According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Machine Learning in Agriculture: A Review.

THE FIFTH DISCIPLINE The Art & Practice of the Learning Organization

Uniform and comprehensive explanations for an arbitrary black-box prediction model.Interactive what-if analysis for the evaluation of decision options.Support for validation and updates of decision makers' mental models.Real-world application on a difficult business problem - sales forecasting.The real-world business-to-business sales data set used is publicly available. A complexity of business dynamics often forces decision-makers to make decisions based on subjective mental models, reflecting their experience. However, research has shown that companies perform better when they apply data-driven decision-making. This creates an incentive to introduce intelligent, data-based decision models, which are comprehensive and support the interactive evaluation of decision options necessary for the business environment.Recently, a new general explanation methodology has been proposed, which supports the explanation of state-of-the-art black-box prediction models. Uniform explanations are generated on the level of model/individual instance and support what-if analysis. We present a novel use of this methodology inside an intelligent system in a real-world case of business-to-business (B2B) sales forecasting, a complex task frequently done judgmentally. Users can validate their assumptions with the presented explanations and test their hypotheses using the presented what-if parallel graph representation. The results demonstrate effectiveness and usability of the methodology. A significant advantage of the presented method is the possibility to evaluate seller's actions and to outline general recommendations in sales strategy.This flexibility of the approach and easy-to-follow explanations are suitable for many different applications. Our well-documented real-world case shows how to solve a decision support problem, namely that the best performing black-box models are inaccessible to human interaction and analysis. This could extend the use of the intelligent systems to areas where they were so far neglected due to their insistence on comprehensible models. A separation of the machine learning model selection from model explanation is another significant benefit for expert and intelligent systems. Explanations unconnected to a particular prediction model positively influence acceptance of new and complex models in the business environment through their easy assessment and switching.

Explaining machine learning models in sales predictions

Business model innovation (BMI) is one of the key activities, which has to be continuously undertaken in every enterprise to sustain competitiveness in today’s digital economy. Although the interest in business models (BM) has risen significantly since the late 1990s, little is known about BMI practices in small- and medium-sized enterprises (SMEs), representing a majority of enterprises in the European market. Our study addresses this research gap and focuses on the investigation of drivers and outcomes of BMI in SMEs. A partial least squares path modeling (PLS-PM) method was used to empirically test the model using data collected in 2017 from 71 SMEs in Slovenia. Results have shown that the innovativeness of enterprises and their business environment has a positive impact on the level of BMI activities in the enterprises. However, this is not the case of fast developing and rapidly changing information technology. In addition, the results show that the level of BMI has a positive impact on BMI outcomes and further on overall business performance. The results contribute to the body of knowledge in the field of BMI activities in SMEs and offer useful insights to SMEs that intend to innovate their business model(s) and government to create a supportive environment.

Drivers and Outcomes of Business Model Innovation—Micro, Small and Medium-Sized Enterprises Perspective

The research project addresses the influence of feedback information on the decision process supported by the application of system dynamics models. A user-friendly application was developed and used in the experiment with decision groups. The participants were 174 undergraduate management science students. They had the task of determining the optimum business strategy by maximizing the multiple criteria function under three experimental conditions: a
 1) an indivIDual decision process without the support of a system dynamics model, a
 2) an indivIDual decision process supported by a system dynamics model, and a
 3) a decision process supported by a system dynamics model and subject interaction via computer mediation. The hypotheses that the indivIDual decision process supported by a system dynamics model yields higher Criteria Function values than one without a system dynamics model, as well as the decision process supported by both a system dynamics model and subject interaction yields higher Criteria Function values than one supported by a system dynamics model alone were confirmed.

The Role of Information Feedback in the Management Group Decision-Making Process Applying System Dynamics Models

This paper addresses the impact of feedback information and facilitation on a decision-making process supported by a system dynamics model. We conducted the Solomon Four-Group Experiment under four conditions: (a1) interrupted individual determination of a strategy supported by a simulation model and a facilitator; (a2) interrupted individual determination of a strategy supported by a simulation model and a facilitator plus group information feedback (GIF); (a3) continuous individual determination of a strategy supported by a simulation model; and (a4) continuous individual determination of a strategy supported by a simulation model plus GIF. The observed variables were criteria function (CF), frequency of simulation runs (FSR), and insight into GIF. The hypotheses that a simulation model supports individual learning and additional GIF contributes to faster learning were confirmed. The importance of the facilitator and structure of feedback information was demonstrated and a model explaining learning in the decision process was developed. Copyright © 2011 System Dynamics Society.

The relevance of facilitation in group decision making supported by a simulation model

Purpose




The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning.




Design/methodology/approach




To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology.




Findings




The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team.




Research limitations/implications




The quality and quantity of available data affect the performance of models and explanations.




Practical implications




The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning.




Social implications




All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making.




Originality/value




The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.

Mirjana Kljajić Borštnar

Papers

Explaining machine learning models in sales predictions

Drivers and Outcomes of Business Model Innovation—Micro, Small and Medium-Sized Enterprises Perspective

The Role of Information Feedback in the Management Group Decision-Making Process Applying System Dynamics Models

The relevance of facilitation in group decision making supported by a simulation model

Decision-making framework with double-loop learning through interpretable black-box machine learning models