Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

摘要

Background and Purpose: The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgment of a decision-maker. The problem of B2B sales forecasting can be modeled as a classification problem. However, top performing machine learning (ML) models are black boxes and do not support transparent reasoning. The purpose of this research is to develop an organizational model using ML model coupled with general explanation methods. The goal is to support the decision-maker in the process of B2B sales forecasting. Design/Methodology/Approach: Participatory approach of action design research was used to promote acceptance of the model among users. ML model was built following CRISP-DM methodology and utilizes R software environment. Results: ML model was developed in several design cycles involving users. It was evaluated in the company for several months. Results suggest that based on the explanations of the ML model predictions the users' forecasts improved. Furthermore, when the users embrace the proposed ML model and its explanations, they change their initial beliefs, make more accurate B2B sales predictions and detect other features of the process, not included in the ML model. Conclusions: The proposed model promotes understanding, foster debate and validation of existing beliefs, and thus contributes to single and double-loop learning. Active participation of the users in the process of development, validation, and implementation has shown to be beneficial in creating trust and promotes acceptance in practice.%Ozadje in namen: Napovedovanje prodaje na medorganizacijskem trgu je kompleksen odločitveni proces. Čeprav obstaja več pristopov in orodij za podporo temu procesu, se odločevalci v praksi še vedno zanašajo na subjektivno presojo. Problem je možno modelirati kot klasifikacijski problem, vendar pa so zmogljivi modeli strojnega učenja crne škatle, ki ne podpirajo transparentne razlage. Namen raziskave je predstaviti organizacijsko-informacijski model, ki temelji na modelu strojnega učenja, razširjenega s splošnimi metodami razlage, s ciljem podpore odločevalcem v procesu napovedovanja prodaje na medorganizacijskem trgu. Načrt/metodologija/pristop: Uporabili smo pristop akcijskega načrtovanja, ki z vključevanjem uporabnikov v proces raziskovanja, spodbuja sprejetost modela med uporabniki. Pri razvoju modela strojnega učenja smo sledili metodologiji CRISP-DM ter uporabili programsko okolje R. Rezultati: Model strojnega učenja smo skupaj z uporabniki razvijali v več ciklih. Model smo ovrednotili z večmeseč-no uporabo v sodelujočem podjetju. Rezultati kažejo, da so uporabniki izboljšali napovedi prodaje, ko so uporabljali model strojnega učenja, opremljenega z razlago napovedi. Ko so začeli zaupati v model, so na podlagi napovedi in razlag spremenili svoja prepričanja, izdelali natančnejše napovedi in prepoznali lastnosti procesa, ki ga model strojnega učenja ne vključuje. Zaključki: Predlagani pristop podpira razumevanje, spodbuja diskusijo in validacijo obstoječih prepričanj ter na ta način prispeva k učenju z enojno in dvojno zanko. Aktivno sodelovanje uporabnikov v procesu razvoja, validacije in implementacije je prispevalo k zaupanju in s tem k sprejetosti modela v praksi.