آیا ویژگی های فردی عملکرد مدیریت موجودی را تحت تاثیر می دهد؟بررسی موردی، هوش، شخصیت، علاقه و دانش

The purpose of this study is to investigate the influence of four personal traits (intelligence, knowledge, personality and interests) on performance in a structurally simple, yet dynamically complex inventory management task. We base our model on PPIK theory from cognitive psychology and ground the experiment we conduct on the tradition of dynamic decision making research. Findings are that intelligence is the strongest predictor of inventory management performance, while the analysis shows weaker but significant relations between the other traits and performance. Regarding interests, we find that a strong interest for social issues leads to higher cost and, thus, worse performance. A similar detrimental impact on performance has a personality that is open for new experiences. Implications for research comprise investigating the relationship between the four traits and accounting for different task complexities. While obviously intelligence or personality of inventory managers cannot easily be changed, this research can help identifying favorable combinations of psychological traits that can be used in personnel selection. The value of this paper lies in contributing to behavioral theory building in operations management by describing and interpreting the psychological foundations for one of the most notorious tasks: controlling a stock of finished products and adapting its inflow to its outflow.

Anecdotal evidence of poor inventory management (IM) performance can easily be found in everyday life, for instance, empty shelves in supermarkets or long waiting times till delivery for a fashionable good. Such shortages can severely damage earnings and raise costs. Excess inventories are, however, just as bad as stock-outs. Large inventories increase the working capital and the inventory risk. In consideration of a vast body of normative research that provides policies, models, and concepts to support decision making of inventory and purchasing managers (see, e.g., Williams and Tokar (2008), for a review), persisting poor inventory management performance (IMP) needs explanation. Behavioral research has identified people as one of the important reasons for IMP shortfalls. Although still relatively scarce (Bendoly et al., 2006), behavioral inventory and supply chain management (SCM) research has collected robust evidence of biases and heuristics that result in performance figures falling short of normative predictions. Contributions have been made from various research streams with different foci and methodologies (Bendoly et al., 2010). Research employing a cognitive psychology perspective mainly uses inventory models with purely exogenous demand as, for instance, the newsvendor model and a repeated measurement experimental design. Schweitzer and Cachon's (2000) seminal work reports suboptimal order quantities, which are explained with anchoring and insufficient adjustment and preferences to reduce ex-post inventory errors. Benzion et al. (2008) extend these findings by varying the demand distribution and Lurie and Swaminathan (2009) by showing that more frequent feedback does not necessarily increase performance. Croson et al. (2008) add to the literature by investigating the overconfidence bias in a newsvendor setting. System dynamics based research “investigates the system level effects of behavioral regularities” (Bendoly et al., 2010) and uses dynamically complex experimental devices as, for instance, the Beer Game (Croson and Donohue, 2006, Smith, 1982 and Sterman, 1989a) or feedback rich management flight simulators (e.g., Diehl and Sterman (1995)). This research shows that participants' IMP suffers systematically from both misperceptions of feedback structure and dynamics (Bendoly et al., 2010). Smith, 1982 and Sterman, 1989a suggests that deficient dynamic mental models that people use to guide their decisions are at the root of this type of misperceptions. Such deficient mental models include an event-based perspective, open-loop view of causality, insensitivity to non-linearities, inappropriate anchoring heuristics and misperceptions of time lags. More recent studies have extended this research stream to informational aspects (Croson and Donohue, 2003, Croson and Donohue, 2005 and Croson and Donohue, 2006) and single echelon supply chains (Bloomfield et al., 2007), where inter-echelon coordination problems are absent. Despite rather striking regularities in decision patterns and shortcomings observed across many studies, there is still considerable variability at the individual subject level. This individual heterogeneity has received scant attention from both system dynamics and cognitive psychology based IM research. Recent exceptions are Bolton et al. (2010) who investigate, how out-of-task experience matters, Moritz (2010) who reports a statistically significant relation from cognitive reflection to IMP, and De Vericourt et al. (2011) who report a significant gender effect. These studies indicate a growing research interest in the investigation of personality traits in an IM context. Our research pursues similar goals, but follows more closely a stream of behavioral research that has long considered personal traits of decision makers—that is psychological research on complex dynamic decision making (Ackerman and Kanfer, 1993, Booth Sweeney and Sterman, 2000, Digman, 1990, Dörner, 1980, Dörner, 1996 and Williams and Tokar, 2008). This research stream has produced relevant findings regarding the linkages between personal characteristics and performance in complex situations. Focusing on dynamic systems, it suggests that human beings have severe difficulties understanding and managing systems which are dynamically complex, that is, which are characterized by feedback, time delays, nonlinearities, and accumulation. For these dynamically complex tasks, elaborate and corroborated theories exist that relate intelligence, personality, interests and knowledge to decision making performance. Especially Ackerman's (1996) PPIK theory has been bolstered by many empirical studies (see, e.g., Wittmann and Hattrup (2004)). However, none of these psychological studies have addressed IM as such. In this research, we combine the three research lines mentioned above: behavioral IM and SCM research based upon either cognitive psychology perspective or on system dynamics, and psychological research on complex dynamic decision making. From the latter field we take the PPIK theory (Ackerman, 1996) and ask how much this theory can contribute to explaining differences in individual IMP (which have been substantiated by the first two research lines). Following experimental system dynamics and dynamic decision making research, we use a dynamically more complex inventory task—with feedback and delays—than in the newsvendor situation that is mostly used by behavioral IM research grounded in cognitive psychology. In our case, inventory managers have to review and decide periodically on production quantities that result—with some delay—in inventory inflows, which accumulate in the stock of inventory. They have to account not only for incoming orders but also for backlogged orders. And, importantly, they have to consider that customers react to bad service levels by decreasing their order rate and vice versa. We test if the PPIK theory can contribute to explaining individual IMP differences. Thus, the purpose of this paper is to address limitations mentioned by Moritz (2010) and De Vericourt et al. (2011) and find out whether intelligence, personality, interests, and knowledge determine performance in an IM task, with the ultimate goal to give recommendations what personal traits successful inventory managers should have. In order to operationalize the four personal traits, we use different standard psychological tests from the literature; we operationalize IMP by the degree of control performance participants demonstrate in a simulated inventory task in a controlled experiment. The paper contributes to theory in behavioral operations management in two ways. From a content perspective, this study describes and interprets the psychological foundations of one of the most notorious tasks in operations management: controlling a stock (e.g., of finished products, work in progress inventory, or raw materials) and adapting its inflow to its outflow with feedback being present. The inflows in such stocks could be production from upstream production stages or purchases; the outflow of these stocks can be, for instance, deliveries to customers or to further production stages; feedback could result in inflows being affected by outflows (with a time delay) and vice versa; for instance, late delivery outflows could reduce future incoming orders. From a methodological perspective, this study contributes to behavioral operations management by its prototypical use of validated psychological tests and of a dynamically complex decision experiment. The dynamic tasks used in these form of experiments resemble actual decision making in operations management substantially, for two reasons. Firstly, they require repeated decision making, not just one singular decision. Secondly, the state of the system, which they represent, depends on earlier decisions, i.e. the task evolves over time depending on participants' actions in the past. The paper continues in Section 2 with a more elaborated discussion of the theoretical background of this work, again focusing on behavioral operations management studies of IM and on complex problem solving from psychology. In the section after that, we present the experimental design and methods that are used; in particular, we describe the tests used to measure personal traits and which method is employed to derive IMP. In the fourth section, the results of the experiment conducted are shown, addressing the relation between personal traits and IMP. The paper concludes with a discussion of contributions and limitations of this research and outlines directions for further research.

