A Framework for Defining, Measuring, and Predicting Service Procurement Savings

Abstract: Recent technical advances have paved the way for transformations such as Industry 4.0, Supply Chain 4.0, and new ways for organizations to utilize services to meet the needs of people. In the midst of this shift, a focus has been put on service procurement to meet the demand of everything from cloud computing and information technology to software solutions that support operations or add value to the end customer. Procurement is an integral part of organizations and typically accounts for a substantial part of their costs. Analyzing savings is one of the primary ways of measuring cost reduction and performance.  This paper examines how savings can be defined and measured in a unifying way, and determine if machine learning can be used to predict service purchase costs. Semi-structured interviews were utilized to find definitions and measurements. Three decision-tree ensemble machine learning models, XGBoost, LightGBM, and CatBoost were evaluated to study cost prediction.  The result indicates that cost reduction and cost avoidance should be seen as a financial, and a performance measure, respectively. Spend and capital binding can be controlled by a budget reallocation system and could be improved further with machine learning cost prediction.  The best performing model was XGBoost with a MAPE of 14.17%, compared to the base model’s MAPE of 40.24%. This suggests that budget setting and negotiation can be aided by more accurately predicting cost through machine learning, and in turn have a positive impact on an organization’s resource allocation and profitability.