Industrial Planning Research Articles

Photovoltaic systems promote grassland restoration by coordinating water and nutrient uptake, transport and utilization

The extensive use of fossil fuels puts ecological and economic coordinated development at risk. Photovoltaic systems relieve the pressure of resource extraction and energy generation on climate change, and their installation and module operation affect vegetation productivity and grassland restoration by changing the microenvironment and ecosystem processes. However, it is still unclear whether plants adapt to these changes in photovoltaic systems to sustain their growth. Here, combined stable isotope techniques with plant traits, plant adaptations to photovoltaic systems at different scales were analyzed from the perspectives of water and nutrient sources, uptake, transport, and utilization. The results showed that photovoltaic systems increased water uptake by the dominant species Leymus chinensis from the top 10 cm of the soil and the aboveground biomass. This was associated with strong plasticity of roots and conduction tissues, which improved resource uptake from the fertile surface soil and transport within the plant, and promoted photosynthesis and leaf water loss by inducing stomatal opening. Different from fixed photovoltaic systems, tracking photovoltaic systems improved ecosystem water use efficiency and surface soil nutrient availability by reducing soil temperature. Tilting tracking photovoltaic systems especially promoted biomass accumulation and grassland restoration. The study suggested that photovoltaic systems promoted vegetation restoration of grassland ecosystem in semi-arid region through the water and nutrient coordination and the carbon-water coupling, and provides a solution for reasonable planning of photovoltaic industry and sustainable socio-economic development.

Evaluating Industrial Areas with Spatial Multicriteria Analysis (SMCA)

The assessment of industrial areas stands as a pivotal challenge, increasingly recognized for its urgency across various regions. The industrial sector is a cornerstone of national economic development, contributing significantly to growth and enhancing community welfare. Nonetheless, the establishment of industries also poses environmental and societal risks, primarily due to potential pollution. This study focuses on evaluating the suitability of industrial zones in East Cikarang District, Bekasi Regency, through spatial analysis, benchmarking findings against government-regulated industrial areas as outlined in the Bekasi Regency spatial plan for 2011-2031. Employing Spatial Multicriteria Analysis (SMCA) via ILWIS software, this research aims to deliver a comprehensive assessment of industrial locations by considering variables such as proximity to roads, rivers, settlements, land slope, and alignment with designated industrial zones in the regional spatial plan. The analysis reveals a nuanced classification of industrial suitability, with 48.55% of the area deemed highly suitable, indicating a substantial portion of East Cikarang District is favorable for industrial development. Conversely, a smaller segment falls into the less suitable or unsuitable categories. Overall, the study concludes that 55% of the region qualifies as suitable for industrial purposes based on the SMCA-derived spatial model, with 98.1% of the assessed area aligning with the criteria for designated industrial zones. These insights are intended to guide industrial planning and environmental management strategies, fostering sustainable development within the region.

Sugarcane yield estimation in Thailand at multiple scales using the integration of UAV and Sentinel-2 imagery

Timely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km2 was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making.

A robust system model for the photovoltaic in industrial parks considering photovoltaic uncertainties and low-carbon demand response

Against the backdrop of carbon peaking and carbon neutrality initiatives, industrial parks have the potential to mitigate external electricity procurement and reduce carbon emissions by incorporating photovoltaic and energy storage systems. However, the inherent unpredictability in photovoltaic power generation poses notable challenges to the optimal planning of industrial parks. In light of this, the present study proposes a robust planning model for the distribution of photovoltaic and energy storage systems within industrial estates, taking into account uncertainties in photovoltaic output and low-carbon demand response. The primary objective of the model is to minimize the yearly comprehensive cost of the industrial park. It is grounded in the carbon emission flow theory, utilizing dynamic carbon emission factors calculated throughout the year as the pricing basis for real-time electricity rates informed by demand response. Subsequently, historical photovoltaic output data are employed to formulate typical output scenarios and their probability distributions through scenario clustering. These norms and constraints serve to bind the associated uncertainty probabilities. Consequently, a two-stage distribution robust model for the photovoltaic and energy storage system is established, employing a data-driven methodology. The efficacy of the proposed model is substantiated through a case simulation of an industrial park utilizing the CPLEX commercial solver. This approach not only underscores the importance of addressing uncertainties in photovoltaic power generation for industrial park planning but also showcases a practical application of the developed model.

