Customer Demand Research Articles

Supply Chain Optimization in the Package Industry through Machine Learning Analysis

The package industry relies heavily on efficient supply chain management to meet customer demands and maintain profitability. However, managing complex supply chains involving multiple suppliers, transportation networks, and distribution channels poses significant challenges. This research proposes a machine learning-based approach to optimize supply chain operations in the package industry. By analysing historical data on supply chain activities, including procurement, inventory management, and distribution, our system aims to identify patterns and trends to improve decision-making processes. Machine learning algorithms such as support vector machine, naive Bayes, and logistic regression are utilized to forecast demand, optimize inventory levels, and streamline logistics operations. Experimental results demonstrate the effectiveness of the proposed approach in enhancing supply chain efficiency and reducing operational costs in the package industry.

Breakthrough Radical Innovation From An Islamic Perspective: A Literature Review

Radical innovation is a breakthrough change in the overall pattern of the old system, as a form of implementing new ideas to achieve a competitive advantage. Radical innovation is a solution to escape from external environmental pressures in the form of competition, deregulation in the industry, scarcity of resources, and higher customer demands. This study builds a conceptual model from an Islamic point of view: the value of maslahah in all cases provides benefits or goodness from radical innovation. Maslahah means the good rewards promised by God in this world and the hereafter, thus maslahah implies the benefits of this world and the hereafter, which encourages people to always think and produce new things that are beneficial.

Configuration of last-mile distribution networks for an encroaching manufacturer

AbstractThe surge of e-commerce has revolutionized distribution channels, escalating from simple single-channel frameworks to complex multi-channel and omni-channel networks. In particular developments in information technology and rising customer expectations have popularized the transition from multi- to omni-channel distribution, where the classic brick-and-mortar stores can also be part of the omni-channel distribution strategy. This evolution poses intricate challenges for manufacturers, especially in the integration and optimization of these channels. Thus, there is a strong need for an in-depth analysis of how manufacturers navigate the transition across diverse distribution channels to meet the varying needs of different customer segments. To this end, we investigate single-, multi-, and omni-channel distribution strategies for the case of a manufacturer selling both standard and customized products to different customer segments with varying preferences. A central contribution of this research is the creation of an integrated optimization model that resolves a location-routing problem, designing a complex and realistic supply chain configuration suitable for an omni-channel distribution system. This model strategically serves to fragmented customer demands through multiple shopping and delivery options. The outcomes of our study indicate that an omni-channel distribution system is a viable approach, capable of serving more customer segments while simultaneously minimizing logistics costs. In addition, we offer a detailed analysis of the cost implications of in-store pickup versus home-delivery options, providing a comprehensive evaluation of their respective impacts on total logistics costs and customer responsiveness.

Transforming customer engagement with artificial intelligence E-marketing: an E-retailer perspective in the era of retail 4.0

PurposeWith the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.Design/methodology/approachThe primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.FindingsThe results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.Originality/valueThe main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

Circular Economy Strategies in Practice: A Qualitative Examination of Industry Adaptation and Innovation

Objective: This study explores how companies across various industries are implementing circular economy strategies in practice, focusing on the drivers, practices, challenges, enablers, and outcomes of their efforts. Theoretical Framework: The study draws upon multiple theoretical perspectives, including the multi-level perspective on sustainability transitions, the resource-based view of the firm, and institutional theory, to understand the complex factors influencing the adoption and implementation of circular economy strategies. Method: A qualitative, multiple-case study design was employed, involving semi-structured interviews with key informants from 20-30 companies actively engaged in circular economy initiatives. Data were analyzed using a combination of deductive and inductive coding approaches, with member checking and peer debriefing used to enhance trustworthiness. Results and Discussion: The findings reveal that environmental sustainability concerns are the primary driver for companies adopting circular economy strategies, with regulatory pressures, competitive advantage, customer demands, and resource scarcity also playing significant roles. Companies are implementing a wide range of circular economy practices, including eco-design, circular business models, reverse logistics, and industrial symbiosis. Key challenges identified include lack of technical knowledge and skills, inadequate infrastructure and technology, and regulatory barriers, while enablers such as leadership commitment, collaboration and partnerships, and employee engagement are crucial for successful implementation. The study also highlights the positive outcomes of circular economy adoption, including environmental benefits, economic gains, innovation and competitive advantage, customer satisfaction, and employee engagement. Research Implications: This study contributes to the theoretical development of the circular economy field and offers practical implications for managers and policymakers. Originality/Value: This study addresses the lack of comprehensive, cross-sectoral research on circular economy implementation in practice, providing valuable insights into the experiences of companies across different industries.

