Profitability Prediction in Agribusiness Construction Contracts: A Machine Learning Approach
Anand N. Asthana · 2013
As agriculture transforms itself from a subsistence activity to agribusiness across the world, the importance of agribusiness construction is increasing. At the same time, on account of globalisation and technological developments more and more agribusiness construction companies are becoming multinational and have started using better data processing techniques. The actual process of how contractors and their clients negotiate and agree to price is complex and not clearly articulated in the literature; but in almost all cases the profitability of a contract is the most important factor for any decision on the bid. Commercial managers employed by construction companies are asked to estimate the expected profit on a prospective contract to either decide whether to proceed with the project or to aid in financial forecasting for the company. The estimation of a prospective contract's profitability is generally done by intuition. A mathematical model to aid in predicting the profitability of a prospective contract would be of immense use to a commercial manager. It may be used as the primary source of predicting profitability whilst some commercials managers may use it as a valuable second opinion. Furthermore, it would of considerable interest to commercial managers to know the effect on predicted profitability of a contract should they change the value of an attribute of a prospective contract. the paper demonstrates that both the VSM and KRR routines are fairly simple to implement in a commercial setting. Commercial application will, however, require close interaction between scientists and business scholars.