Profit Improvement Research Articles

All that Glitters is Not Gold! Could M&As Post‐Bank Reforms be Just a Tool for Balance Sheet Embellishment?

AbstractThis study investigates whether the surge of mergers and acquisitions (M&As) that occurred in Italy after the enactment, in 2016, of a new reform of credit cooperative banks (CCBs) ultimately improved their performance. Worldwide, CCBs have proved to play a crucial role in spurring local economic development. With the adoption of a dataset composed of 594 Italian banks and 3933 bank‐year observations from 2008 to 2020, our results show a positive and significant impact of the 2016 reform on M&As among CCBs. However, contrary to the expectations of regulators, our results highlight a puzzling deterioration in bank stability of the CCBs involved in M&As, with no relevant improvement in capitalization and profitability, reinforcing the view that bigger is not always better, at least in the context of CCBs. Our evidence sheds light on the perils underlying a massive consolidation process of local banking industries, calling for a regulatory recalibration and further research investigating the impact of different growth models capable of preserving the diversity and unique characteristics of banks, rather than pursuing a ‘one‐size‐fits‐all’ model.

Optimization of reversible solid oxide cell system capacity combined with an offshore wind farm for hydrogen production and energy storage using the PyPSA power system modelling tool

AbstractEight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%.

Implementing data-driven financial management systems in SMEs: A case review approach

The adoption of data-driven financial management systems has become increasingly vital for the growth and sustainability of small and medium-sized enterprises (SMEs). This review explores the implementation of such systems through a case review approach, analyzing their impact on financial decision-making, operational efficiency, and overall business performance. SMEs often face challenges in managing their financial processes due to limited resources, inefficient manual methods, and a lack of real-time data insights. Data-driven financial systems, leveraging automation, artificial intelligence (AI), and analytics, offer a solution by streamlining these processes, enhancing accuracy, and improving decision-making. The review examines three SMEs from different sectors manufacturing, retail, and services that have implemented data-driven financial systems. Through qualitative interviews, financial data analysis, and performance assessments before and after system implementation, the research identifies key improvements in cash flow management, profitability, and financial reporting. Each case highlights how automation and real-time data access have led to more strategic financial decisions, reduced costs, and operational efficiency. The findings suggest that data-driven financial systems not only increase financial transparency but also provide a competitive advantage by enabling SMEs to make informed decisions quickly. However, the review also identifies barriers to implementation, such as technical expertise, costs, and employee training, emphasizing the need for tailored solutions based on company size and sector. The review concludes by discussing future trends in financial technology adoption among SMEs and offers recommendations for facilitating successful integration. This research contributes to the understanding of how data-driven financial management can drive sustainable growth in SMEs. Keywords: Data-Driven, Financial Management, SMEs, Review.

Scheduling and assessment of multi-area virtual power plant including flexible resources using swarm intelligence technique

A virtual power plant (VPP) makes the distributed energy resources (DERs) visible to support grid services, promoting clean energy use by offering an alternative to reduce reliance on conventional sources. This paper proposes optimized operation and scheduling of VPP by forming a group of flexible resources that includes renewables i.e., solar, wind power, fuel cell, and co-generation units along with storage facilities to form a holistic network in a multi-area interconnected system framework. Initially, the scheduling is performed for a single area system in which a comparison is made with the available work relevant to the proposed formulation. In addition, the scheduling is further extended to the proposed multi-area based virtual power plant (MAVPP) performed on a day-ahead (DA) basis and the target objective(s) i.e., net profit and emission are optimized. Furthermore, a recently developed marine predator optimization algorithm (MPOA) is employed to solve the proposed problem formulation and the results are compared and discussed extensively with conventional techniques. Also, the analysis pertaining to MAVPP is carried out which is beneficial in terms of economic and environmental perspective. The result reveals that the maximum value of net profit for MAVPP is converging to an optimum value of 59,351.88 $ followed by the attained value of minimum emission of 51,700.73 Kg by consuming an elapsed time of 92.96 and 104.76 s., respectively in 200 iterations. The outcomes are promising and upon comparison with the available literature work, the superiority of the selected technique is evident in terms of computational time, improvement of net profit and reduction in emission followed by the ease of convergence behaviour in fewer iterations.

