landscape. By nurturing a vibrant OLCu, companies can equip their employees with the tools and support they need to become more learning agile. This agility translates into a workforce that can readily adapt to new technologies, evolving customer demands, and a dynamic business environment. Consequently, firms can expect greater innovation, improved service delivery, and, ultimately, a significant boost in overall performance. This research also contributes significantly to the existing body of knowledge. It adds a fresh perspective to organizational learning theory by highlighting the crucial role of learning agility as a bridge between OLCu and firm performance, particularly in the Indian IT sector.

Progressions in Agriculture and Farming Practices: Indispensable in leading to Up-gradations of Rural Communities

Within rural communities, individuals are contributing efficiently in promoting better livelihoods opportunities. The participation of the individuals in agriculture and farming practices is regarded as one of the primary occupations of the individuals, belonging to rural communities. The agricultural labourers and farmers are making use of traditional methods and procedures in doing their jobs. Furthermore, there are agricultural labourers and farmers who are making use of modern, scientific and innovative methods and procedures. These are regarded as essential in leading to an increase in productivity and profitability. Furthermore, it is vital for individuals to hone their competencies, abilities and aptitude. They need to generate information in terms of various factors, which are facilitating in generating desired outcomes. These include, marketing strategies, labour market factors, customer demands and so forth. As a consequence of being well-aware in terms of all these factors, individuals will be well-prepared in carrying out all types of tasks and activities in an adequate manner. In this manner, they will contribute efficiently in leading to up-gradations of overall living conditions. Therefore, it is well understood, progressions in agriculture and farming practices are indispensable in leading to up-gradations of rural communities. The main concepts that are taken into account in this research paper are, understanding the importance of progressions in agriculture and farming practices; measures to be implemented in leading to progressions in agriculture and farming practices and measures to be implemented in improving conditions of agricultural labourers and farmers.

Involvement of Private Pharmacies in the Biological Diagnosis of Malaria Cases: The Case of Côte d’Ivoire

Introduction. In Côte d’Ivoire, current policy recommends the introduction of rapid diagnostic tests (RDTs) for malaria, including in the private sector, to facilitate effective case management. The main objective of the study was to examine the impact of introducing RDTs in private pharmacies with regard to national recommendations. Patients and methods. In order to assess knowledge, attitudes and practices after a few years of practice, a cross-sectional study was carried out from August to December 2015 among 300 professionals from private pharmacies (PPs) in the northern zone of the city of Abidjan. Results. Overall, PPs performing RDTs accounted for 25.1%. Fever (63.3%), headache (17.7%) and body aches (16.1%) were the main signs that led pharmacists to perform the test. The main criteria determining the choice of RDT are good sensitivity and specificity (45.6%), easy handling (42.6%) and lowest cost (11.8%). In 57.4% of cases, pharmacists performing RDTs said that the cost of the test (CFA 2000 or EUR 3 on average) is the main reason for non-acceptance by customers. In 97.1% of cases, the measures taken in the event of negative results were symptomatic treatment counselling followed by a medical consultation. In 2.9% of cases, some pharmacists recommended an antimalarial. On the other hand, in 92.6% of cases, the measures taken in the event of a positive result were antimalarial treatment with artemisinin-based combination therapies (ACTs). For pharmacies that did not carry out RDTs (74.9%), low customer demand was cited in 56.1% of cases. Conclusion. It would be appropriate for private pharmacy outlets to comply with the legislation by not carrying out RDTs within their pharmacies. Pending the implementation of these texts, it is imperative to improve on the shortcomings revealed.

Machine Learning Enabled Inventory Prediction

Effective inventory management is a critical aspect of business operations, ensuring optimal stock levels, minimizing costs, and meeting customer demand. Accurate inventory prediction plays a pivotal role in achieving these objectives. This project explores the application of the XGBoost algorithm, a powerful machine learning technique, for inventory prediction. XGBoost's ability to handle complex nonlinear relationships and its robust performance make it a promising approach for this task. The project aims to develop an inventory prediction system using the XGBoost algorithm, leveraging historical sales data and other relevant factors to forecast future inventory levels. This project demonstrates the potential of machine learning, specifically the XGBoost algorithm, to revolutionize inventory management practices, enabling businesses to achieve greater efficiency and profitability. Keyword : Machine Learning, Non-linear Relations, Cost Minimization, Stock levels, Profitability, Inventory Prediction.

Innovative decision support tools for perishable food supply chain management

PurposeWe introduce decision support tools aimed at optimizing perishable food supply chain management, effectively balancing conflicting objectives such as the exporter’s product collection cost and the importer’s profit. This involves considering factors like perishability, selling price, discount rate, and order quantity to achieve optimal outcomes.Design/methodology/approachThis study considered a three-echelon supply chain comprising farmers, a single exporter, and a single importer providing a single, random-lifetime, perishable product under deterministic customer demand. The proposed mathematical model derived the optimal order quantity, selling price, and discount rate for the entire supply chain. This integrated optimization model treats both demand and supply sides as a multi-objective problem, employing a nonlinear program and a two-stage capacitated vehicle routing problem formulation. Numerical examples and a case study focusing on Thailand durian supply chain were conducted to illustrate the approach of the proposed model.FindingsTaking into account both the importer’s profit and the exporter’s product collection cost, the proposed integrated supply chain model and tools maximize profitability, minimizes waste, and meets demand by optimizing perishable product collection costs and proposing a discount system for selling prices.Research limitations/implicationsLimited to a single perishable product in a three-echelon international food supply chain. Future research can explore different products and supply chain contexts.Practical implicationsThe tools enhance decision-making for supply chain managers, improving efficiency, reducing costs, and enhancing customer satisfaction in the perishable food industry.Social implicationsThe proposed model aids in local workforce management by forecasting required manpower for upcoming seasons. By factoring in product quality and pricing, it ensures customers receive fresh products at fair prices. Furthermore, the near-zero waste concept enhances storage conditions at importers' facilities, contributing to improved environmental hygiene.Originality/valueThe integrated model and decision support tools offer a novel approach to address complexities and conflicting objectives in perishable food supply chains, providing practical insights for researchers and practitioners.

Conceptualising the effects of green supply chain on firms’ propensity for responsible waste disposal practices in emerging markets

ABSTRACT This study examines how green supply chain management (GSCM) in emerging markets affects firms’ waste disposal practices in Thailand’s dynamic economy. Using partial least squares structural equation modelling (PLS-SEM), we examined 320 Thai SMEs’ survey data to understand GSCM. The research reveals two key findings: A strong positive correlation exists between green supplier initiatives and green manufacturing processes, with suppliers’ sustainable efforts accounting for 27.2% of the variance in green practices (r-square = 0.272). This shows how much green supplier engagement affects manufacturing. Green distribution plays a significant role in promoting customer cooperation in the green economy, explaining 48.9% of the variance in customer engagement with sustainable practices (r-square = 0.489). Green supplier and distribution practices significantly impact waste disposal, but green manufacturing and customer cooperation fall short of anticipated impacts. Green supplier cooperation is essential for green manufacturing but does not directly lead to effective waste disposal, suggesting a disconnection and the potential for more integrated approaches. Green distribution helps meet customer demand for sustainable practices. This divergence suggests a potential oversight in GSCM implementation, emphasizing the need to integrate GSCM considerations into emerging market firms’ strategic frameworks and strategic policy interventions to improve waste disposal practices through GSCM adoption.

Assessing Customer Satisfaction in Mahindra’s Automotive Division: The Role of Service Quality and Technological Integration

This study examines customer satisfaction in Mahindra's automotive division, highlighting the impact of technological integration and service excellence. Using a mixed-methods approach, the study examines qualitative insights from in-depth interviews with consumers and service staff in addition to quantitative data from customer surveys. The results show that excellent customer satisfaction is greatly increased by high service quality, with personnel competency, promptness, and reliability being important contributing variables. To further enhance the client experience, cutting-edge technology integration is essential. Examples of these are linked car features and digital service platforms. The report emphasizes how crucial it is for Mahindra to keep coming up with new ideas and tweaking its service plans in order to stay ahead of the competition and satisfy changing customer demands. The management implications emphasize how crucial it is to allocate resources to both human resources and The findings show that timeliness, dependability, and professionalism—the three qualities that make up service quality—are crucial in influencing consumer satisfaction. Additionally, by offering convenience and customized services, the integration of cutting-edge technologies—like digital service interfaces and linked car features—significantly improves the client experience. The results indicate that in order to exceed customer expectations and maintain competitive advantage, Mahindra should give equal weight to ongoing improvements in service quality and technology advances. In order to promote long-term customer loyalty and happiness, the study offers management practical insights by highlighting the necessity of strategic investments in personnel training and state-of-the-art technologies. Keywords: Customer satisfaction, Technology integration, Strategic investments, Competitive advantage, Personalized services, Management insights, Digital service interfaces, Connected car features.