Machine learning utilization in sales and operations planning process

Abstract

Sales and operations planning process has been a part of organizations’ everyday life al-ready tens of years. Even though there are a lot of research in this field, organizations are still struggling to get enough results and reach enough efficiency in their planning process. Nowadays, different kinds of intelligent solutions have become more and more in use. Machine learning is one of those solutions, which can help organizations to reach better results in their operations, also in sales and operations planning. The problem is often to find suitable phase or problem where to utilize machine learning in a proper way. Machine learning has become a popular research topic, and in general, artificial intelligence is very interesting topic with a lot of possibilities nowadays. This thesis focuses on the machine learning possibilities in the field of sales and operations planning within a case organization. The goal was to focus on the machine learning utilization within sales and operations planning, including the possible value and challenges of it. The study was conducted with the help of relevant research literature and an interview round with the key persons of the case organization. Those persons had a significant role in sales and operations planning process in different business areas. Literature review introduces the theoretical background of the research topic including sales and operations planning, machine learning and how they can be connected. Empirical part covers the sales and operations planning process of the case organization and focuses on the machine learning utilization possibilities, possible value and the most important factors that should be taken into account in machine learning utilization. Main findings focused on the actual utilization of ML within organization’s sales and operations planning process, the value that can be reached by utilizing machine learning and also the challenges around the topic. In demand planning of a business area could be implemented a machine learning solution by taking into account some new external data sources and so get more detailed and fact-based view into demand. In general there were several different value factors found in the research. Most of them were connected to the calculation power of ma-chine learning and its ability to handle data in more intelligent way than humans. The biggest challenges seemed to be related to handling qualitative and experience-based data, that the organization include pretty much, especially regarding sales and operations planning. All in all, theoretical background gave a good basis for the research and empirical part larger the view around the researched topic. Comparable results between those two sections could be found and the big picture around machine learning and sales and operations planning seems to be really interesting.