Synergistic enhancement for energy-saving, emission reduction and profit improvement in iron and steel manufacturing system: Strategies for parameter regulation and technologies integration

The steel industry, characterized by high energy consumption and carbon emission, poses a significant threat to global energy and environmental security. In response to the energy conservation and dual-carbon targets, the steel industry must urgently find sustainable pathways for transformation and development. Steel production structure optimization and the application of cutting-edge technologies have been proven to be highly effective. However, the complexity and variability of actual production conditions highlight the absence of a methodology capable of adapting to real production scenarios, elucidating influencing principles across multiple factors, analyzing synergistic mechanisms of multiple indicators, and proposing collaborative control strategies. Furthermore, the potential for technology applications that are closely integrated with the company’s resource endowment remains uncertain, leading to an unclear overall strategy for energy saving and carbon reduction. This study develops a multi-scale optimization and evaluation model for ISMP, incorporating interconnected and matching processes, as well as nested multi-indicators, in response to the context. Subsequently, the disturbance mechanisms of various factors are accurately identified and a multi-indicator synergistic control strategy and framework are proposed. Following optimization, the exergy loss, energy consumption, carbon emission intensity, cost, and pollutant emissions of ISMP per tonne of steel are reduced by 1418.70 MJ, 42.93 kgce, 154.79 kg, 171.21 CNY, and 0.0168 kg, respectively. Additionally, measures such as reducing moisture content in coking coal can effectively regulate multiple indicators. Based on the proposed sensitivity quantification method, the temperature of hot blast is identified as the most effective controllable factor. Technologies like hydrogen-rich fuel injection, integrated waste heat recovery, and solar photovoltaic & energy storage have shown favorable prospects, according to analysis conducted from various angles regarding economic viability and potential for energy-saving and carbon reduction. However, the application of aforementioned technologies may be limited by financial consideration, necessitating cautious and judicious investment choices.

A Meta-analysis of the Profit-Sharing Practices among Engineering, Construction, and Related Firms: Strategy to Increase Company Revenue

This paper explores the impact of profit sharing on employee motivation, productivity, and company performance in the engineering and construction industry. Through a meta-analysis of recent studies, it identifies key factors that influence the effectiveness of profit-sharing programs. The findings reveal that profit sharing significantly boosts employee motivation and productivity, with increases of 15% and 12% respectively. Moreover, job satisfaction rates are 20% higher among employees participating in profit-sharing programs. However, there is a notable preference split, with 60% of employees favoring profit sharing for its potential higher earnings, while 40% prefer fixed salary increases for stability. The research further indicates that frequent and actively managed profit-sharing distributions, such as quarterly payouts, lead to better financial performance, with a 10% increase in overall performance and a 5% improvement in profitability. Project-based profit sharing in the construction sector is particularly effective, resulting in a 25% increase in project completion rates and a 15% reduction in project costs. Additionally, profit sharing is more effective in unionized organizations, with an 18% increase in effectiveness, and mandatory profit-sharing frameworks enhance worker satisfaction and productivity. Based on these findings, the paper recommends developing tailored profit-sharing strategies that cater to different employee preferences, implementing frequent and active management of profit-sharing distributions, adopting project-based profit sharing in project-centric industries, engaging with unions for fair terms, advocating for supportive regulatory frameworks, and continuously monitoring and evaluating the effectiveness of these programs. By adopting these strategies, engineering and construction firms can enhance employee motivation, improve productivity, and achieve better financial performance, driving organizational success.

Different Types of Milk Flow Curves and Factors Affecting Milkability in Buffalo Species