Decoding E-Commerce: Data Science and Machine Learning Explorations

In the world, the growth of e-commerce is been rapidly increasing day by day. The availability of the products and the demands of customers are highly notable. Here we can use the emerging models of data science and machine learning to build a data driven strategies in developing e-commerce platform. This paper delves into the pivotal role of data science and machine learning in the e-commerce, exploring the applications, advantages, and challenges. Through a better understanding of existing papers, literatures and case studies, it investigates how the data science methodologies and machine learning are utilized for many different applications using different methodologies across the various e-commerce platforms.

A Comprehensive Analysis of Inventory Management and Its Potential Implications on Financial Reports

Abstract: Inventory management plays a pivotal role in businesses' operational efficiency and financial success, encompassing processes aimed at maintaining optimal stock levels while minimizing costs and meeting customer demand. This paper presents a comprehensive analysis of inventory management practices, focusing on their impact on financial performance, challenges, opportunities, and best practices. Through a literature review, the study examines existing research on inventory management, highlighting its significant positive relationship with organizational performance. The research methodology includes correlation analysis, descriptive statistics, questionnaire analysis, and hypothesis testing to explore the intricate dynamics of inventory management. Findings reveal a strong correlation between efficient inventory management practices and improved financial metrics, emphasizing the importance of implementing advanced inventory management systems, enhancing forecasting accuracy, and fostering collaboration within supply chains. The study concludes with practical recommendations for businesses seeking to optimize their inventory management processes and enhance financial performance in today's competitive marketplace

Barriers to adaptation of environmental sustainability in SMEs: A qualitative study.

This study examines the antecedents of environmental sustainability in small and medium enterprises (SMEs) of a developing country and explores the specific internal and external factors for environmental sustainability. The study focused on SMEs in Balochistan, Pakistan, utilizing convenience and purposive sampling techniques to select a sample size of 30 SMEs. In-depth qualitative interviews were conducted using a semi-structured questionnaire. The results of the study revealed that lack of finance and education are major barriers to recognizing and addressing environmental sustainability issues, along with the lack of government support and regulations to ensure compliance with environmental safety laws, hence leading to low concern for sustainability practices among SMEs. Awareness and attitude of SME owners/managers, along with customer demand and government policies, influence the adoption of environmental sustainability practices. Overcoming financial constraints and promoting cooperation among stakeholders are key to fostering sustainable practices in SMEs. This research makes an important contribution to the sustainable management literature by providing new and in-depth insights into the barriers that impede environmental sustainability in SMEs of developing countries.

Research and application of commodity demand forecasting algorithm based on improved ARIMA-LSTM

Through thorough investigation and research, it has been determined that commodities possess a certain degree of timeliness, with long production cycles and short storage times. Accurately predicting customer demand can effectively reduce costs. This paper proposes an improved model based on ARIMA-LSTM, incorporating data cross-correlation analysis for effective data classification. By leveraging the unique characteristics of both ARIMA and LSTM prediction time series models, the data is divided into stationary and non-stationary models for accurate forecasting of future demand for goods. After rigorous testing on the test set, our proposed model achieved impressive results with an accuracy rate of 0.855, precision rate of 0.834, and recall rate of 0.854 respectively. Therefore, the model proposed in this paper has a good performance in forecasting the sales volume of general commodities with time series.

A Review of Research on Pricing Strategies and Pricing Factors of Enterprise Products

As one of the core strategies of enterprise marketing activities, product pricing has an important impact on the market share, profit level and brand image of enterprise products. There are many factors that enterprises consider in product pricing, such as cost, customer demand, brand awareness, market penetration, market trend, market development prospects, market competition environment, policy and other factors, which are important factors affecting enterprise product pricing. This paper firstly introduces the importance of product pricing in marketing strategy, combined with the nature of domestic and foreign enterprise products, and then summarizes the pricing strategy, pricing method and applicable conditions of enterprise products, and analyzes the pricing factors and influencing mechanism of enterprise products. Then, it summarizes the product cost, product types, customer demand, price elasticity, consumer behavior, sales channels, etc. Finally, it summarizes the shortcomings of existing literature research.

