Volume 13 (2022): Issue 3 (October 2022)

This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning.

Background: In making investment decisions, asset risk and return are two crucial criteria on which investors base their decision.

Objectives: This paper provides risk and return analysis and compares different traditional and alternative investments with special emphasis on the COVID-19 crisis. Assets included in the analysis are stocks, bonds, commodities, real estate, foreign exchange, cryptocurrencies, renewable energy sources, gold, and oil.

Methods/Approach: The risk measures of standard deviation, Value at Risk (VaR), Conditional Value at Risk (CVaR), and Sharpe ratio are used to compare the representatives of each asset class.

Results: The crisis had the highest impact on the risk of crude oil, renewable energy sources, real estate, and stocks, a slightly lower impact on the risk of commodities and gold, and a very low impact on the risk of bonds, foreign exchange, and cryptocurrencies. The order of assets regarding earning potential during the crisis, compared to the period before the crisis, changed significantly for commodities in a positive way and for gold and bonds in a negative way.

Conclusions: This research shows that stocks won against all other assets, including gold and cryptocurrencies, during the COVID-19 crisis. The good features of a new alternative investment – renewable energy sources – with excellent earning potential are shown.

Background: Farm-level models have become an important tool for agricultural economists as there is a growing demand for microsimulation and analysis of farms at the individual level.

Objectives: In this paper, we present a mathematical model with the main objective of assessing the effectiveness of production and various possible strategies for agricultural holdings by reducing risks. At the same time, we were also interested in the environmental impacts of such strategies. The latter was measured using the indicator of GHG emissions.

Methods/Approach: The model applied is based on linear programming and upgraded with QRP for risk analysis. The approach was tested on medium size mixed agricultural holding, which often faces challenges in light of the structural changes taking place in Slovenia.

Results: The results suggest that such a farm could improve financial results with a more efficient risk management strategy. With a slightly modified production plan, the expected gross margin (EGM) can be increased by up to 10% at more or less the same risk. However, if the farmer is willing to diversify the production plan and take a higher risk (+23%), the farm’s EGM could increase by up to 18%. This kind of change in the production plan would also generate 17% more GHG emissions in total, calculated as kg equivalent of CO2 at the farm level, as both BL and C scenarios have the same relative ratio at 3.12 GHG CO2 eq. /EUR.

Conclusions: Through this research, we concluded that diversification has a positive potential on a mixed farm, and the farm could achieve better financial results. With flexibility in management, the farmer could also achieve higher risk management efficiency and better farm results.

Background: Recent scientific research explains corporate social responsibility as an economic activity. This paper interprets social responsibility as a means of power to increase firms’ market share in a duopoly.

Objectives: This paper analyses the duopoly model in which firms decide on optimal social investments and production in two phases. The basic research question is how the significance of the conflict affects social investments, market shares, production quantities, profits, and social welfare.

Methods / Approach: Conflict technology is described by contest success functions determining market shares. Game theory, optimization, and comparative statics are used in the analysis.

Results: The conditions of equilibrium existence and its characteristics are described. Conflict adversely affects the profit of the inefficient firm while it favourably affects social welfare. Conflict’s impact on an efficient firm’s profit depends on the marginal cost difference.

Conclusions: If there is no significant cost difference, it is more favourable for firms not to invest in socially responsible activities by agreement, which hurts social welfare. When marginal cost difference is significant, corporate social responsibility increases an efficient firm’s profit, positively impacting social welfare.

Background: Ageing and shrinking of the European population influence the shrinking of central places and the hinterland of cities in a spatial structure. Migration also influences the shrinking or growing of spatial units. Various factors influence migration and, thus, spatial units’ demographic, social and economic stability. The age structure of citizens in a spatial unit may change not only due to population ageing but also because these factors influence the migration flows of different cohorts differently, which has not been studied so far.

Objectives: We used data on internal migration between Slovenian municipalities in 2018 and 2019 to develop a cohort-based spatial interaction model to estimate future inter-municipal migration.

Approach: In a spatial interaction model, we analyzed differences in the attractiveness and stickiness of municipalities for different cohorts, focusing on those over 65 who may wish to prolong their working status. We also tried to answer the question of how to mitigate shrinkage processes in spatial units by investigating the potential to contribute to the social value of communities.

Results: The study’s results show that the 65+ cohorts do not have the same preferences regarding the attractiveness and stickiness factors as younger migrants.

Conclusions: The results of our study could contribute to better decisions at the national, regional, and/or local level when designing strategies for regional, urban, and/or rural development, exploring the best solutions for long-term care, and investing in appropriate networks, or considering the revitalization of rural municipalities.

Background: Regional Development (RD) allows countries to balance regional differences by providing economic and social benefits to communities. This research highlights the importance of logistics activities to regional social development, and a framework to assess these connections is proposed.