Buffaloes are characterized by longer teats and teat canals and stronger muscular resistance of the teat wall than cattle; it is necessary to have a high vacuum level to open the teat canal and begin milk ejection. In buffalo milking management, milk yield, and flow profiles are essential parameters to record and evaluate. The milking machine is a critical point, and the characteristics of the milking vacuum and the pulsation rate are closely related to milk flow observations. In Italy, the most used vacuum levels are 44-46 kPa (range 40-53 kPa). The data on the milkability traits of the Mediterranean Italian breed made it possible to classify eight different types of milk flow curves due to anatomical, physiological, and management differences. This study aims to evaluate the main factors influencing milkability in dairy buffaloes. The results suggest the detachment of the milking cluster to reduce the decreasing and blind phases with the following advantages: reduction of the total milking time and consequently of the worker's time, improvement of the farmer's profitability and milk quality through decreasing the incidence of mastitis. Milk ability is influenced by physiological, sanitary, management, and genetic factors. In Mediterranean Italian buffaloes milked with the Automatic Milking System (AMS), a considerable variation in milk ejection and, consequently, in the milk flow curve was found compared to the conventional one, with better pre-stimulation, independent milk ejection for each teat, optimal milking of all quarters. In conclusion, continuous milkability monitoring will help optimize milking practices by reducing labor time and increasing farmers’ income through better milk quality. In addition, the identification of buffaloes with desirable types of milk flow curves could be helpful for buffalo breeders’ associations to address farmer management and to define potential new breeding objectives.

Digital Twins in 3D Printing Processes Using Artificial Intelligence

Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration of new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables modeling and research into ways to achieve better sustainability, greater efficiency, and improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, and potential research directions to provide the infrastructure and underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along these lines allows for a better consideration of their individual risk factors and use of available data, resulting in an approach to generate holistic virtual representations (DTs) to facilitate predictive analyses in industrial practices. Artificial intelligence-based DTs are becoming a new tool for monitoring, simulating, and optimizing systems, and the widespread implementation and mastery of this technology will lead to significant improvements in performance, reliability, and profitability. Despite advances, the aforementioned technology still requires research, improvement, and investment. This article’s contribution is a concept that, if adopted instead of the traditional approach, can become standard practice rather than an advanced operation and can accelerate this development.

La administración sanitaria privada en España: análisis de sus resultados desde 2015 al 2021.

This study details the economic progression of the private healthcare sector in Spain from 2015 to 2021,focusing on the financial statements of the main entities. Using a quantitative approach, data from the SpanishMercantile Register was examined, applying ratio and trend analysis. The results highlight a notable improve-ment in financial profitability and the soundness of the capital structure, evidencing a reduction in long-termindebtedness. The research highlights the predominance of the Quirónsalud group, which demonstrateda substantial increase in equity and achieved outstanding financial profitability. The profitability of thecompanies in the sector is observed to have evolved positively, standing out for their ability to adapt andprosper even in the context of the pandemic. The conclusion emphasises the favourable economic evolutionof the sector, highlighting the improvements in profitability and financial structure of the companies analysed.

Pharma 4.0: A deep dive top management commitment to successful Lean 4.0 implementation in Ghanaian pharma manufacturing sector

The primary aim of this study is to assess the significance of top management commitment in the context of Lean 4.0 implementation within the pharmaceutical manufacturing industry in Ghana. The study seeks to understand and evaluate the overall effectiveness and achievements associated with adopting Lean 4.0. Employing a positivist mindset, the research utilizes an explanatory quantitative research design and a survey technique. Data collected from 181 employees of pharmaceutical companies in Ghana undergo analysis using SmartPLS (version 4) and IBM SPSS version 26. The study employs a combination of descriptive statistics to summarise data characteristics and inferential statistics to test various hypotheses related to Lean 4.0 adoption. The analysis reveals that the successful integration of lean methods and Industry 4.0 technologies requires meticulous management. Simultaneously, individual implementations of lean principles and Industry 4.0 technologies positively impact business performance. Surprisingly, the study does not observe a substantial positive influence of Lean 4.0 on corporate performance, suggesting that immediate improvements in efficiency or profitability may not result from the adoption of this framework. This research contributes to the field by highlighting the need for careful management in integrating lean methods and Industry 4.0 technologies. It also emphasizes the positive impacts of lean principles and Industry 4.0 technology on business performance. The unexpected finding regarding the lack of immediate improvements in corporate efficiency or profitability with Lean 4.0 adoption prompts considerations of initial implementation challenges or the organization's need for time to adapt to this integrated approach.

