We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decision model is validated in a real-world application at a leading manufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditional methods. This paper was accepted by Charles Corbett, operations management.

To facilitate the implementation of lean production, practitioners and researchers have suggested an array of critical success factors (CSFs). However, despite a broad consensus about what needs to be done, companies still struggle to implement lean. Contingency theory posits the explanation that the common advice is not universal but is instead contingent on the situation. This paper investigates how contingency variables influence what practitioners see as success factors for implementing lean. A survey asked 432 practitioners from 83 factories belonging to two multinational companies for their opinions about what managers should do to ensure the success of the factory-level implementation of lean production. The survey responses were grouped into general success factors, which were then tested for differences across four contingency variables: corporation, factory size, stage of lean implementation and national culture. In general, the analysis supports a generic list of CSFs, but with some minor exceptions. For example, the stage of lean implementation in a factory influences to a slight extent which factors are perceived as more effectual than others. The paper contributes to the literature on lean production and offers several recommendations for managers striving to implement lean in their firms.

Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human–AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human–AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed $$N=9,600$$ assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed $$N=5,650$$ assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, $$P=0.001$$ ) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, $$P=0.010$$ ) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.

Although disruptive “Industry 4.0” technologies often lack a clear business case, vendors are advocating and companies are actively exploring their use in operations settings. The technology management literature suggests that successful adoption derives from an appropriate fit between the specific technology and (1) economic and strategic factors, (2) operational and supply chain factors, and (3) organizational and behavioral factors. Through a five‐year research project, we explore how drones—an archetypal emerging technology supported by a thriving vendor ecosystem—transitioned from early ideas to experimental applications to full adoption in daily operations. We analyze a range of data, including exploratory interviews with drone ecosystem actors, a secondary dataset, and case studies of drone applications in Geberit and IKEA. Key findings relate to our observation that technology adoption patterns for emerging technologies do not always follow the traditional linear logic of technology fit. We find that emerging technologies are characterized by a dynamic interaction between technology push from a thriving ecosystem and market pull from companies exploring meaningful operational and business value using the concept of “use case.” Based on these findings, we contribute to the technology management literature with an alternative technology adoption framework for emerging “Industry 4.0” technologies.