Enhancing System Predictability and Profitability: The Importance of Reliability Modelling in Complex Systems and Aviation Industry

Predicting the future has always been a human endeavor, ranging from antiquated methods such as monitoring aquariums for indications of earthquakes to contemporary techniques that evaluate system probabilities and capacities. Taking into account the current emphasis on improving product reliability by customer demands and global competitiveness, we introduce the idea of reliability in the context of the Airbus A320 airplane in this paper. When it comes to business and commercial aircraft, timetable compliance and punctuality are critical components of an aircraft’s profitability. For many operators of commercial aircraft, reaching the 98% reliability criterion is a typical objective. This study examines the Airbus A320 in great detail, concentrating on a particular system scenario that has two possible failure modes, one where gears do not retract after takeoff and the other being when landing, the gears fail to extend. The organization bears specific costs as a result of these shortcomings. The purpose of the study is to examine these expenses and offer insights into the financial ramifications by performing a profit analysis. We examine the failure and repair patterns by utilizing the Markov Process and Regenerative Point method. This study adds to our understanding of the reliability issues facing the aviation sector and has applications for improving the Airbus A320 aircraft’s operational effectiveness and financial performance.

KEUNGGULAN KOMPETITIF SETELAH PANDEMI: STRATEGI REFORMULASI

This research focuses on MSMEs in Indonesia facing the challenges of the COVID-19 pandemic, with the aim of reformulating post-pandemic competitive advantage strategies. The Systematic Literature Review method was used to collect and evaluate relevant literature. The results showed several key findings: MSMEs must utilize resources to explore new opportunities and manage enterprise growth. About 70% of participants emphasized the importance of online sales and distribution channels to meet changing customer demands. Active and transparent negotiations with stakeholders, including employers, investors and labor, were considered crucial. Proactive and creative leadership is also required to implement effective strategies. In addition, pentahelix cooperation between government, business, academia, society and the media is considered important. The research concluded that MSMEs need to adapt to the changes in habits, behaviors, and technology that occurred during the pandemic to reformulate their vision, mission, goals, and business plans. Controlled and well-monitored strategy implementation is key to achieving competitive advantage in the post-pandemic era.

Important drivers for customer satisfaction – a Swedish crisis reflection

PurposeThe purpose of this study is to examine crisis effects on customer satisfaction and underlying drivers by adding a new set of data to previous research. The core questions are: are the findings from Hallencreutz and Parmler (2019, 2021) sustained or can new customer demands, needs, expectations and behaviours be traced in the wake of the ongoing crisis?Design/methodology/approachA first study covering 2005–2017 was completed in 2018, published online in 2019 and in print in 2021 (Hallencreutz and Parmler, 2021). This new study adds the years 2018–2023 to the data set and reuses the partial least squares (PLS) approach to structural equation models, also known as PLS path modelling.FindingsThis additional study sustains the results from the initial study (Hallencreutz and Parmler, 2019, 2021). The variable product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction together with brand image, and the current state of permacrisis has not changed that.Research limitations/implicationsThe study is built on Swedish data from the EPSI Rating Initiative (Eklöf and Westlund 2002) covering customer perceptions in banking, insurance (life and non-life), telco (mobile operators, broadband and Pay-tv) and energy (trade, distribution and heating) over the years 2005–2023.Practical implicationsThe study emphasizes the importance of understanding how customer satisfaction drivers evolve over time in different industries and societal sectors, especially during crises. This additional study sustains the paradigm shift in the studied industries – product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction, and the current state of economic downturn has not changed that.Social implicationsSociety will have to learn to live with political and economic instability and unpredictability for the foreseeable future. To recognize the increasing value deriving from firms’ intangible assets while providing flawless deliveries seems to be a way forward in troublesome times. This is also a catalyst for existing societal trends: the necessary reforms to master sustainable transformations will require an ongoing adaptation process, with both winners and losers across continents.Originality/valueThe world has coped with a global pandemic, and Europe is currently experiencing a humanitarian, political and economic crises caused by a war in Ukraine. This extended period of global instability and insecurity could be called a permacrisis (Collins dictionary, 2022). This study offers a unique quantitative analysis built on Swedish data from EPSI Rating initiative.

Evaluation of Edible Coatings from Components from Chlorella vulgaris and Comparison with Conventional Coatings

The present customer demand for ready-to-eat food items with higher nutritious value and longer shelf life necessitates creative solutions. An edible coating is a sustainable packaging solution that can prevent food deterioration and preserve food quality. Proteins, starch, and the addition of plasticizers are used to create edible coatings. The aim of this study was to develop coating solutions that can best preserve food using isolated starch and proteins from Chlorella vulgaris, and then compare them to coatings that comprise conventional ingredients like chitosan and starch. A number of criteria pertaining to the coatings’ mechanical, optical, thermal, and physical properties were tested. The alternative coatings performed just as well as the conventional ones, with the protein algal coating exhibiting the best thermal, optical, and physical qualities. The food product that needs to be coated can determine which coating is ideal. In conclusion, edible coatings derived from Chlorella vulgaris offer a sustainable solution to preserve ready-to-eat food items, showcasing comparable performance to conventional coatings.