Financial Performance Assessment Using Balanced Scorecard Methodology in Indonesian Banks

The objective of this study is to assess the financial performance of XYZ Bank through the lens of the balanced scorecard approach. The research method employed is qualitative, with data drawn from the annual reports of the banks in question for the period spanning 2020 to 2021. This period encompasses both the pre-merger and post-merger phases of Bank Syariah Indonesia. The findings of this study indicate that, from a financial perspective, the combined ROE ratio of the three Islamic banks, which has undergone a merger, is improving and includes healthy qualifications, approaching 15%. The customer perspective reveals an encouraging improvement in profitability, which is attributable to both customer migration from the predecessor bank to the merged bank and new customer growth. The merger has facilitated operating efficiency due to the synergy of the merger, leading to an increase in employee productivity, as measured by revenue. The establishment of several business unit networks represents an innovative form of internal business process, which can facilitate service delivery. The implications of this research provide a more detailed description of performance measurement using data from the company's published annual reports. Previous balance scorecard research employed survey methods in order to obtain information related to the growth and learning perspectives, as well as the customer and internal business process perspectives. In contrast, this study uses only data from the company's financial statements and annual reports.

Does climate change matter for bank profitability? Evidence from China

Using panel data from 87 China’s banks from 2011 to 2022, this research investigates whether and how climate change affects bank profitability. It is discovered that the improvement of bank profitability is severely hampered by climate change. The main ways that climate change affects bank profitability are by causing financial losses to bank creditors, changing the likelihood of defaults and the quality of bank credit assets. Energy conservation and carbon reduction, the implementation of green financial policies, and ensuring that banks have enough capital are all factors that can help mitigate the negative effects of climate change on bank profitability. In addition, climate change has a greater negative effect on the profitability of small-sized banks, regional banks, and banks with lower levels of liquidity. In conclusion, this study offers forward-looking suggestions for banks to reduce risks from climate change, which is critical for encouraging low-carbon and green development and averting systemic financial risks. It also offers theoretical references for Chinese banks to develop customized policies and strategies to address these risks.

Intelligent Farming Systems in Future Using Machine Learning: A Focus on India

Abstract: The integration of machine learning (ML) into Indian agriculture holds transformative potential for addressing critical challenges such as climate variability, water scarcity, soil degradation, and market fluctuations. This paper explores the current state of ML applications in Indian agriculture, highlighting successful case studies and initiatives led by government, private sector, and academic institutions. It discusses the technological integration of ML with the Internet of Things (IoT), remote sensing, and blockchain to enhance precision farming practices. Key barriers to widespread adoption, including data quality, infrastructure, and farmer awareness, are identified, along with strategies to overcome them. Future directions emphasize the importance of robust data infrastructure, localized ML models, collaborative research, sustainable practices, and supportive policy frameworks. By leveraging ML, Indian agriculture can achieve significant improvements in productivity, sustainability, and profitability.

Impact of crop rotation and tillage operations on mitigating greenhouse gas emissions and evaluation of sustainability index in rice- wheat-green gram cropping system of north Bihar

In North Bihar (NB), the conventional rice-wheat cropping system has led to soil, water, and environmental degradation, alongside low profitability, threatening sustainability. To address these concerns, a thorough field research was conducted over the course of three years to assess different methods of tillage and crop establishment in a rice, wheat, and greengram cycle. The experiment involved five scenarios with different combinations of crop rotation, tillage techniques, seeding procedures, fertilizer use, and irrigation strategies. Uncertainty analysis showed no significant change in mean and variance estimation among seven scenario replications at 5% significance level. Compared to traditional farming (SN-1), managing DSR-rice (SN-5) increased profitability by 17.56%, improved energy use efficiency (EUE) by 32.16%, and reduced irrigation by 24.76% and global warming potential (GWP) by 23.46%. Similarly, substituting zero tillage wheat (ZTW) SN-5 resulted in comparable profitability gains (18.25%) and significant improvements in irrigation (10 %), EUE (+48.65%), and GWP (−20 %) compared to SN-1. Green gram ZT also showed increased profitability (17.35%), with notable improvements in EUE (+38.31%) and GWP (−12.92%) compared to SN-1. Principal component and correlation analyses revealed relationships between total energy inputs, yields, economic returns, and sustainability indices, highlighting the benefits of crop rotation and tillage practices in optimizing resource use. The study suggests that compared to conventional systems, significant improvements in productivity, profitability, energy-use efficiency, and environmental mitigation can be achieved with Crop Rotation and Tillage Operations techniques.

