Applying SWOT for B2B Decisions, Extension to larger data with Machine Learning Regression

Abstract

Abstract In this research Akella Systems company, AS, wants to locate its operation in another company either Sreyas an institute or C1 a company. A Business to Business, B2B association decision was required. The Strengths, Weaknesses, Opportunities and Threats, SWOT, concerning defined fields are defined and points allotted for AS, Sreyas, and C1. SWOT is used individually for decision-making on one parameter. In this analysis, a relative SWOT of the company’s capabilities is used for decision-making of location. Further, each company is evaluated relative to the other by SWOT polynomial regression. Standard regression using Cramer’s rule was used. A Business unit to Business unit, B2B decision was taken by locating the Akella Systems company, within the Sreyas Institute after relatively evaluating the SWOT. Linear equation fit of the data was also obtained from MS Excel and fits perfectly with R2 = 1 with the regression model. The relative evaluation of the two companies can be inferred from the coefficients of the equation. The constant-coefficient gives the average difference of all SWOT parameter differences of the company and the coefficient of the linear variable gives the progressive influence of one company over the other. Sreyas college had an average more SWOT value of 6.12 compared to AS and AS had a progressive influence in the relationship over Sreyas, as the coefficient was 0.1845. Sreyas college was selected to be the partner by AS. The polynomial fit metric developed for relative SWOT can be used as a training set, for large ML data regression.