Doculynx is a nationwide document scanning service provider, with centers and sales offices across the US, from California to Georgia. The organization aids clients with data workflow and document imaging solutions, including scanning. Their deals tend to be highly complex and variable based on dozens of factors, including the type of document, length of indexing, the type of quality assurance needed, and distance from center. These variables determine the number of labor hours per box – the primary cost driver for the organization.
Given the complexity of deals that Doculynx closes, a methodical approach is required to ensure that a deal covers the labor expense to complete the work, and provide enough margin to add to profitability. Traditionally, pricing decisions were made by “best guess” method, with no feedback on whether the pricing was accurate. Doculynx asked BSA to help them understand their actual deal cost, and to build a quotation tool that predicted cost based on this algorithm.
Business Science Associates began, by conducting Doculynx’s first time study – measuring how much labor was required for all production steps, and then measuring the variation of driver. Applying statistical regression (and other tools), we used these measurements to create an algorithm that accurately predicted the deal cost. Using this algorithm, BSA then developed a predictive model and self-service quotation tool, to help sales managers analyze deal profitability.