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Publisher Copyright: © 2023 The Authors Most studies published in Technovation adopt a theory-driven approach toward empirical phenomena. In this editorial, we call for a more instrumental approach, one that effectively serves the needs of practitioners in the field of technological innovation as well as scholars seeking to increase their impact on innovation practice. In this respect, design science (DS) methodologies have emerged in various adjacent domains such as information systems, operations management, and entrepreneurship. Inspired by Simon's “The Sciences of the Artificial”, DS operates at the interface of problem-solving design and explanatory science to create and test solutions as artifacts. Whereas DS work can result in various kinds of artifacts, tools for practitioners are the most promising ones. We first provide several examples of DS work resulting in widely used tools and then identify various challenges in the technological innovation domain that call for tools addressing these challenges. Subsequently, we provide practical guidance on how to prepare manuscripts about designing and testing tools which are likely to have a major impact on practice. Non

PurposeThis paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.Design/methodology/approachThe paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.FindingsThe cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.Research limitations/implicationsVirtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.Originality/valueThe paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.

Food waste in grocery supply chains may exceed one third of the total volume, depending on the category. To address this problem effectively, grocery retailers are introducing automated systems for more efficient store replenishment and dynamic pricing. The stock keeping unit (SKU) and store level forecast is pivotal in these operations management solutions, but operationally challenging. Large grocery retailers have millions of SKU-store combinations that depending on the operational application would need to be forecasted on a weekly, daily, hourly, or even 15-min frequency. However, in grocery it is challenging to account for demand variation at high frequencies without introducing manual decisions into the process of forecast model configuration. To investigate the limits of current practice and explore opportunities of technology-enabled change, we explore how an advanced forecasting method for electricity demand, called TBATS, can automate daily forecasting for grocery store replenishment. Adopting an interventionist approach, we explore the implications for the design of the operational process in the operational setting provided by the case company. We find that TBATS can produce high frequency base forecasts for the SKU-store level accurately for a period exceeding 3 months. This finding points to the opportunity of shifting operational focus from recalculating forecasts to monitoring forecast errors. Introducing variable, even indefinite re-training frequencies for forecasting models is a significant change of the forecasting process for situations where monitoring requires less computation than retraining, potentially reducing the time and cost associated with increasing the forecast frequency.