Enterprise Decision Support Research Articles

Impact of Management Information Systems on Enterprise Decision Support

This thesis examines the impact of management information systems (MIS) on decision support in business, focusing on the role of MIS in improving data availability, enhancing data analysis capabilities, improving decision-making efficiency and supporting collaborative decision-making. Through real-world case studies of large manufacturing companies and retail businesses, the paper demonstrates how MIS implementation can significantly improve productivity, inventory management, and customer satisfaction. The manufacturing company in Case 1 improved production line efficiency and inventory management, reduced inventory costs, and enhanced decision support by integrating an MIS platform. The retail enterprise in Case 2 achieved sales growth, inventory optimisation and customer satisfaction through real-time data analytics and CRM systems. The paper concludes that the application of MIS in modern enterprises can effectively improve the quality of decision-making and operational efficiency, and provide important support for enterprises in complex market environments. Through an in-depth analysis of MIS functions and their impact, the paper provides useful references and lessons for enterprises in the implementation and optimisation of decision support systems.

Design and Implementation of the Employment Management Decision Support System based on Machine Learning

To address the challenges of current college student employment management, this study designed and implemented a machine learning-based decision support system for college student employment management. The system collects and analyzes multidimensional data, uses machine learning algorithms for prediction and matching, provides personalized employment guidance for students, and provides decision support for universities and enterprises. The research results indicate that the system can effectively improve the efficiency and accuracy of employment guidance, promote school-enterprise cooperation, and achieve a win-win situation for all parties.

Strategy for sustainable supply chain transformation: a resource orchestration perspective

PurposeWith the popularization of sustainable concepts, how to transform into a sustainable supply chain has received widespread attention in practice. Under this circumstance, this paper aims to propose a theoretical framework of sustainable supply chain transformation (SSCT) from a perspective of resource identification and utilization, investigates resources/capabilities that can be used to promote SSCT and explores how to use resources/capabilities to achieve SSCT effectively.Design/methodology/approachAn inductive multi-case analysis is applied to this paper. Four state-owned/non-state-owned enterprises from the manufacturing sector are selected as the research objectives, which are all leaders in the industry based on the 2022 China TOP 500 Enterprises Ranking. Meanwhile, to guarantee the diversities of enterprises, the four selected enterprises are respectively positioned in upstream and downstream of the supply chain.FindingsA theoretical framework of SSCT is proposed with following research findings: (1) Technology resources, facilities and equipment resources, and business process reengineering capability are the key resources/capabilities to promote SSCT. (2) From the supply chain structure perspective, there exists a leader-participant structure in SSCT. The enterprise with dominant resources/capabilities should actively transfer to a SSCT leader. From the supply chain function perspective, specific sustainability assessment indicators and special teams are two necessary settings for SSCT. From the supply chain lifecycle perspective, SSCT should be promoted in a phased manner and dynamically adjusted in each stage. (3) Digital transformation degree and enterprise ownership play a moderating role in the implementation of strategies.Originality/valueThis paper proposes a conceptual framework of SSCT based on the resource orchestration perspective, which provides decision support for enterprises in practice.

Implications of risk-averse manufacturer on coordination of green supply chain with mixed carbon policies

PurposeThis paper intends to address the decision-making and coordination of green supply chain (GSC) considering risk-averse manufacturers under mixed carbon policy.Design/methodology/approachThis paper focuses on a GSC consisting of a manufacturer and a retailer, in which the manufacturer is risk-averse (R-A). This paper employs Stackelberg game theory and mean variance analysis to assess the pricing decision-making process under various scenarios. Furthermore, cost-sharing contracts are introduced to coordinate the GSC.FindingsThe research results suggest that the green level of the product and the profit of the GSC under a centralized scenario are higher than those under a decentralized scenario, while the retail price is lower. Under the decentralized scenario, the green level of product, wholesale price and manufacturer’s profit in the R-A scenario are lower than the values in the risk-neutrality scenario, while retailer's profit is higher. In addition, when a cost-sharing contract is utilized for coordination in the GSC, it can lead to Pareto improvement, regardless of whether the manufacturer makes risk-neutrality or R-A decisions.Originality/valueThis research provides a deeper understanding of GSC decision-making and coordination strategy under mixed carbon policy with consideration of R-A from a theoretical perspective and provides decision support for enterprises to choose strategies in practice.

