Organizing for permanent beta: performance measurement before vs performance monitoring after release of digital services

Abstract

PurposeDue to the complexity of digital services, companies are increasingly forced to offer their services “in permanent beta”, requiring continuous fine-tuning and updating. Complexity makes it extremely difficult to predict when and where the next service disruption will occur. The authors examine what this means for performance measurement in digital service supply chains.Design/methodology/approachThe authors use a mixed-method research design that combines a longitudinal case study of a European digital TV service provider and a system dynamics simulation analysis of that service provider's digital service supply chain.FindingsWith increased levels of complexity, traditional performance measurement methods, focused on detection of software bugs before release, become fragile or futile. The authors find that monitoring the performance of the service after release, with fast mitigation when service incidents are discovered, appears to be superior. This involves organizational change when traditional methods, like quality assurance, become less important.Research limitations/implicationsThe performance of digital services needs to be monitored by combining automated data collection about the status of the service with data interpretation using human expertise. Investing in human expertise is equally important as investing in automated processes.Originality/valueThe authors draw on unique empirical data collected from a digital service provider's struggle with performance measurement of its service over a period of nine years. The authors use simulations to show the impact of complexity on staff allocation.

The mediating role of knowledge management processes in the effective use of artificial intelligence in manufacturing firms

“…, 2021; Mohapatra et al. , 2021; van Oorschot et al. , 2022); 2) implement knowledge management processes (KMPs) capable of identifying the necessary knowledge and disseminating it within the organisation (Leoni, 2015; Sutopoh et al.…”

“…Increasing uncertainty and turbulent contexts might have significant repercussions on the manufacturing industry in terms of mandatory closures, logistics bottlenecks, supply difficulties and volatility in consumption trends (Ardolino et al, 2022). Indeed, in recent times, numerous research studies have demonstrated how the success and survival of manufacturing firms are closely linked to their ability to 1) embrace advanced digital technologies, such as artificial intelligence (AI) (Eloranta et al, 2021;Mohapatra et al, 2021;van Oorschot et al, 2022); 2) implement knowledge management processes (KMPs) capable of identifying the necessary knowledge and disseminating it within the organisation (Leoni, 2015;Sutopoh et al, 2021); and 3) put in place supply chain resilience (SCR) strategies to maintain satisfactory levels of performance in the short, medium and long terms (Belhadi et al, 2021a). In fact, these three elements (AI, KMPs and SCR)individually consideredpositively affect firm performance (Jallow et al, 2020;Li et al, 2017;Tan and Wong, 2015).…”

What is next? The effect of reverse logistics adoption on digitalization and inter-organizational collaboration

“…Rasool et al (2021Rasool et al ( , p. 1204) defined DSC as "a seamlessly interconnected transparent supply chain, that independently performs decision support activities to minimize human input needs". Studies have reported several benefits of DSC over the traditional supply chain, including improved transparency, speed, flexibility, productivity and profitability (Haoud and Hasnaoui, 2019;Oorschot et al, 2022). The list of these benefits expands when applied to a closed-loop supply chain.…”