Development of a DSS using Stratified PPSWR to Estimate the Retail Sales and Market Share of a Company

Student Name: R. Ashwin Kumar

Monthly retail sales and market share of a company was proposed to be estimated by observing these variables for a sample of dealers of the company. Since the wholesale purchase patterns of the universe of dealers of the company was available, it was reasoned that exploiting this information in the sampling design would be more beneficial, instead of a simple random sampling. In particular, it was reasoned that dealers making large wholesale purchases would contribute more to the retail sales (of all brands) and thus one should include such dealers in the sample with larger probability. This gave rise to the idea of PPSWR sampling design. But it was reasoned that the frequency of purchase should also be factored into this equation (because dealers making large wholesale purchases every month, is in a different category compared to dealers making same kind of purchase, say, once in only three months) to stratify the dealers. Thus the dealers were stratified according to their frequency of wholesale purchase using cluster analysis, and their ``sizes'' were calculated according to their average monthly wholesale purchase amounts, for implementing the Stratified PPSWR sampling design to estimate the monthly retail sales and market share. However since intrinsically the problem was dynamic in nature with respect to the dealer profiles, this M.Tech project was conceived, which supplemented the above theoretical work, which arose as a consulting project, by developing a fully automated Decision Support System for maintenance and upgradation of the dealer database, stratification of the dealers using Hartigan's $k$-means clustering algorithm, optimal sample size allocation to each stratum, drawing of the sample by accommodating various logistic constraints, and finally providing the required monthly estimates.