From data to design: AI practice in design decisions

Abstract Facing the increasingly fierce market competition, enterprises need to make correct marketing decisions. Based on the decision support system, cloud server, and SOA framework, the article establishes an enterprise intelligent marketing decision-making system by combining the TCP/IP communication protocol and designing a data warehouse to store the marketing decision-making data. The Apriori algorithm in association rules is used to generate marketing rules to assist marketing decision-making, the marketing data is classified using the random forest algorithm, and the sparrow search algorithm optimizes the parameters of the random forest model. In practice, simulation experiments were designed to analyze the performance of the random forest model and the marketing decision-making system, and they were applied to the marketing decision-making example of the e-commerce platform. It was found that the AUC value obtained when the number of binary trees of the random forest model was 75 was 0.836, and the number of conflicts per second of the system was only 0.41 when the number of concurrent users was 5*104. The use of association rules resulted in eight marketing decisions, with the highest probability of user purchase prediction reaching 98.54%. Based on the intelligent marketing decision system, it can help decision-makers better analyze market changes and provide decision support for enterprises to develop scientific and reasonable marketing strategies.

Power battery third-party reverse logistics provider selection: Fuzzy evidential reasoning

Massive power batteries (PBs) are crucial to new energy vehicle enterprises. Due to Extended Producer Responsibility (EPR), the third-party reverse logistics provider selection(3PRLPs) process has become an important decision to save cost. This paper uses an innovative combination of qualitative analysis and quantitative data integration to address the PB 3PRLPs problem by using Failure Modes and Effects Analysis (FMEA) and fuzzy evidential reasoning (FER). Firstly, the possible failures and potential effects in the PB 3PRLPs are identified by the FMEA to determine criteria and importance grades. Subsequently, AHP is utilized to calculate the criteria weight based on the importance of grades. FER is creatively applied to address the intersection of assessment grades and allocate the belief degree (BD) of the interaction to fuse heterogeneous data. Additionally, sensitivity analysis is done to look into the stability of the sequencing. Compared with other methods, the proposed method not only solves the subjectivity of AHP weighting but also manipulates probabilistic and fuzzy uncertainties for multi-criteria decision-making (MCDM). This method is useful in quantitatively analyzing the 3PRLPs problem and in providing auxiliary decision support for enterprises.

To ally or not to ally: Enterprises' preferences for technology alliance modes in transnational cooperation

AbstractWhen entering a foreign market, transnational enterprises often conduct research and development (R&D) activities to develop patent technologies and then produce products to meet the technology standard of the target market. Technology alliance is an efficient way to enhance the enterprise's capability of R&D. We assume that the R&D process is uncertain and then construct four models based on different alliance modes. We further study the impacts of different factors on enterprises' expected profits and then investigate enterprises' preferences for alliance modes. Our study can provide decision support for transnational enterprises going abroad and enterprises in foreign markets on their choices of technology alliance modes.

Manufacturer encroachment in the presence of the store-brand

PurposeThis paper aims to identify the conditions under which encroachment is a viable strategy for a manufacturer to gain competitive advantage and achieve higher profitability in the presence of the store-brand.Design/methodology/approachThis paper proposes game-theoretic models in a two-echelon supply chain consisting of a manufacturer (him) and a retailer (her), in which he distributes his national brand through the retailer, and endogenously determines whether to establish a new direct sales channel to sell the national brand when the retailer introduces her store-brand.FindingsAnalytical results show that the bar for the manufacturer to encroach the end market in the presence of the store-brand is always higher than that for him to encroach in the absence of the store-brand. Although incurring channel competition, encroaching with the national brand in the presence of the retailer's store-brand can lead to either a win-lose or win-win result for the manufacturer and the retailer. Numerical studies claim that, higher brand substitution can push down the retailer's enthusiasm to introduce her new brand. Counterintuitively, when the retailer introduces her store-brand, higher brand substitution does not necessarily push up the manufacturer's enthusiasm to respond with national-brand encroachment. When consumer preferences for the two brands are heterogeneous, a higher consumer preference for the retailer's store-brand results in the retailer's higher enthusiasm to introduce her store-brand and the manufacturer's lower enthusiasm to encroach with his national brand.Originality/valueThis study can help researchers to better understand the retailer's store-brand introduction, manufacturer encroachment and their interaction theoretically, and further provide decision support for enterprises to choose brand and channel strategies in practice.

Digital Transformation of Production Tools: Industrial Robots Transform Inventory Management in Manufacturing

Industrial transformation characterized by informatization, digitization and intelligence is advancing in depth, and intelligent technologies are being used on the ground, and the results of digital transformation in the production field, represented by industrial robots, have been widely integrated into economic production activities, so it is necessary to study the economic consequences of them. In this research, the influence of using industrial robots on the inventory management capabilities of Chinese manufacturing enterprises is investigated by using data related to inventory management of listed Chinese manufacturing enterprises from 2011-2019, and an index of industrial robot penetration of Chinese manufacturing enterprises is constructed. The empirical results show that industrial robots significantly improve the inventory control ability of enterprises. Controlling for factors such as firm characteristics, a 1% increase in the penetration rate of industrial robots in manufacturing firms was associated with a 10.66-day reduction in inventory turnover days. A test of heterogeneity revealed that firms with low financing constraints and technology-intensive firms improved their inventory control capabilities more. The study verifies the effect of industrial robots on the inventory control capability of enterprises and provides decision support for enterprises to carry out digital transformation in the production area.

Towards Public Opinion Digital Twin: A Conceptual Prototype

This paper proposes a novel modeling concept, the “public opinion digital twin,” for public opinion analysis. The public opinion digital twin can be regarded as an experimental sandbox for social science. By digitalizing public data acquired from cyberspace into digital models, the modeling enables practical simulation, data analytics, scenario reflection, and decision support in a digital space with fine controllability, so that all possible evolutions of the research target can be analyzed. By simply inputting or filtering variables, any number of future scenarios are simulated, the effect models of each strategy for coping with public opinion are presented, and the optimized solution can be derived from continuous deep learning. If a robust digital twin is established and the required digital replicas are constantly updated, the system can perform risk assessments and trend predictions for social events. In this case, public opinion information can provide intelligent decision support for governments or enterprises and significantly facilitate social loss aversion, which will greatly advance the revolution in production, dissemination, and guidance.

Integrated Optimization of Order Allocation and Last-Mile Multi-Temperature Joint Distribution for Fresh Agriproduct Community Retail

Community retail is an important research issue in the field of fresh agriproduct e-commerce. This paper focuses on the problem of last-mile multi-temperature joint distribution (MTJD), which combines time coupling, order allocation, and vehicle scheduling. Firstly, according to the temperature of a refrigerated truck in multi-temperature zones, a split-order packing decision is proposed to integrate the different types of fresh agriproduct. Then, the order allocation strategy is incorporated into a comprehensive picking and distribution schedule, while taking into account the time-coupling of picking, distribution, and delivery time limit. To improve consumer satisfaction and reduce order fulfillment costs, an optimization model combining multi-item order allocation and vehicle scheduling is established, to determine the optimal order allocation scheme and distribution route. Finally, taking fresh agriproduct community retail in the Gulou District of Nanjing as an example, the effectiveness and feasibility of the model are illustrated. The numerical results of medium- to large-scale examples show that, compared with the variable neighborhood search algorithm (VNS) and genetic algorithm (GA), the mixed genetic algorithm (MGA) can save 29% of CPU time and 65% of iterations. This study considers the integrated optimization of multiple links, to provide scientific decision support for fresh agriproduct e-commerce enterprises.

Evolutionary Analysis of Supply Chain Integration Strategy on Chinese Steel-Producing Firms considering Policy Risk Cost Factor.

Despite a number of adverse factors, China's steel industry has maintained a rapid growth trend. China continues to consume two-thirds of the world's iron ore, the majority of which is imported. In this context, Chinese steel companies have begun to consider integrating their supply chains to increase efficiency and lower costs. However, the increasingly volatile international environment makes this an extremely risky proposition. As a result, the issue of how Chinese steel producers should participate in global supply chain integration has emerged as a critical research question that requires investigation. In this paper, we examine the supply chain integration problem using a typical China–Australia steel trade as an example. Specifically, we discuss in detail whether relevant firms should continue to promote supply chain integration in the Chinese–Australian steel industry, as well as the decision boundary of influence, using evolutionary game theory and policy risk cost factors. The empirical analysis demonstrates that policy risk has a range of effects on different types of steel firms. Even when international tensions are considered, smaller steel companies may retain a greater willingness to integrate their supply chains. Overall, the above findings can provide necessary decision support for enterprises to formulate supply chain management strategies.

