The Potential for Cannibalization of New Products Sales by Remanufactured Products*

The potential for cannibalization of new product sales by remanufactured versions of the same product is a central issue in the continuing development of closed‐loop supply chains. Practitioners have no fact‐based information to guide practice at firms and academics have no studies available to use as the basis for assumptions in models. We address the cannibalization issue by using auctions to determine consumers’ willingness to pay (WTP) for both new and remanufactured products. The auctions also allow us to better understand the potential impact of offering new and remanufactured products at the same time, which provides us insights into the potential for new product cannibalization. Our results indicate that, for the consumer and commercial products auctioned, there is a clear difference in WTP) for new and remanufactured goods. For the consumer product, there is scant overlap in bidders between the new and remanufactured products, leading us to conclude that the risk of cannibalization in this case is minimal. For the commercial product, there is evidence of overlap in bidding behavior, exposing the potential for cannibalization.     

A Flexible Evaluative Framework for Order Picking Systems

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.     

The stressor–strain relationship in diary studies: A meta-analysis of the within and between levels

Daily diary studies use the same set of measures repeatedly for several days. Within the work stress domain, these studies are able to isolate the effects of daily exposure to stressors within people from the general level of stressors between people. This meta-analysis investigated both content-related and methodological aspects of workplace stressor–strain relationships in diary studies. Results from 55 unique samples (a combined sample size of 5409) indicated that the magnitude of the stressor–strain relationship was stronger at the between-person level than the within-person level. Further, when the stressor was measured prior to the strain (within the same day), the relationship was somewhat stronger than when stressor and strain were measured concurrently. This suggests that stressor–strain effects might take some time to fully manifest. Differences were also detected among types of strains: affective strains had stronger relationship with stressors than behavioural strains. There were also differences in the stressor–strain relationship depending on both the type of strain and the timing of their respective measurement (concurrent versus predictive), suggesting that certain strain responses require more time to manifest. Overall, this meta-analysis elucidates important considerations in the design and interpretation of diary studies on occupational stress.     

High value manufacturing (HVM) in the UK: case studies and focus group insights

This paper studies the internal mechanisms that allow organisations to become high value manufacturing (HVM). Using a qualitative methodology, three UK manufacturing companies formed in-depth case studies with semi-structured interviews, observations and historical data. The HVM value matrix of Martinez and co-workers is used to categorise each organisation’s value proposition. Wider benchmarking of the three organisations was carried out against a focus group with an additional seven manufacturing organisations. Thus, data from 10 manufacturing organisations are included in this research. The cases follow the ‘customer intimacy’ HVM discipline. The business processes supporting these value propositions were identified. Interestingly, each organisation’s desired value proposition differs from their current one. ‘Technological integrators’ predominantly rely on new product development (NPD) and Strategy processes, whereas ‘Socialisors’ rely predominantly on Strategy and Customer Relationship processes. Companies can use the findings to better understand their current HVM value proposition and, where necessary, plan their transition to a future desired HVM value proposition.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Let's Call it Quits: Break‐Even Effects in the Decision to Stop Taking Risks

“Chasing” behavior, whereby individuals, driven by a desire to break even, continue a risky activity (RA) despite incurring large losses, is a commonly observed phenomenon. We examine whether the desire to break even plays a wider role in decisions to stop engaging in financially motivated RA in a naturalistic setting. We test hypotheses, motivated by this research question, using a large data set: 707,152 transactions of 5,379 individual financial market spread traders between September 2004 and April 2013. The results indicate strong effects of changes in wealth around the break‐even point on the decision to cease an RA. An important mediating factor was the individual's historical long‐term performance. Those with a more profitable trading history were less affected by a fall in cash balance below the break‐even point compared to those who had been less profitable. We observe that break‐even points play an important role in the decision of nonpathological risk takers to stop RAs. It is possible, therefore, that these nonpathological cognitive processes, when occurring in extrema, may result in pathological gambling behavior such as “chasing.” Our data set focuses on RAs in financial markets and, consequently, we discuss the implications for institutions and regulators in the effective management of risk taking in markets. We also suggest that there may be a need to consider carefully the nature and role of “break‐even points” associated with a broader range of nonfinancially‐focused risk‐taking activities, such as smoking and substance abuse.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Evaluating and Visualizing the Economic Impact of Commercial Districts Due to an Electric Power Network Disruption

Critical infrastructure networks enable social behavior, economic productivity, and the way of life of communities. Disruptions to these cyber–physical–social networks highlight their importance. Recent disruptions caused by natural phenomena, including Hurricanes Harvey and Irma in 2017, have particularly demonstrated the importance of functioning electric power networks. Assessing the economic impact (EI) of electricity outages after a service disruption is a challenging task, particularly when interruption costs vary by the type of electric power use (e.g., residential, commercial, industrial). In contrast with most of the literature, this work proposes an approach to spatially evaluate EIs of disruptions to particular components of the electric power network, thus enabling resilience‐based preparedness planning from economic and community perspectives. Our contribution is a mix‐method approach that combines EI evaluation, component importance analysis, and GIS visualization for decision making. We integrate geographic information systems and an economic evaluation of sporadic electric power outages to provide a tool to assist with prioritizing restoration of power in commercial areas that have the largest impact. By making use of public data describing commercial market value, gross domestic product, and electric area distribution, this article proposes a method to evaluate the EI experienced by commercial districts. A geospatial visualization is presented to observe and compare the areas that are more vulnerable in terms of EI based on the areas covered by each distribution substation. Additionally, a heat map is developed to observe the behavior of disrupted substations to determine the important component exhibiting the highest EI. The proposed resilience analytics approach is applied to analyze outages of substations in the boroughs of New York City.     

Lack of reciprocity among Dutch teachers: Validation of reciprocity indices and their relation to stress and well-being

This research presents the results of two related studies on the convergent and construct validity of three measures of reciprocity in exchange relationships at work. In Study 1, 71 Dutch teachers were interviewed about their specific investments and outcomes in the exchange relationships with their students, colleagues and school. ANOVA revealed that they reported significantly more investments than outcomes, and that the number of reported investments and outcomes mentioned varied as a function of the type of exchange relationship. Building on these results, multi-item scales were created to assess reciprocity at a detailed level for each of the three exchange relationships. Study 2 validated these specific reciprocity measures by relating them to two global assessments of reciprocity (convergent validity) as well as to measures of job stress and well-being (construct validity). LISREL-analysis of data obtained from a further sample of 224 teachers revealed that for each type of exchange relationship there were significant, consistent and meaningful relationships among the three reciprocity measures. Further, hierarchical regression analysis showed that the reciprocity measures were differentially related to job stressors and measures of well-being. Implications are discussed.