Implementation of Frugal Innovation Approach in Moroccan Industry through the Development of a New Frugal System

To keep pace with economic growth in an environment marked by globalization, competition, and the significant magnitude of market requirements, companies must be dynamic to adapt quickly to new demands. This dynamism must be achieved through frugal projects aimed at achieving better performance with fewer resources. In line with this vision, we have proposed a new frugal innovation approach to develop a new concept of machines to meet the requirements of Smart Automation, which is the main element of the transformation plan defined by our company as a strategy for the coming years. This strategy aligns with the direction set by Morocco for the second generation of the Industrial Acceleration Plan 2021–2025. This work serves to introduce the objectives of the second-generation Industrial Acceleration Plan in Morocco, describe a company transformation strategy with an emphasis on the Smart Automation component and its context, explain frugal innovation applications within our organization and their outcomes, and outline the project and frugal innovation approach perspectives in Morocco. The results demonstrate the effectiveness of the newly proposed and implemented concept of frugal innovation, illustrated in a cost reduction of 59.7% compared with the manual process, of less than 56.3% compared with the existing semi-automatic process, and of less than 36.8% compared with the fully automatic process and in the improvement of quality by a reduction in customer complaints of 72.7% and in the scrap rate percentage of 0.2%. This has been achieved by providing cost-effective solutions in our competitive automotive environment. These results reinforce the importance of the approach in its global application.

РАЗВИТИЕ ПРОГРАММНОГО УПРАВЛЕНИЯ РЕГИОНАЛЬНЫМИ АГРОПРОДОВОЛЬСТВЕННЫМИ СИСТЕМАМИ

Regional agro-food systems are important components of the national economic complex, ensuring the country's food security, development of rural areas, and improving the level and quality of life of the population. In modern conditions, government programs are becoming the main tool for strategic planning of industries, regions and the national economic system as a whole. The new system for managing state programs, approved in 2021, is aimed at eliminating many problems in the implementation of the program-target method, in particular, eliminating the ineffective use of budget resources and strengthening interdepartmental interactions. A new approach to the development, monitoring and implementation of state programs in the agro-food sector involves the addition of state programs with a number of parameters aimed at the fullest implementation of Russia’s national development goals. Proposals for improving the state programs of the Saratov region “Development of agriculture and regulation of markets for agricultural products, raw materials and food in the Saratov region” and “Integrated development of rural areas” are substantiated. The importance of transforming the redistribution model of financing the regional economy, which creates certain problems for regions when co-financing state programs, is emphasized.

Pharmaceutical retail supply chain responses to the COVID-19 pandemic

AbstractIn the wake of the COVID-19 pandemic, the importance of resilient supply chain and operations management in the pharmaceutical industry has intensified. A particular challenge has been the timely adaptation to rapid fluctuations in the business environment to cope with disruptions and ensure the adequate supply of medical equipment and pharmaceuticals. In this study, we perform a simulation-based analysis of pandemic-triggered impacts on pharmaceutical supply chains in Germany with the aim of examining supply chain response to the COVID-19 pandemic. To accomplish this, we build a digital supply chain model using a hybrid approach that combines case study analysis and discrete-event simulation implemented in anyLogistix. Based on the results, we propose a specific response action plan for the pharmaceutical industry that can be deployed in response to severe disruptions and pandemic-like crises. We also offer a resilience framework for the post-pandemic environment as a guideline for strategic decision-making.

