Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Evaluating the human resource related soft dimensions in green supply chain management implementation

Because of increased carbon emissions, environmental protection initiatives have gained significant attention at global level. One of the major initiatives taken by the industrial sector to minimize the negative environmental effect of the value chain activities is Green Supply Chain Management (GSCM). In industry, soft (human resource-related) dimensions influence the implementation of GSCM process greatly. In the literature, relatively less discussion is provided on assessing the significance of soft dimensions in efficient GSCM acceptance in industry. The present work is an attempt to construct a structural framework for assessing the significance of the soft dimensions in adopting GSCM concepts by taking a case of automotive company in India. A hybrid approach of Best Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is employed in this work. BWM is used to prioritize the GSCM oriented soft dimensions, and DEMATEL is employed to extract interrelationships among them. The result shows that ‘Top management commitment’, ‘Employee involvement’, ‘Organizational culture’ and ‘Teamwork’ are the highly prioritized causal soft dimensions in efficient GSCM adoption. This research work would help industry managers and practitioners to decide where to concentrate for GSCM concepts in context of soft dimensions for sustainable business development.     

On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting,Aggregation, and Robustness

Social Indicators Research - In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured...     

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.