Objectives: How to boost regional socioeconomic development through logistics.

Methods/Approach: The contributions of logistics to socioeconomic development are analysed based on the previous research, and the case of the Alto Minho (AM) region in Portugal was used to illustrate the connection between logistics and regional development. Results showed that logistics had created jobs, increased company turnover and exports, and increased GDP growth in several regions. For the AM region, results indicate that many companies are operating in this area, contributing to supporting municipalities to reduce regional disparities.

Conclusions: A framework for assessing regional logistics performance is proposed together with several logistics performance indicators. This approach is essential for future developments integrating logistics into socioeconomic development.

Background: Circular Economy has been considered one of the most powerful principles of modern society. The concerns about increasing resource consumption have forced governments and companies to look at the circular models as a hedge against resource scarcity and an engine for innovation and growth.

Objectives: This research aims to bring together the Circular Economy and the consumer’s perspective to perceive the impact of its choices on CE initiatives.

Methods/Approach: A survey was conducted considering the consumer’s engagement with the circular economy concepts.

Results: The results pointed out the awareness and willingness of consumers for the transition from the linear to the circular production model, providing an added value to consumers for reducing environmental impacts.

Conclusions: Consumers’ behaviour can have a forefront role in building a guide with best practices to be considered by companies, designers, and consumers on implementing initiatives in the field of Circular Economy.

Background: The Plan-do-check-act (PDCA) cycle methodology for a continuous improvement project implementation aims for the internal logistics upgrade, which is especially important in the industrial context of a component manufacturing company for the automotive sector.

Objectives: The goal is to quantify the gains from waste reduction based on the usage of the PDCA cycle as a tool in the implementation and optimisation of a milk run in an assembly line of a company in the automotive sector by determining the optimal cycle time of supply and the standardisation of the logistic supply process and the materials’ flow.

Methods/Approach: The research was conducted through observation and data collection in loco, involving two main phases: planning and implementation. According to the phases of the PDCA cycle, the process was analysed, and tools such as the SIPOC matrix, process stratification, 5S, and visual management were implemented.

Results: Using Lean tools, it was possible to reduce waste by establishing concise flows and defining a supply pattern, which resulted in a reduction of movements. The transportation waste was reduced by fixing the position of more than half of the materials in the logistic trailers. The developed Excel simulator provided the logistic train’s optimal cycle time.

Conclusions: The assembly line supplied by milk-run was fundamental to highlight a range of improvements in the process of internal supply, such as better integration of stock management systems, greater application of quality, or the adoption of better communication systems between the different areas and employees.

Background: This paper focuses on activities related to Customer Orders Management withing an auto components plant in the Automotive Industry. The main challenge was highlighted: customers don’t always regard the flexibility rules agreed with the company. Hence, planners must decide if variation in ordered quantity can be accepted in the forecast period or if adjusting is necessary.

Objectives: The purpose was not only to streamline the decision-making process in the planning team but also to provide essential tools for the execution of their daily tasks – a visual and interactive dashboard to assess whether variations in customer orders were within the limits agreed with the company.

Methods/Approach: Following Lean information management and business intelligence principles, a thorough process analysis was carried out, centralized and standardized reports were created that served as databases, and the dashboard was developed.

Results: The proposed tool allowed reductions from 3,5h per week, spent mainly on collecting data, calculating variations, and selecting and adjusting the flexibility limits, to 0,2h a day per planner.

Conclusions: Besides streamlining planners’ daily activities, main contributions regard the promotion of digital transformation, data-driven decision-making, and an automated record of customer order variations that could easily be adapted to suppliers.

Background: E-commerce has emerged as a good response to the pandemic of COVID-19. However, the costs of providing a service, which includes a driver and a vehicle, in a regular vehicle that can transport goods that need positive cold (0° to 5°C) are very high.

Objectives: This paper aims to investigate how a big Portuguese retailer company can reduce its dependence on refrigerated vehicles, simplifying operations and reducing the costs of transporting positive and negative cold food.

Methods/Approach: This research was carried out in a food retailer Portuguese company, more precisely in a Dark Store dedicated to the online channel. The study was developed based on the AS-IS/TO-BE process analysis methodology, starting with the analysis of the current situation, giving rise to the so-called AS-IS model.

Results: It was possible to reduce costs associated with transporting positive cold goods. As a result, there are 30% fewer costs associated with order transportation. With an additional 10% in space optimization with the gain of space within the galley of each vehicle.

Conclusions: The costs of transporting positive and negative cold foods were decreased, and substituting vehicles with room temperature transport reduced the need for refrigerated vehicles.

Background: This paper presents a machine learning approach to forecast Croatia’s international bilateral trade.

Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia.

Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE).

Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms.

Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows.