Increasing Success Rates of Capital Expenditure Procurement Process by Machine Learning Algorithms Development in Owner Estimate (OE) Price Analysis

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N H A Amiri, M A Berawi, M Sari


The realization of capital expenditure budget is one of the key performance indicators. The largest portion is owned by procurement activities of goods / services. Owner Estimate (OE) is an estimate of price of goods / services that has taken into account all components of cost until it is ready to be used and utilized by users. OE is used to assess the fairness of the offer price of prospective providers of goods / services. OE preparation must be based on accountable methods and based on relevant, actual and reliable data. Machine learning is about designing algorithms that automatically extract valuable information from data. Machine learning uses linear regression to be one of the most widely used algorithms for performing price and sales prediction models. In this study, Machine learning areĀ  used to calculate owner estimate predictions in 2021 in the procurement of State-Owned Company Building Construction. From the results of the development obtained predictions with machine learning can be used in calculating the owner estimate price analysis for price predictions on the procurement of capital expenditures in building construction. Prediction results from machine learning have good fit accuracy so it can be concluded that predictions from machine learning can help budget planning and cost estimaties (Owner Estimate), which increase the project's success rate.

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