Vendor Profile Analysis

摘要: Vendor Profile Analysis The industrial purchasing function is among the most critical activities for ensuring long-term viability of a firm. An effective department can substantially help trim firm's operating costs, thereby enhancing profitability. [1] Moreover, increasingly becoming important to marketing success firm, in that purchasing's policies and procedures have an impact, albeit indirect, on mix ultimate customer satisfaction. [2] It is, therefore, essential organization decisionmaking mechanism which ensures vendors are objectively appraised, with goal optimizing decisions. This article examines powerful, yet simple, technique aiding buyers prepurchase evaluation vendors. technique, referred as vendor profile analysis (VPA), designed use microcomputers using software, such Lotus 1-2-3 Symphony, capable conducting Monte Carlo simulations. easy understand because it simply modification "weighted point method." [3] power VPA stems from its simulation modeling decision makers' perceptions uncertainty associated predicting performance. As will be shown, simplifies maker's input model, provides output contains considerably more information upon base purchase decisions than do standard weighted models. Some general background current models first presented. followed by detailed discussion technique. BACKGROUND ON VENDOR ANALYSIS MODELS Suprisingly, there few published outlining normative techniques assist engaged evaluation. Traditionally, fallen into three categories: (1) categorical methods;(4) (2) methods; [5] (3) cost-based techniques, "cost-ratio" method. [6] Other models, not fitting these categories, include model based cluster analysis, [7] sequential elimination procedure, [8] linear programming application. [9]These latter however, apparently used only minimally. Timmerman in-depth review mechanics, strengths, weaknesses traditional techniques. [10] Because space limitations relevance this discussion, method reviewed here. Basic Structure Weighted Point Method Based number versions method, far commonly Its basic structure described following model: [Mathematical Expression Omitted] where [A.sub.j] = summated score representing overall performance anticipated j [a.sub.i] importance weight attached evaluative criterion i [b.sub.ij] rating n criteria A typical employing presented Table I. begins identification weighting key dimensions (evaluative or choice criteria) required evaluating alternative Column 1 I enumerates possible examining equipment. Evaluative assigned weights ([a.sub.i]s) emphasize their differential contribution decision. uses simple five-point scale purpose (column 2) - heavier weights. [Tabular Data Decision makers next rate expected ([b. …