<abstract xmlns="http://www.w3.org/1999/xhtml"><bold>Background and Purpose:</bold> 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.<bold>Design/Methodology/Approach:</bold> 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.<bold>Results:</bold> 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.<bold>Conclusions:</bold> 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.</abstract>

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.

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

Faculty of Computer and Information Science

Organizacija

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Background and Purpose: The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support...

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

Online veröffentlicht: 22. Aug. 2017

Seitenbereich: 217 - 233

Eingereicht: 15. Mai 2017

Akzeptiert: 20. Juli 2017

DOI: https://doi.org/10.1515/orga-2017-0020

Schlüsselwörter
decision support, organizational learning, machine learning, explanations, B2B sales forecasting

© 2017 Marko Bohanec et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 22. Aug. 2017

Seitenbereich: 217 - 233

Eingereicht: 15. Mai 2017

Akzeptiert: 20. Juli 2017

DOI: https://doi.org/10.1515/orga-2017-0020

Schlüsselwörterdecision support, organizational learning, machine learning, explanations, B2B sales forecasting

© 2017 Marko Bohanec et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
decision support, organizational learning, machine learning, explanations, B2B sales forecasting