Economic viability and production of cattle in confinement under high-grain and conventional diets

Brazilian beef cattle farming stands out for its large-scale production, playing a vital role in national and international trade. Despite this, there is a need for improvement in productivity and profitability. The extensive model, despite territorial demand, exhibits low use of technologies and problematic management. Intensive livestock farming minimizes these issues, offering greater production efficiency by reducing the age at which cattle are finished and optimizing the use of space on the property. The choice of diet in confinement systems aims to enhance the cost/benefit ratio. This research aims to compare the performance of Nelore cattle subjected to different diets during the finishing phase in confinement, considering intake, productivity, carcass characteristics, and economic viability. For the experiment, 20 Nellore cattle were used, divided into two groups of 10 animals, where one received the treatment of feed + forage, and the other a high-grain diet. For the variables of average daily gain (ADG), total weight gain (TWG), and feed-lot time, there was no effect (P < 0.05) from the treatments. However, an effect (P < 0.05) on carcass yield between diets was observed, directly impacting the economic viability of beef cattle production, as the abattoir pays per kilogram (cwt) of carcass.

Simple heuristics for the joint inventory and pricing models with fixed replenishment costs

This study considers the joint inventory and pricing problem of a firm selling a single item over a multi-period planning horizon. A fixed replenishment cost is incurred whenever the replenishment decision is made. Period demands are considered to be non-stationary, stochastic, and price-dependent. The literature shows that the optimal policy in such a system is of an (s, S, p)-type. The implementation of this type of policy might be challenging in practice due to the prevailing computational difficulty in determining the optimal policy parameters. This study, therefore, aims to develop computationally efficient heuristic approaches for the joint inventory and pricing problem. The proposed heuristics provide different levels of flexibility in making inventory and pricing decisions. Furthermore, they are essentially built on the concept of the replenishment cycle and do not require solving a stochastic dynamic program. Our numerical study demonstrates the profit efficiency of the proposed heuristic approaches against the optimal policy. The results also indicate that firms lacking the technical ability to employ a dynamic pricing strategy might still gain significant profit improvement by only using a dynamic inventory control policy.

Modeling Determinants of Profitability in Ethiopia Manufacturing Companies: A Dynamic Panel Model Approach

Background: Profitability is the primary measure of the overall success of company. The analysis of profitability ratios is important for the shareholders, creditors, prospective investors, manufacturers and government alike. Objective: The objective of this study was fitted a dynamic panel regression model that efficiently fit the data and further to identify the determinants contributing significantly to profitability in Ethiopia. Methods: This study was conducted on secondary data of 32 sample manufacturing companies collected from the audited financial statements of large tax payers’ office and national bank of Ethiopia. The study was covered a period of seven years from 2011 to 2017. The panel unit root test of Levin-Lin-Chu tests was made for each variable and applied first difference transformation for the variables that had unit root. A dynamic panel regression model was utilized for data handling technique using generalized method of moments (GMM) estimation. Results: We compared the results when one unit increase in lagged profitability, managerial efficiency, capital intensity, GDP, exchange rate and one ratio decrease in leverage, ceteris paribus, turn out were found to increase the profitability of manufacturing companies by around 0.69, 0.179, 4.52E-06, 3.844, 0.04 and 0.393 ratio, respectively. The model reveled that previous profitability, leverage, capital intensity, managerial efficiency, GDP and exchange rate had a statistically significant (P<0.05) effect on companies’ profitability in Ethiopia. Conclusion: As per our findings, the manufacturing companies should minimize leverage financing from its capitals and should emphasize the management of appropriate financing to increase profitability. Finally, we recommended that policy makers should coming up with better policies on improvement of profitability.

Quality and Pricing Decisions in a Dual-Channel Closed-Loop Supply Chain Considering Imperfect Product Recycling

Level of quality not only affects demand but also affects the proportion of imperfect products and then affects profit. This paper takes the quality level as a decision variable, considers both the consumer demand and the recovery of imperfect products in a dual-channel closed-loop supply chain (CLSC), and studies the significance of a revenue-sharing contract for coordination. The results show that the relationship between the optimal price and the quality level is affected by the repair cost of imperfect products and that the consumer’s attention to price and quality affects the trend of the overall profit with the quality level. In some cases, simply improving the quality level cannot expand the demand and is not even conducive to the improvement of green supply chain profits. It is also found that in both centralized and decentralized scenarios, coordination contracts can effectively motivate retailers to collect waste and help improve the economic efficiency of green supply chains.

