Smart products not only provide novel functionalities, but also may establish new business models, markets, or distribution channels, strengthen relationships with consumers, and/or add smart remote services. While many technical obstacles of such products have already been overcome, the broad market dissemination of smart products still poses some vital managerial challenges for decision makers. In this paper, we outline the technical potential and future trends of smart consumer products, discuss economic challenges in four scopes, namely, preference-based new product development, market analysis, supply chain design, and industry development, and, in particular, we highlight research perspectives for management science in this promising field.

No abstract

E-grocery offers customers an alternative to traditional brick-and-mortar grocery retailing. Customers select e-grocery for convenience, making use of the home delivery at a selected time slot. In contrast to brick-and-mortar retailing, in e-grocery on-stock information for stock keeping units (SKUs) becomes transparent to the customer before substantial shopping effort has been invested, thus reducing the personal cost of switching to another supplier. As a consequence, compared to brick-and-mortar retailing, on-stock availability of SKUs has a strong impact on the customer's order decision, resulting in higher strategic service level targets for the e-grocery retailer. To account for these high service level targets, we propose a suitable model for accurately predicting the extreme right tail of the demand distribution, rather than providing point forecasts of its mean. Specifically, we propose the application of distributional regression methods -so-called Generalised Additive Models for Location, Scale and Shape (GAMLSS) -to arrive at the cost-minimising solution according to the newsvendor model. As benchmark models we consider linear regression, quantile regression, and some popular methods from machine learning. The models are evaluated in a case study, where we compare their out-of-sample predictive performance with regard to the service level selected by the e-grocery retailer considered.

The standard mixed-integer linear model formulation for the multi-item discrete lot-sizing and scheduling problem (DLSP) is extended by additional partially nonlinear constraints for the case of two-stage batch production. The corresponding feasibility problem is NP-complete in the case of non-zero setup times. A simulated annealing approach is suggested for computing production schedules on both stages. Numerical results are presented. IntroductionIn general, manufacturing is a multi-level process, where coordinating the different stages requires additional effort compared to single-stage problems. The multi-level lot-sizing problem deals with choosing cost-optimal lot-sizes in the case of uncapacitated (MLLP) or capacitated (MLCLP) production facilities. For the MLLP, some research on serial and assembly product structure is reviewed by Salomon (1991). The M LCLP is known to be N P-hard even for a single product (Chen and Thizy 1990). Salomon (1991) reviews briefly the literature on the MLCLP and suggests heuristics for the single bottleneck case. Maes et al. (1991) present heuristics for the MLCLP with capacity constraints on more than one production stage. However, these approaches do not consider the sequencing of the batches within a production period. In contrast, the discrete lot-sizing and scheduling problem (DLSP) is a deterministic production planning model for the simultaneous cost-optimal choice of the lot-sizes and the sequence of jobs in the multi-item case. The principal idea of the DLSP is to divide the finite time horizon into (small) time intervals in which the machines can be used either for production of at most a single product, or can be set up for such a production. By considering only finite production speeds, the manufacturing capacity is limited. A comprehensive reference to the DLSP is given by Salomon (1991). Drexl and Haase (1992) explore the similarities of different model types suggested for the lot-sizing and scheduling problem.The standard DLSP pertains to the case of instantaneous availability of the manufactured units prior to completion of the lot. But in practice units may arrive in inventory in one batch no earlier than completion of the whole lot. The DLSP is modified for this case of batch production by Briiggemann and Jahnke (1993). Salomon (1991) shows that generating feasible solutions for the standard DLSP with nonvanishing setup times is NP-complete, which is also true for the version modified for batch production. Especially in this latter case, exact algorithms will be timeconsuming, if they succeed in finding a solution at all. However, in practical applications it is desirable to study the sensitivity of the optimal schedules to changes in the cost parameters which are often not known precisely. Moreover, quick responses to changing data are needed in applied production planning. These tasks can only be accomplished by fast heuristics even though the solutions proposed might be suboptimal.

In the past, most companies in the European apparel industry focused on minimizing manufacturing costs in the design of supply chains in conjunction with long-distance shipping from production sites in the Far East and relatively long production cycles. Today, for some market segments, the speed of production cycles is more important than the cost because short throughput time allows the flexibility to adjust to rapidly changing fashion trends in these market segments. Accordingly, choosing the most beneficial supply chain strategy has become an established research topic. However, apparel markets are complex systems. Therefore, attempts to reduce the underlying complexity in order to model these markets have limited existing research to the consideration of only selected aspects of markets (e.g., considering only homogeneous buyers, a single period, a single product, or a single manufacturer in the absence of competition) rather than taking a more comprehensive view. These limitations can be overcome by applying an agent-based simulation approach—an approach that can account for a wider range of factors, including several competing manufacturers utilizing different supply chain strategies, individual consumer preferences and behavior, word-of-mouth communication, normative social influence, and first-hand experience, as well as advertising. In this paper, the capability potential of such agent-based market simulation is demonstrated by investigating two supply chain strategies (fast fashion vs. traditional fashion) with varying product and communication strategies (product attributes and advertising) in several market scenarios.