Improvement of Industrial Maintenance Plans through Assistance-Driven Reliability-Centered Maintenance and Case-Based Reasoning Design

The present work builds on studies where the industrial market is currently characterized by a highly variable demand in terms of the quantities and flexibility of manufacturing or mass customization, which translates into a more demanding production context in terms of the continuous changes that are required in the production systems, the effect of which results in an increase in the fatigue of the machines that make up the production systems. However, current production systems tend to use highly communicative and sensorized cyber–physical systems; these characteristics can be used to integrate them into decision-assisted systems to improve the availability of the industrial plant. The developed assisted system focuses on collecting and taking advantage of historical knowledge of industrial plant failures and breakdowns. By ideally integrating the reliability-centered maintenance (RCM) methodology and case-based reasoning (CBR) algorithms implemented in a Java application, it is possible to design maintenance plans that are adjusted to the real and changing operational context of any industrial plant. As a result, faster and more accurate decisions are made, because they are based on data. This article focuses on improving certain aspects of the developed assisted system by adding more value by incorporating fuzzy logic (FL) techniques. The aim is to improve the way of entering information about risk factors and their relative importance by incorporating natural language instead of a numerical score, resulting in increased precision in the calculation of the risk priority number (RPN) of the new cases that are incorporated into the assisted system. On the other hand, an attempt has been made to correct two of the main inherent and recognized weaknesses in the classic RPN calculation method by implementing an appropriate mix of fuzzy logic techniques.

Aviation industry is a basic element of the national economy and the system of international economic relations

It is proved that air transport is a driving force and a means of formation and development of the domestic market, which ensures the process of integration of the national economy into the world economy. The role and place of the air transport industry in Ukraine's unified transport system are determined, and the advantages and disadvantages of air transport and its role in ensuring international integration processes are analyzed. The essential advantages of air transport that distinguish it from other modes of transport (sea and land) are the highest speed and time savings for transportation; high reliability and safety of transportation; geographical accessibility of departure and arrival points for passengers and cargo, predictability of transportation time, the level of autonomy of passenger service and cargo handling processes; lower costs for cargo insurance. The main disadvantage of air transportation is the high cost of transportation services. At the same time, an increase in transportation distance leads to its reduction, and in some cases, the cost of air transportation may even be lower than other types of transportation due to the grouping of small cargo. This is actively used in modern supply chains. Endogenous and exogenous factors affecting the pricing process in the air transportation industry are investigated. The systemic and non-systemic factors of influence are described, and the need for diversification as the most effective tool for reducing risk in global instability is determined. The author outlines the most critical tasks for developing the Ukrainian air transportation market, which are actualized with its unblocking. The specifics of the elements of the strategic development model, which should be considered in strategic planning in the air transport industry, are scientifically substantiated. Keywords: air transport industry, air transportation, strategic development, international integration, national economy, international economic relations.

Statistical Machine Learning Model for Distributed Energy Planning in Industrial Park

With the advancement of industrial modernization, industrial parks have become the main body of new energy production and consumption. However, due to the large demand for energy in industrial agglomeration, the way of energy utilization is changing to sustainable. The direct connection of distributed energy resources in industrial parks, including photovoltaic power generation systems, has an important impact on its planning and operation. Furthermore, weather scenarios can have an impact on distributed photovoltaic generation, and the uncertainty in photovoltaic power output will, in turn, affect the uncertainty in industrial park planning. Therefore, this paper aims to address the issues of inaccurate prediction of distributed electricity generation during the planning period and the non-uniform distribution of energy resources such as electricity, heating, and cooling. This is achieved through the application of statistical machine learning. The paper intends to incorporate the ideas of statistical machine learning into the model for industrial park distributed energy random opportunity-constrained planning, aiming to resolve the problems of non-uniform distribution of distributed energy sources within the park, along with uncertainty in their outputs and high overall investment costs. The model takes the planning, construction and operating costs of the industrial park as the objective function, uses the lost load cost to ensure the safety of the industrial park, and uses the Chebyshev inequality probability to limit the output characteristics of distributed energy equipment. In terms of operation, the planning period is subdivided into heating period, cooling period and transition period, and the balance of electricity, heat and cold is considered. Finally, an actual example of an industrial park is used to verify the effectiveness of this method. Experimental validation shows that this approach can ensure safety requirements in industrial parks during the heating season, cooling season, and transitional periods by flexibly adjusting the confidence threshold. Simultaneously, it delivers significant economic benefits.

