Brown beans (Phasedus vulgaris L.) were subjected to treatments to evaluate effects of pH, temperature, CaCl,, tannase and fermentation on degradation of phytate. Soaking was performed at 21"C, 37°C and 55°C at pH 4.0, 6.0, 6.4, 7.0, and 8.0. Optimal conditions for phytate degradation were pH 7.0 and 55'C. After soaking 4, 8 or 17 hr at these conditions 79%, 87% and 98% of phytate was degraded, respectively. Addition of tannase enhanced reduction of phytate. Fermentation of presoaked whole beans resulted in reduction of 88% of phytate after 48 hr.

Purpose In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products. Design/methodology/approach A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature. Findings With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling. Research limitations/implications Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts. Practical implications The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes. Originality/value This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

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.