Introduction
In any business, decision-making methods must be implemented at many different levels to ensure the quality and effectiveness of each step or change. Various tasks, from selecting an employee for the position of an HR specialist to large-scale acquisitions of other companies, affect the organization’s overall well-being to varying degrees. To optimize this activity, decision-making methods are used.
Decision-making approaches formalize goals and come to meaningful conclusions not only with the help of qualitative expert assessments but also by obtaining quantitative equivalents of priority. The latter is actively used not only in business but also in modeling, for example, the environment (Maier et al., 2019). This paper discusses one of these approaches – a weighted score matrix- and provides an example of its application in selecting the most suitable project according to various criteria and factors for changing an essential business process in one of the companies.
Weighted Score Matrix
The decision matrix method is a valuable tool for making various decisions. Its power is most pronounced when a person has to choose from a set of suitable alternatives and, at the same time, take into account several different factors. Input data as a set of determinants and alternatives is a necessary and sufficient condition for applying this approach. At the same time, current research on this theory includes promising areas such as fuzzy logic (Chen & Tsai, 2021). However, in addition to these data, it is necessary to give a roughly accurate assessment of each option for all variable factors. Each determinant has its weight, and this indicator is also given by assessing the conducting calculation and the person making the decision.
The selection of available alternatives and factors is carried out in the first stage of preparing this matrix. Even if the choice consists of only two options, adapting this technique is possible and can be effective in the qualitative ranking of the estimates of weights and alternatives. For consistency of calculations, it is recommended that the sum of the weights be equal to 100, which is the equivalent of percentage designations (Romanelli, 2023). However, this step is not required to implement this approach.
It is believed that the more categories of factors are defined for selection, the more accurate the result will be. A weighted score matrix (WSM) can consider the multicriteria of various tasks when choosing a solution while providing a simple calculation mechanism that does not require special software or hardware. All work can be done even on a sheet of paper with a pen and a calculator. The ease of use of the method determines its popularity in many areas of management decisions.
However, this approach cannot be considered ideal in every situation – often, a more profound statistical assessment is required to conclude. Scientific studies operate similarly, whereas WSM is not a meaningful and accurate method. This approach can claim scientific objectivity only when experimentally obtained or certified by some confirmation of estimates of the weights and values of alternatives.
The essence of WSM is multiplying the category’s weight by the value of the corresponding alternative and getting the sums for all given indicators for each decision. As a result, the highest value will be the highest priority choice. However, this method can show the best alternative and a complete ranking of all solutions from first to last. Such a comprehensive assessment can be helpful when choosing several functional solutions at once or analyzing the effectiveness of an already chosen one, among other options.
Results
The calculations for this work were made in the Microsoft Excel software package and are presented in Appendix A. The results showed that the best solution for changing the internal process in the company would be Project C, which scored the highest WSM score in all of the categories indicated. Project B was slightly worse than the chosen solution, while Project A lags far behind other alternatives.
It is possible to explain such results by the most significant categories of safety, risk, and costs, which, more than others, affect the final answer. Despite being the best solution, Project C is more at risk than the other two. Although the methodology suggests that this risk is mitigated by safety and cost benefits, company management should not lose sight of project B, which offers, for example, better profitability but more cost and less safety.
Project A should be rejected immediately because, despite the main advantage of minimal costs, it creates various risks, including in terms of safety, which is most important according to weight. Since all project estimates are equal, the competitiveness category does not contribute any defining information regarding the three alternatives. Less important opportunity and sustainability categories are the worst for Project B, which management should also consider when integrating this alternative and comparing it with Project C. The advantage of this approach is that with a valid template in the spreadsheets, decision-makers can change the weights or scores and see the instantly changed calculation of total scores.
Conclusion
This paper describes one of the decision-making methods – the weighted score matrix and gives an example of its implementation in practice. Given the advantages described in the ease of use of the approach and the visibility of the results, one should always be aware of the subjectivity of weights and estimates, but only if they have not been obtained from official data or experimentally. The case review showed that Project C was the best solution for changing the company’s internal process, but management should also include Project B, which scored slightly less than the leader. Project A should be rejected because it does not meet the requirements for the most significant categories.
References
Chen, S. M., & Tsai, K. Y. (2021). Multiattribute decision making based on new score function of interval-valued intuitionistic fuzzy values and normalized score matrices. Information Sciences, 575, 714-731. Web.
Maier, H. R., Razavi, S., Kapelan, Z., Matott, L. S., Kasprzyk, J., & Tolson, B. A. (2019). Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. Environmental Modelling & Software, 114, 195-213. Web.
Romanelli, M. (2023). The Weighted Scoring Model. Project Management Tools & Techniques. Web.
Appendix A