Tourism talent management: empirical research among tourism academics, practitioners, and government officials

ABSTRACT This study explores tourism talent management by integrating the perspectives of tourism academics, senior industry practitioners, and government officials. Semi-structured interviews were conducted with 10 academics specializing in tourism research and education, 10 senior tourism practitioners, and 5 government officials responsible for tourism industry planning and development in China. Through thematic analysis, this research provides fresh insight into different stakeholders’ perceptions of talent management in China’s tourism industry. Tourism academics indicated the importance of tourism as a research area and expressed their strong sense of social responsibility as tourism scholars. Tourism practitioners emphasized the need to move from being service providers to being tourism talent pool incubators. They also recognized the importance of corporate social responsibility for both tourism talents’ individual excellence and the sustainable development of China’s tourism industry. Tourism government officials acknowledged the government’s role in fostering corporate social responsibility through talent management in China’s tourism industry.

Sustainability of renewable energy in China: Enhanced strategic investment and displaced R&D expenditure

Tackling climate change renders advancing the renewable energy sector exceptionally crucial. As the industry leader, China has achieved remarkable success but also faced concerns that necessitates re-examining the sustainability of its development. Focusing on the strategic development planning of renewable energy enterprises, the present study delves into the phenomenon of strategic investment crowding out R&D expenditure.11The acronym “R&D" in the text stands for research and development. The analysis results of data structure alleviate the concerns about the possibility of discretionary decisions due to the unique policy and financing background of Chinese enterprises. Furthermore, leveraging the industry's unique instrumental variable and considering the sequential allocation of funds, the underlying mechanism behind the crowding-out effect is elucidated. The analysis and policy recommendations presented herein serve as a valuable reference for governments to scientifically and prudently formulate development plans for the renewable energy industry.

Aggregate production planning and energy supply management in steel industry with an onsite energy generation system: A multi-objective robust optimization model

This paper introduces an optimization model developed for the steel industry, the most energy-intensive industrial sector in many countries. New methodologies for both aggregate production planning and energy supply management are developed to maximize profit while minimizing energy consumption and greenhouse gas (GHG) emissions. Moreover, uncertainties are considered, and robustly optimized strategies are identified for a grid-connected steel production company with an onsite energy generation system. The performance of the developed model is evaluated through extensive computational experiments. The results show how energy tariffs affect integrated production planning. Also, the selected production planning time depends on several factors such as electricity, labor, and maintenance costs, and pending orders that need to be fulfilled. The onsite power plant produces electricity when the electricity production cost is lower than the price of electricity provided by the connected grid. Furthermore, the company’s environmental policies influence the choice of energy generation technology. However, implementing an onsite energy generation is a viable policy that benefits both profitability and pollutant reduction. This study helps decision-making in production planning and energy supply management in the iron and steel industry (which can be applied to other industries too), ensuring environmental improvement at the lowest costs.

Statistical Data Analysis in Emergency Management Elements of Indian State of Tamil Nadu Manufacturing Industries Utilising LPG

Introduction: Process safety is becoming more difficult as requirements increase. When balancing quality, volume, and safety on a budget, major accidents are unavoidable. The inability of organizations to absorb unwelcome and unanticipated disruptions is a major contributor to complacency and a decline in safety attitudes. The inability of industries to interpret standards impedes self-regulation. Methods: A total of 50 emergency management element questionnaires were developed to evaluate the state of an industry. These questionnaires were categorized as first aid, personal protective equipment (PPE), communication systems used in plants, safety education and training, occupational hygiene and health, management systems on accident reporting, safety and health, analysis and investigation, and procedures and protocols. From 32 LPG industries in Tamil Nadu districts, information for all 50 questions was gathered and the statistical tool "Analysis ToolPak" in Excel 2010 was used to analyze the data. Results: This study conducted a comprehensive statistical analysis of various aspects of industrial on-site emergency planning. The questionnaire's reliability was determined using Cronbach's alpha test. Utilizing descriptive statistics, t-tests for means, one-way ANOVA, and histograms, statistical evaluation was conducted. Encouragingly, all the results were favorable, providing compelling evidence that the LPG-utilizing industries in various regions of Tamil Nadu, India, are exceeding safety requirements. Conclusion: In conclusion, the findings suggest that improved legal frameworks, laws, and implementation of EME in communication systems can greatly enhance the development of safety in plants, fire protection, and emergency health services. These measures can significantly reduce the occurrence of accidents and improve the response time to emergencies, ultimately saving lives and reducing property damage.

Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy?

In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers.

Revenge outbound travel in the post-pandemic era: evidence from an extended TPB model

ABSTRACT Revenge travel has become a hot topic in the global tourism industry since the lifting of pandemic-based travel restrictions. This research aims to thoroughly investigate the factors motivating revenge outbound tourism. Using an extended theory of planned behavior (TPB) model, this research considers factors such as social and environmental information, psychological stimulation, and rational control. The results of the structural equation model (SEM) analysis of questionnaire responses from 561 Chinese residents confirm that environmental and social information and psychological stimulation jointly influence travelers’ attitudes toward revenge tourism, along with perceived behavioral control (PBC). These factors significantly facilitate intention and, in turn, promote retaliatory tourism behavior. The results of this research provide valuable contributions to the theoretical analysis of revenge tourism and the TPB model. This study also offers practical guidance on the recovery and growth planning of the tourism industry after the relaxation of travel restrictions.

Convergence or Divergence: A Computational Text Analysis of Stakeholder Concerns on Manufacturing Upgrading in China

Stakeholder participation is essential to the reasonable design and smooth implementation of industrial policies. Discourse analysis can be employed as a valuable methodology to decode stakeholder (dis)agreements. Following stakeholder theory, we examine four stakeholder groups, namely the government, enterprises, media, and academia, to analyze the public focus of industrial upgrading in China. We adopt a computational text analysis approach (keyword frequency calculation, word collocation analysis, and theme identification) to understand the divergent and convergent stakeholders’ concerns toward manufacturing upgrading in China from 2015 to 2020, which is widely considered as a manufacturing upgrading policy formulation stage. Our results show that stakeholders mainly focus on innovation capability and digital transformation for China's manufacturing upgrading. There, industrial planning is the major issue for government and academia. Contradistinctively, enterprises’ main concern is servitization. Enterprise internationalization is more frequently mentioned than manufacturing upgrading in the media industry. Policymakers should engage in various tactics to make the policies endorsed by other stakeholders. Most importantly, the government should fully integrate the views of entrepreneurs in the policy initiation stage. We provide practical implications for the Chinese government to implement better the Made in China 2025 plan.

Forecasting the annual sales of new energy vehicles in China based on the DGM (1, N) model

New energy vehicles are of great significance to China’s economy and ecological environment. Selected from 2012 to 2022 new energy vehicles annual sales and 19 related factors data, gray correlation analysis, select the gray correlation of large charging pile ownership, new energy vehicles related enterprise registration, power battery loading, a total of three factors as a variable DGM (1,3) model, calculation parameters, and the validity of the model, the results proved that the model prediction credibility and prediction accuracy is high, the relative error, reasonable expectations for the future policy, industrial planning, investment strategy, etc.

Edible coating, food-contact surface coating, and nanosensor for biofilm mitigation plans in food industry

Edible coating, food-contact surface coating, and nanosensor for biofilm mitigation plans in food industry

COMPARATIVE ANALYSIS OF THE FINANCIAL PERFORMANCE OF THE BANKS

In the dynamic world of finance, banks play a pivotal role in economic stability and growth, and analysing their financial performance is crucial for investors, regulators, and policymakers. This study offers a comparative analysis of the financial performance of banks over five years (2018-19 to 2022- 23), underscoring their importance in the financial ecosystem. It evaluates profitability, asset quality, and liquidity, key indicators of a bank's health and efficiency. Profitability is assessed through Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), while Asset Quality is gauged using Gross and Net Non-Performing Assets (NPA) ratios, and the Credit Deposit Ratio. Liquidity is examined via Liquid Assets to Total Assets and Liquid Assets to Demand Deposits ratios. Employing ratio analysis and a correlation matrix, the research uncovers trends and interrelationships in these financial metrics. The findings reveal an overall enhancement in profitability and asset quality, with notable increases in ROA and ROE and a decline in NPA ratios. Liquidity analysis shows varying trends. This study provides critical insights into the banking sector's financial health, highlighting areas of strength and potential risk, essential for informed decision-making and strategic planning in the financial industry.