Enhancing productivity and sustainability of ravine lands through horti-silviculture and soil moisture conservation: A pathway to land degradation neutrality

Ravine lands are the worst type of land degradation affecting soil quality and biodiversity. Crop production in such lands is impossible without adopting proper conservation measures. In-situ moisture conservation techniques could play an instrumental role in restoring ravine lands by improving soil moisture. We hypothesized that restoring ravine land through a combination of tree planting, fruit crop cultivation, and in-situ moisture conservation practice would result in significant improvements in productivity, profitability, and soil fertility. An experiment was conducted involving the combination of Malabar Neem (Melia dubia) and Dragon fruit (Hylocereus undatus) in conjunction with in-situ soil moisture conservation measures specifically involving half-moon structures (HM). The experiment was conducted under randomized block design (RBD) comprising eight treatments. These treatments include sole Melia cultivation (MD 3m × 3m), sole cultivation of dragon fruit (DF 3m × 3m), silviculture system (MDF-3m × 3m), horti-silviculture system with larger spacing (MDF-4m × 4m), sole Melia cultivation with in-situ moisture conservation (MDH-3m × 3m), sole Dragon fruit cultivation with in-situ moisture conservation (DFH-3m × 3m), horti-silviculture system of Melia and Dragon fruit with in-situ moisture conservation (MDFH-3m × 3m), and horti-silviculture system with larger spacing and in-situ moisture conservation (MDFH-4m × 4m). Each treatment was replicated thrice to evaluate their impact on productivity, profitability, soil fertility, and carbon sequestration for 8 years (2016–2023). The results revealed that the horti-silviculture system (MDFH-3 × 3 m) exhibited the highest total tree biomass and total carbon sequestration with an increase of 183.2% and 82.8% respectively, compared to sole Melia cultivation without HM and sole Melia with HM. Furthermore, sole Melia with HM augmented soil nutrients (N, P, K, and SOC) by 74.4%, 66.4%, 35.2%, and 78.3%, respectively, compared to control (no planting), with performance at par with MDFH-3 × 3 m. Similarly, sole Melia with HM enhanced SOC stock and SOC sequestration rate by 79.2% and 248% over control. However, it was found at par with MDFH-3 × 3 m. The horti-silviculture system (MDFH-3 × 3 m) consistently produced the highest fruit yield throughout the years surpassing other treatments. This treatment increased the average dragon fruit yield by 115.3% compared to sole dragon fruit without HM. Hence, the adoption of the horti-silviculture system (MDFH-3 × 3 m) could be a promising strategy for achieving enhanced environmental and economic benefits in ravine lands. Therefore, dragon fruit based horti-silviculture system (MDFH-3 × 3 m) could be recommended for restoration of ravine lands, improving land productivity, and mitigating impact of soil erosion particularly in Western India or similar agro-climatic regions of the world.

E-commerce Platform in Digital Economy: Analysis and Optimization of Amazon's Footwear Store

With the continuous deepening of globalization, cross-border e-commerce has become an important part of global trade. Especially driven by digital payment technology, network technology, industrial upgrading and policy support, China's cross-border e-commerce industry has developed rapidly. This research aims to analyze the sales data of men's footwear on the Amazon platform, understand market trends, user behavior patterns, and product characteristics, and then provide data-supported marketing strategies and operational decisions for e-commerce platforms. We have adopted a variety of data analysis techniques, including data cleaning, ARIMA model prediction, price band analysis, repeat purchase rate analysis, user profiling (RFM model), sentiment analysis and cluster analysis. Through comprehensive analysis of multi-dimensional information such as store sales data, product attributes, market hot words, and user evaluations, market dynamics and user preferences are revealed. We have comprehensively applied a variety of data analysis models and techniques, such as using the ARIMA model for sales prediction, the LDA model for sentiment analysis, and the RFM model combined with cluster analysis to construct a refined user profile. Through in-depth analysis of user transaction behaviors and product attributes, accurate marketing strategies can be provided for e-commerce platforms, enhance user experience, increase user stickiness, and thus achieve business growth and improvement of profitability. At the same time, the research also points out the importance of brand building, supply chain management, risk assessment and other aspects, providing strategic suggestions for the long-term stable development of cross-border e-commerce.