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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

The purpose of this study is to describe participation in decision-making among service users with severe mental illness.

Service users want to participate in decision-making and in the planning of their care. There are widely known methods, such as shared decision-making, that could be used to facilitate service user participation. Three focus group interviews were conducted with the participation of 14 persons with mental illness and/or substance abuse who were service users at two Swedish Homes for Care and Residence (HVB). Data were analyzed by qualitative content analysis.

Two themes emerged: service users’ involvement in decisions is hampered by the professionals’ approach and adequate information and experience of participation means greater empowerment.

Although it is known that service users would like to have more influence, and that methods like shared decision-making are recommended to empower service users and improve the decision process, research on these matters is limited.

This study reveals that there is a need of more systematic decisional support, such as shared decision-making, so that service users can be seen as important persons not only in guidelines and policy documents but also in clinical practice.

The findings indicate that service users do not participate in decisions systematically, although policies, guidelines and laws providing that service users should be offered an active part in decision-making with regard to their care and treatment.

Although it is known that service users would like to have more influence, and that methods like shared decision-making are recommended to empower service users and improve their decision process, research on these matters is limited. The findings indicate that service users do not participate in decisions systematically, even though policies, guidelines and laws are in place stipulating that service users should be offered an active part in decision-making with regard to their own care and treatment. The results of this project bring improvement opportunities to light.

The purpose of this study was to describe mental health professionals’ experiences of changes in attitudes towards, and knowledge about, users of mental health-care recovery and decisional participation in clinical practice after an educational intervention.

Users of mental health care want to participate in decisions regarding their own mental health care. Shared decision-making as a method is coherent with recovery orientation in mental health services and results in better-informed patients and fewer conflicts regarding decisions. A qualitative intervention study was designed to evaluate changes in attitudes and knowledge about mental health recovery in Sweden. Nine participants were interviewed, and the data were analysed by content analysis.

Three categories were generated from the analysis: Increased theoretical knowledge, changing attitudes about practical approaches and the significance of social factors in recovery.

When shared decision-making is to be implemented in mental health, professionals need to gain knowledge about recovery and need to adopt changed roles as health professionals. Educational interventions therefore seem necessary if such changes are to happen.

Drawing on information processing theory, the linkage between buffering and bridging and the ability on the part of procurement to resolve demand–supply imbalances is investigated, as well as contexts in which these strategies may be particularly useful or detrimental. Buffering may be achieved through demand change or redundancy, while bridging may be achieved by the means of collaboration or monitoring.

This study employs a hierarchical regression analysis of a survey of 150 Finnish and Swedish procurement and sales and operations planning professionals, each responding from the perspective of their own area of supply responsibility.

Both the demand change and redundancy varieties of buffering are associated with procurement's ability to resolve demand–supply imbalances without delivery disruptions, but not with cost-efficient resolution. Bridging is associated with the cost-efficient resolution of imbalances: while collaboration offers benefits, monitoring seems to make things worse. Dynamism diminishes, while the co-management of procurement in S&OP improves procurement's ability to resolve demand–supply imbalances. The most potent strategy for tackling problematic contexts appears to be buffering via demand change.

The results highlight the importance of procurement in the S&OP process and suggest tactical measures that can be taken to resolve and reduce the effects of supply and demand imbalances.

The results contribute to the procurement and S&OP literature by increasing knowledge regarding the role and integration of procurement to the crucial process of balancing demand and supply operations.

This 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.

The 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.

The 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.

Virtual 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.

The 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.

The purpose of this study is to elaborate on how analytics capability develops within the PSM function. This study is an in-depth exploration of how analytics capability develops within the purchasing and supply management (PSM) function.

A multiple case study was conducted of the PSM function of six case firms, in which primary data were collected through semi-structured interviews with PSM analytics stakeholders. The data were analyzed based on an analytics capability framework derived from the literature. Cases were chosen based on them having advanced PSM practices and ongoing analytics projects in the PSM area.

The findings shed light on how the firms develop their analytics capability in the PSM functional area. While we identify several commonalities in this respect, the authors also observe differences in how firms organize for analytics, bringing analytics and PSM decision-makers together. Building on the knowledge-based view of the firm, The authors offer a theoretical explanation of our observations, highlighting the user-driven side of analytics development, which has largely been unrecognized by prior literature. The authors also offer an explanation of the observed dual role that analytics takes in cross-functional initiatives.

The exploratory nature of our study limits the generalizability of our results. Further, our limited number of cases and interviewees indicate that there is still much to explore in the phenomenon of developing analytics capability.

Our findings can help firms gain a better understanding of how they could develop their analytics capability and what issues they need to consider when seeking leveraging data through analytics for PSM decisions.

This paper is, to the best knowledge of the authors, the first empirical study of analytics capability in PSM.