https://orcid.org/0000-0001-6919-2029
Figures & data
Figure 1. Stylised MCDM criteria selection process.
This flowchart depicts a five-step criteria selection process for multicriteria decision making. It begins with ‘Clarifying objectives’, leading to ‘Criteria identification’. The next steps are ‘Criteria selection’ and ‘Selection validation’. The final step, ‘Feedback’, forms a feedback loop connecting back to both ‘Clarifying objectives’ and ‘Criteria identification’. Each step is represented by a distinct box, with arrows illustrating the sequential and interconnected nature of the process.
Table 1. Description of steps within the criteria selection process.
Figure 2. Example application of criteria selection process.
This flowchart presents a conceptual criteria selection process for multicriteria decision making, with added details on practical implementation. The process consists of five main steps, starting with ‘Clarifying objectives’ and then progressing to ‘Criteria identification’. The sequence continues with ‘Criteria selection’ and ‘Selection validation’. The final step, ‘Feedback’, creates a feedback loop that connects back to both ‘Clarifying objectives’ and ‘Criteria identification’. Each of these steps is represented as a separate box. Additionally, adjacent to each step, there are supplementary boxes that provide the purpose of the step and an example of its practical application. Arrows link the boxes to illustrate the process flow and the interrelationships between each step.
Figure 3. Process used to conduct the research, adapted from Ghadge et al. (Citation2022).
Flowchart depicting the research process, adapted from Ghadge et al. (2022). The process initiates with ‘Develop research questions’, followed by ‘Data sourcing’. This leads to the next stages of ‘Data extraction’ and ‘Data analysis and synthesis’. The flowchart is structured with each step enclosed in individual boxes. Chevron arrows connect these boxes, highlighting the sequential progression of the research stages. The diagram visually represents the methodical approach taken in the research, from question development to data analysis.
Figure 4. PRISMA data screening flow diagram.
This flowchart presents a PRISMA-based data screening process. It starts with ‘Primary literature search’, followed by ‘Abstract screen’. The process then progresses through ‘Full text review’, ‘Backward literature search’, and concludes with ‘Additional article selection’. Each step is represented in a distinct box. Arrows connect these boxes in a linear sequence, while additional arrows branching off from each step indicate the remaining number of articles after each stage. This visual arrangement effectively demonstrates the narrowing down of articles through the screening process, culminating in the final count of articles reviewed. The flowchart is a clear depiction of the systematic approach in literature screening and selection.
Figure 5. Comparison of publication trends for MCDM-based and non-MCDM-based inventory classification articles.
This column chart compares the publication frequency of inventory classification articles using multicriteria decision making models versus other approaches, from 1987 to 2020. The x-axis represents the publication year, while the y-axis indicates the number of articles published. The chart shows a noticeable trend: the use of multicriteria decision-making models in articles has been declining since 2016. In contrast, publications using other approaches have been on the rise since 2005. The chart visually encapsulates these trends, clearly differentiating between the two methodologies over the specified period.
Figure 6. Top ten publication outlets for MCDM-based inventory classification research.
This pie chart displays ten segments representing different publication outlets for articles on multicriteria inventory classification using multicriteria decision making models. The chart highlights four main outlets as having the highest proportion of published articles: the International Journal of Production Research, European Journal of Operational Research, Expert Systems with Applications, and Computers and Industrial Engineering. Each segment's size reflects the relative volume of publications in these outlets. The chart provides a visual representation of where most research in this field is published, indicating these four journals as key contributors.
Table 2. Case-based inventory classification literature included in the review.
Figure 7. Criteria identification and selection methods used in case-based MCDM-based inventory classification research by MCDM model classification.
This single figure comprises two separate column charts. The first chart at the top displays the number of articles reporting various criteria selection methods. In this chart, the highest column represents articles that do not specify any selection method. Directly beneath, the second chart illustrates the count of articles reporting different criteria identification methods, with the tallest column indicating the predominant use of internal experts for criteria identification. Both charts are organised in descending order based on article counts, clearly showing the frequency of different methods used in the articles for criteria selection and identification.
Table 3. Definitions of MCDM model classifications used in this review.
Table 4. MCDM-based non-inventory classification studies applying expert opinion and survey instruments for criteria validation.
Table 5. MCDM-based non-inventory classification studies applying expert opinion and interviews for criteria validation.
Table 6. Soft-OR approaches used for criteria validation in non-inventory classification MCDM-based studies
Data availability statement
Data sharing is not applicable to this article as no data were created or analysed in this study.