Research and Application of the Simulation Method for Product Development Process Based on System Dynamics

Product development is a complex process involving intricate components, dynamics and constantly evolving internal and external environments, as well as numerous influencing factors. In order to accurately simulate and predict the effectiveness of the development process, this paper proposes a system dynamics simulation method based on information maturity. Different types of development processes are simulated, and the discussion includes the impact of activity information correlation, information evolution coefficient, start time, and other parameters on the dynamic behavior of the process. This study examines a specific mold development process as a case study to validate the method’s feasibility, accurately predicting the duration and cost of the process. It also investigates dynamic fluctuations resulting from uncertain events such as changes in customer demand and resource shortages. The method provides support for process optimization and resource scheduling.

Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

A disaggregated integer L-shaped method for stochastic vehicle routing problems with monotonic recourse

This paper proposes a new integer L-shaped method for solving two-stage stochastic integer programs whose first-stage solutions can decompose into disjoint components, each one having a monotonic recourse function. In a minimization problem, the monotonicity property stipulates that the recourse cost of a component must always be higher or equal to that of any of its subcomponents. The method exploits new types of optimality cuts and lower bounding functionals that are valid under this property. The stochastic vehicle routing problem is particularly well suited to be solved by this approach, as its solutions can be decomposed into a set of routes. We consider the variant with stochastic demands in which the recourse policy consists of performing a return trip to the depot whenever a vehicle does not have sufficient capacity to accommodate a newly realized customer demand. This work shows that this policy can lead to a non-monotonic recourse function, but that the monotonicity holds when the customer demands are modeled by several commonly used families of probability distributions. We also present new problem-specific lower bounds on the recourse that strengthen the lower bounding functionals and significantly speed up the solving process. Computational experiments on instances from the literature show that the new approach achieves state-of-the-art results.

Determinants of stock prices in telecommunication industry: An application of fundamental analysis

The present study attempted to assess the impact of fundamental ratios on the share prices of selected telecommunication companies in India. India has dramatically expanded over the past ten years to become the second-biggest telecoms market worldwide, with 1.17 billion users. The Indian telecom industry has proliferated thanks in part to the government of India’s liberal and reformist policies and strong customer demand. It has become a lucrative investment sector for investors due to its recent and prospective growth. Data on 13 telecom firms indexed in the S&P BSE telecommunication index from 2013 to 2022 were taken from companies’ annual reports, the BSE website (Bombay Stock Exchange), and other secondary sources. Six firm-specific fundamental factors viz. Debt to Equity ratio (D/E), Current ratio (CR), Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to earnings ratio (P/E), Return on equity (ROE), and three country-specific fundamental factors viz. Gross Domestic Product, Inflation rate, and S&P BSE Sensex return were considered. Fixed effect panel regression through Generalized Least Square (GLS) model was performed to find inferences. Debt Equity ratio and Inflation rate were found to impact share price negatively. Conversely, the Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to Earnings ratio (P/E), and Return on Equity (ROE) positively impacted selected companies’ share prices. The study results will benefit individual & institutional investors in formulating their investment and portfolio diversification strategies for gaining a high effective rate of return on their investments.

Vegetable Merchandise Analysis from a Quarterly Sales Data Perspective

In the field of fresh vegetable retailing, the category diversity and time-sensitive characteristics of commodities pose unique challenges to inventory management and pricing decisions. Comprehensively analyzing the distribution pattern of sales volume of different categories and individual items of vegetable commodities and their interrelationships is of great significance to retailers' scientific replenishment and pricing decisions. By categorizing vegetable sales every quarter, a comprehensive analysis is carried out on a category and individual item basis. Visualization was used to show the distribution pattern of sales volume by category and individual item. Subsequently, Kendall correlation analysis was utilized to explore the relationship between the sales volume of different vegetable categories. In addition, the interrelationships between the sales volumes of different vegetable individual items were investigated in conjunction with waterfall diagrams. Through in-depth analysis of sales data, understanding the sales relationship between different vegetable categories can help retailers develop a more refined pricing strategy, make pricing adjustments based on the relative sales of goods, and improve sales revenue and profit levels. Such scientific inventory management and pricing strategies not only help meet customer demand and enhance customer experience, but also strengthen customer loyalty and promote long-term stable business development.

Trends in Service Marketing Strategies for Enterprises

In the context of intensifying market competition and increasingly diversified customer demand, the marketing strategies for enterprise service are facing unprecedented challenges. This paper will delve into the current and future trends of marketing strategies for enterprise services, analyze the key factors influencing these trends, and propose corresponding marketing strategy recommendations. Through a review of relevant literature, empirical research, and case analysis, this paper aims to provide enterprises with targeted marketing strategy guidance to adapt to the ever-changing market environment.