Research on evaluation system of cross-border E-commerce platform based on the combined model

Abstract In the rational allocation of the cross-border e-commerce (CBEC) platform, adopting data analysis to evaluate it objectively and accurately can provide scientific decision support for enterprises. Therefore, in this paper, the evaluation indicators of the CBEC platform are selected according to the principles of science and typicality, including four first-level indicators, such as service quality, and 16 second-level indicators, such as function of data analysis. The weight of each index is calculated by analytic hierarchy process (AHP), and four e-commerce platforms, such as Amazon, are evaluated by fuzzy comprehensive evaluation. The score of the CBEC platform based on this evaluation system has a high matching degree with the comprehensive assessment of enterprise executives, which can provide scientific support for decision-making in enterprises.

RETRACTED: Supply chain operation evaluation and management decision by fuzzy cognitive map model

AbstractThe components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) > WL3 (0.6823) > WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) > WL2 (0.6949) > WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises.

Comparative Analysis and Strategy Research of Enterprises Dealing with Network Public Opinion Based on Text Mining

Enterprises need to pay attention to and track changes in online public opinion, and create a good public opinion environment for enterprise development. This study analyzes the successful experiences and deficiencies of enterprises in dealing with network public opinion, which is helpful to provide decision support for enterprises to respond to network public opinion. Two public opinion incidents of “Pinduoduo Employee's Sudden Death” and “Huolala Girl Jumps from the Car” are taken as the research objects. Based on text mining methods, the evolution of network public opinion, semantic network and sentiment trends are compared and analyzed. Through comparative analysis, it is found that Pinduoduo's comments focus on the corporate institutions and labor law, and revolve around multiple important nodes. The Huolala's comments focus on the truth and rectification of the event, and only revolve around two important nodes. The proportion of negative emotions in the early stage of the “Huolala girl jumping from the car” is much higher than that of the “Pinduoduo employee sudden death”. However, Huolala did better than Pinduoduo in terms of coping strategies, which makes the proportion of negative emotions in the later stage lower and comments tend to be more rational. This study put forward corresponding strategies and suggestions for enterprises to effectively respond to the crisis of online public opinion. Firstly, network public opinion early warning mechanism need to be established to control from the source. Secondly, companies have to understand the direction of public opinion and grasp the focus. Thirdly, companies need to strengthen responsibility awareness and have courage to take responsibility. Fourthly, follow-up management system and propose corrective measures should be carried out.

BUSINESS PERFORMANCE AND FINANCIAL HEALTH ASSESSMENT THROUGH ARTIFICIAL INTELLIGENCE

Research background: Globalisation and the development of technology introduce new requirements for effective business management. Every business must constantly adapt to the environment, analyse and know its competitors and its customers’ requirements, and meet other stakeholders’ commitments. An unsuccessful business will go into liquidation. The intention of any business should be not only to avoid this situation, but to thrive and prosper and create value for its shareholders. Purpose of the paper: The aim of this study is to propose an appropriate tool for cluster analysis and determine the ability of a business to survive a potential financial distress. Methods: Details from financial statements of construction companies operating in the period 2015-2019 in the Czech Republic are analysed. Attention is mainly directed to items that represent the capital and asset structures of a company, liquid assets, and the ability to generate sales and profit. Artificial neural networks in the form of Kohonen networks are used for the purpose of cluster analysis. Financial analysis is used to examine the underlying dataset as well as for a detailed analysis of selected clusters, i.e. the contribution margin and ratio indicators. Findings & Value added: The basic analysis clearly shows that companies in liquidation attempt to reduce the value of inventories and engage additional foreign capital with a view to survival, while there is a certain solidarity between companies’ key persons. Cluster analysis using Kohonen networks is quite successful. The present methodology and approach can still be applied to the design of an enterprise decision support tool. Further research may study whether the representation of businesses in the different clusters will change over time, or whether the development of the construction industry can indeed be predicted based on an analysis of the leaders.

Factors Affecting Consumers' Online Choice Intention: A Study Based on Bayesian Network.

