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.     

Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

Winning the interviewing game. II

Try to gain as much knowledge as possible about the culture, tactics, psychology and sensitivity of a hiring organization before you go on the job interview. Even small slights or seemingly harmless missteps can cost you the job.     

The nature, extent and determinants of violence against psychiatric personnel

Violence against psychiatric staff seems to be on the increase. Such abuse can lead to mental health consequences for the staff and a reluctance to be closely involved with patients. Few Swedish investigations have examined violence against mental nurses and psychiatrists, or undertaken comparative studies between them. In this study we examined the extent, nature and determinants (i.e. risk factors) of violence against psychiatric nurses (n = 731) and psychiatrists (n = 320) working in the eight health care districts of Stockholm. These caregivers were assessed cross-sectionally by means of a questionnaire covering various areas (e.g. violence and work environment). The majority of the participants (85%) reported having been exposed to violence during their careers, with 57% being victimized in the past 12 months. Physical violence was common, and factors such as negative attitudes to work and diminished sense of autonomy were associated with an increased vulnerability to violence. Nurses and psychiatrists did not differ in violence variables. In spite of the weaknesses of the design (cross-sectional self-selecting sample), this study corroborates previous findings and identifies personal factors associated with violence that have received little attention in the literature (e.g. lack of respect for the organization of care).     

Winning the interviewing game

Those who don't "interview well" are not likely to receive the job offer, despite their qualifications. A job interview is actually a fierce competitive activity that offers only two grades: an A or F. By nature, physicians are competitive; they like to win. Infrequent interviewees are prone to making easily corrected mistakes, such as showing no enthusiasm or having poor eye contact. The key for interviewing success is preparation--doing research, developing a personal statement, and role-playing practice interviews. View the interview as a sales call whose bottom-line goal is to achieve an offer, or at least to let you leave with the option to return for future discussions.     

Organizational Learning in Global Purchasing: A Model and Test of Internal Users and Corporate Buyers*

This research examines a model centered on organizational learning in purchasing. Two different studies are conducted to test the hypotheses among purchasing users (Study 1) and buyers (Study 2). The user sample consists of users representing 355 strategic business units of a Fortune 500 multinational corporation. The buyer sample consists of corporate buyers of 200 multinational corporations drawn from the membership directory of the National Association of Purchasing Management (NAPM). In each study, the focus is on the learning relationships between corporate buyers and internal users in the purchasing organization. Based on the two studies, the results suggest that organizational learning in the purchasing process is influenced by the organizational culture factors of localness, transformational leadership, and openness. Organizational learning has a positive effect on information processing in the purchasing system, which, in turn, has a positive influence on the cycle time of the purchasing process.     

Communication after an integrity-based trust violation: How organizational account giving affects trust

How can an organization repair trust through communication after an ethical failure? This study examines how trust is repaired after an integrity-based trust violation using three different accounts: apology, excuse, and refusal. In our approach, we rely on two strands of attribution theory, which suggests that different attributions for responsibility and credibility affect trust. An experiment with n = 368 was conducted to explore trust repair effectiveness of apology versus refusal and apology versus excuse after an integrity-based trust violation. Results revealed apology as a double-edged sword; it repairs trust more successfully than refusal and excuse because it is evaluated as more credible. However, it is less successful than refusal and excuse because it is evaluated as more responsible.     

Change agent's contribution to recipients' resistance to change: A two-sided story

In the change management literature, most studies on recipients' resistance to change include only the views of agents or of recipients, thereby ignoring that these parties may have different perceptions. In this quantitative study, we include the perceptions of both parties in studying the recipients' resistance and the impact of the agent's leadership behavior. In a sample of 117 agent-recipients groupings, covering 110 different change projects in 90 organizations, we found that agents perceive higher levels of recipients' resistance than do the recipients themselves. Additionally, we found that agents who create space to enable recipients to think and act differently (by employing creating behavior) report higher levels of recipients' resistance, whereas recipients perceive their resistance to be lowered when agents facilitate an emotional connection to the change (framing behavior). The depth of the change appeared to moderate the relationship between agent's leadership behavior and recipients' resistance, indicating that agents and recipients differ in which change leadership behaviors they perceive as increasing or decreasing resistance at different levels of change depth. These findings imply to reconsider the relationship between agent and recipients and we propose some promising avenues for future studies in resistance research.