Preface: artificial intelligence in operations management

Affording digital transformation: The role of industrial Internet platform in traditional manufacturing enterprises digital transformation

This study aims to investigate the role of industrial Internet platform in facilitating the digital transformation of traditional manufacturing enterprises. While prior research has predominantly focused on industrial Internet platform enterprises, there is a noticeable dearth of research concerning traditional manufacturing enterprises lacking the resources to establish such platforms. To address this research gap, we conduct an exploratory case study and propose an affordances upgrade model that elucidates the digital business transformation process of these manufacturing enterprises leveraging industrial Internet platforms. The research findings can be summarized from two key perspectives. Firstly, the industrial Internet platform offers valuable technical support and potential opportunities for manufacturing enterprises to achieve digital business transformation through three distinct affordances: consonance, resonance, and adaptation. These affordances enable enterprises to align their operations with the capabilities and possibilities provided by the platform, thus facilitating their digital transformation. Secondly, to effectively harness these affordances, enterprises must strategically leverage the platform's technical services and systems in their production and operational practices. Through the accumulation of practical experiences, enterprises gradually transition their production modes from experience institutionalization and standardization to a state of refinement. The dynamic leapfrogging process of digital transformation in traditional manufacturing enterprises, facilitated by the industrial Internet platform, is reflected in the realization of these three affordances and their underlying resource capabilities. This research significantly contributes to the field by expanding the scope of inquiry to encompass traditional manufacturing enterprises and presenting a stage model for their digital transformation utilizing industrial Internet platform.

Minimality and comparison of sets of multi-attribute vectors

In a decision-making problem, there is often some uncertainty regarding the user preferences. We assume a parameterised utility model, where in each scenario we have a utility function over alternatives, and where each scenario represents a possible user preference model consistent with the input preference information. With a set A of alternatives available to the decision-maker, we can consider the associated utility function, expressing, for each scenario, the maximum utility among the alternatives. We consider two main problems: firstly, finding a minimal subset of A that is equivalent to it, i.e., that has the same utility function. We show that for important classes of preference models, the set of possibly strictly optimal alternatives is the unique minimal equivalent subset. Secondly, we consider how to compare A to another set of alternatives B , where A and B correspond to different initial decision choices. This is closely related to the problem of computing setwise max regret. We derive mathematical results that allow different computational techniques for these problems, using linear programming, and especially, with a novel approach using the extreme points of the epigraph of the utility function.

From human business to machine learning—methods for automating real estate appraisals and their practical implications

Until recently, in most countries, the use of Automated Valuation Models (AVMs) in the lending process was only allowed for support purposes, and not as the sole value-determining tool. However, this is currently changing, and regulators around the world are actively discussing the approval of AVMs. But the discussion is generally limited to AVMs that are based on already established methods such as an automation of the traditional sales comparison approach or linear regressions. Modern machine learning approaches are almost completely excluded from the debate. Accordingly, this study contributes to the discussion on why AVMs based on machine learning approaches should also be considered. For this purpose, an automation of the sales comparison method by using filters and similarity functions, two hedonic price functions, namely an OLS model and a GAM model, as well as a XGBoost machine learning approach, are applied to a dataset of 1.2 million residential properties across Germany. We find that the machine learning method XGBoost offers the overall best performance regarding the accuracy of estimations. Practical application shows that optimization of the established methods—OLS and GAM—is time-consuming and labor-intensive, and has significant disadvantages when being implemented on a national scale. In addition, our results show that different types of methods perform best in different regions and, thus, regulators should not only focus on one single method, but consider a multitude of them.