In China, the mature development of online retail channels provides consumers with multiple consumption choices, and the factors that affect whether consumers choose to search or purchase online are numerous and complex. In this context, this paper reports on experimental research regarding consumers’ willingness to choose channels based on the two-stage decision-making theory. Using structural equation modeling, the factors influencing consumers’ online search intention and purchase willingness and the relationship between them are studied. In particular, the perceived benefits, channel trust, and channel transfer costs are explored. Furthermore, a Bayesian network is used in order to analyze the degree of influence of each factor quantitatively. It is found that online trust is an important factor affecting consumers’ online search intention, and the most important factor for consumers’ online purchase intention is their perceived benefits of online shopping. At the same time, there is a positive relationship between online search intention and purchase intention. This study can provide management decision support for online retail enterprises and help to promote the healthy development of online shopping.

Research on Location and Layout of Electric Vehicle Charging Facilities Based on Power System Dispatch

In order to enable the limited charging stations in the road network to meet as many demands as possible, and to give full play to the advantages of electric vehicles in changing the energy consumption structure and reducing greenhouse gases, a dualobjective charging station optimization problem is proposed. The paper uses the shortest path and the next-shortest path to describe the traffic flow factors in the traffic network. The goal is to maximize the traffic flow intercepted by the charging station, minimize the investment cost of the charging station and minimize the node voltage offset, and establish electric vehicle charging Station multi-objective planning model. The results prove the effectiveness of the proposed method and provide decision support for enterprises to scientifically and reasonably select the location of charging stations.

An empirical study on artificial intelligence technology based on big data to assist enterprise management decision

With the continuous development of China's economy, the level of science and technology has been improved to a great extent. The advent of the era of Internet and cloud computing has brought a major change to China. However, with the advent of the era of big data, a bigger technological change is coming. The arrival of the era of big data has brought a certain impact on China's enterprise management and decision-making, and put forward higher requirements for China's enterprise management and decision-making. Therefore, enterprises need to constantly strive to improve themselves so as to better adapt to the era of big data. In order to keep pace with the development of The Times, major companies and enterprises need to constantly change their internal management methods in order to achieve sustainable and stable development in their own fields and make their management decisions smoother. Among them, optimization and reform of the application of big data are particularly important. Starting from the characteristics of big data and its role in enterprise management decisions, this paper analyzes the current situation of big data management within enterprises and discusses the influence of big data on enterprise management decisions from five aspects, namely, environment, data, participants, organization and technology. And this paper analyzes the construction method and design idea of enterprise decision support system based on artificial intelligence.

Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach

In the era of circular economies, governments and consumers are increasingly aware of environmental protection, which encourages enterprises to devote more attention to reverse logistics (RL). However, the limited resources and technical limitations of most manufacturing companies have motivated them to outsource their RL activities to professional third-party RL providers (3PRLPs). Optimal 3PRLP selection is instrumentally valuable in RL outsourcing practices because it has the potential to increase enterprises’ economic profitability and to improve their long-term development. Generally, 3PRLP selection is treated as a multiple-attribute decision-making (MADM) problem. To this end, this paper aims to build a multi-perspective MADM (MPMADM) framework to offer systematic decision support for enterprises to select the optimal 3PRLPs. Attribute assessments in the proposed framework take the form of generalized comparative linguistic expressions (GCLEs), which can be transformed into hesitant fuzzy linguistic term set (HFLTS) possibility distributions with semantic analysis in order to enhance information quality and reliability. Expert weights are then assigned in the use of an optimization model based on the correlation consensus measurement. Afterwards, the two-stage aggregation paradigm for computing with HFLTS possibility distributions is used to gather assessments at expert and attribute levels to compile overall assessments of each alternative 3PRLP. Compared with existing studies, our proposal considers environmental and social sustainability for attribute system establishment and introduces GCLEs for 3PRLP selection, which offer greater flexibility for experts to articulate their evaluations. In addition, the two-stage aggregation paradigm eliminates distortion and loss of information and provides decision makers with the capability to control the outcome’s precision. Moreover, the proposed expert weight determination approach is conducive to generating reliable weight vectors. Several illustrative examples, sensitivity analysis, and comparative analysis further demonstrate the flexibility and practicability of our proposal.