We present an approach to forecast customer orders of ready-to-launch new products that are similar to past products. The approach fits product life cycle (PLC) curves to historical customer order data, clusters the curves of similar products, and uses the representative curve of the new product’s cluster to generate its forecast. We propose three families of curves to fit the PLC: bass diffusion curves, polynomial curves, and simple piecewise-linear curves (triangles and trapezoids). Using a large data set of customer orders for 4,037,826 units of 170 Dell computer products sold over three and a half years, we compare goodness of fit and complexity for these families of curves. Fourth-order polynomial curves provide the best in-sample fit with piecewise-linear curves a close second. Using a trapezoidal fit, we find that the PLCs in our data have very short maturity stages; more than 20% have no maturity stage and are best fit by a triangle. The fitted PLC curves of similar products are clustered either by known product characteristics or by data-driven clustering. Our key empirical finding is that, for our large data set, data-driven clustering of simple triangles and trapezoids, which are simple to estimate and explain, perform best for forecasting. Our conservative out-of-sample forecast evaluation, using data-driven clustering of triangles and trapezoids, results in mean absolute errors approximately 2%–3% below Dell’s forecasts. We also apply our method to a second data set of a smaller company and find consistent results. The online appendix is available at https://doi.org/10.1287/msom.2017.0691 .
PurposeCaring for older adults is an increasingly complex and multi-dimensional global concern. This article provides a comprehensive definition of the older adult care experience and discusses its key components to help practitioners deliver older adult-centered care to maximize well-being outcomes for older adults.Design/methodology/approachBased on prior research on service operations, service experience, person-centered care and the unique, evolving needs of older adults regarding their care, this paper develops a conceptual framework in which the older adult care experience is the central construct, and key dimensions of well-being are the outcomes.FindingsThe older adult care experience is shaped by older adults' perceptions and evaluations of the care that they receive. Older adult-centered care has autonomy, dignity, unique needs and social environment as its core dimensions and results in those older adults feel empowered, respected, engaged and connected as part of their experience. The article also discusses how such experience can be evaluated by using quality dimensions from service operations, hospitality and healthcare contexts, and challenges that service firms may face in creating older adult care experience.Research limitations/implicationsGiven the changing demographics and unique needs of older adults, it is an imperative for academics and practitioners to have an understanding of what determines older adult care experience to better serve them. Such understanding is important as by creating and fostering older adult care experience, service organizations can contribute to individual and societal well-being.Originality/valueTo the authors' best knowledge, this is the first paper to provide a comprehensive conceptualization of the older adult care experience.
This paper looks at the technological growth of new car fleet fuel efficiency in the European Union between 1975 and 2015. According to the analysis results, from1975 to 2006 the fuel efficiency technology improvements were largely offset by vehicles' increased weight, engine size, and consumer amenities such as acceleration capacity. After 2006, downsizing in weight and engine capacity was observed in new car fleet, while fuel consumption decreased by 32% between 2006 and 2015. We adopt a statistical method and find that from 1975 to 2015, a 1% increase in weight would result in 0.3 to 0.5% increments in fuel consumption per 100 km, and a 1% reduction in 0-100 km/h acceleration time would increase fuel consumption by about 0.3%. Impacts of other attributes on fuel consumption are also assessed. To meet the European Union's 2021 fuel consumption target, downsizing of cars, as well as at least maintaining fuel efficiency technology growth trend observed between 2005 and 2015, are needed.Government policies on controlling improvement in acceleration performance or promoting alternative fuel vehicles are also important to achieve European Union 2021 target.
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