To our knowledge this is the first study that relates a comprehensive picture of the psychological state of participants to their performance in an IM task with endogenously influenced demand, which meets all the criteria of a dynamically complex decision making task. Based on the set of personal traits offered by the PPIK theory, we investigated what characteristics good decision-makers in the area of IM possess. Intelligence and knowledge stand out as predictors of performance: both are persistently positively related to outcomes of the experiment in the regression models. In that sense, our results contribute to the literature about the influence of intelligence and knowledge on the capability of humans to solve complex problems. While findings have been mixed so far, for IM we achieved a rather unequivocal result of the positive influence of intelligence and knowledge. From a practical point of view, this suggests that persons that deal with IM tasks will benefit from being intelligent and having acquired knowledge in the wider field of that domain. From the perspective of the PPIK theory, testing in how far both can be substitutes of each other and whether intelligence must be invested in order to accumulate knowledge and how this relates to performance in IM tasks is an open question. Our results are less clear regarding interests and personality. For both, no findings in the literature exist that can be directly related to IM tasks. Thus, the linkage of our findings to the literature is difficult. Nevertheless, as in the study by Ackerman et al. (1995), we can conclude that the additional variance explained by including non-ability factors (so, personality and interests) is relatively low compared to the influence of intelligence and knowledge on performance. Regarding interests, being interested in social issues has a negative effect on IMP (and a technical interest seems to help, though not significantly in our study). One assumption in this regard might be that people with a strong interest in social affairs may simply find a rather technical and structured task like IM “too boring” and not challenging enough. Assumingly, they would have liked more to deal with “soft” issues that arise from the interactions of individuals or groups. Concerning personality, being open for new experiences also has a negative effect on IMP (as has extraversion, but not significantly so). It can be assumed that people sharing this openness are probably also more equipped to deal with innovative and unstructured situations than with the relatively structured procedures in IM. Openness to experience is described by McCrae (1994) as applying to people that “combine intellectual curiosity with broad interests, liberal views, adventurous tendencies and a need for variety” (p. 257). Presumably, such a personality trait simply does not fit the nature of the task demanded from participants in the experiment: repeatedly deciding on relatively structured issues, preferably in a “calm” way (if one wants to achieve high performance scores). From a methodological perspective, this study demonstrates that the PPIK theory is useful when doing research in the field of behavioral operations management. It allows capturing a rather comprehensive picture of the personal traits of decision makers. These traits can be related to performance in structurally simple but dynamically complex decision making tasks. The type of IM task that we employed proved useful to complement more frequently applied tasks as the newsvendor problem, adding feedback and endogenously generated demand which increased dynamic complexity. From a practical point of view, the negative relation of social interests and openness to IMP might indicate that some people have strengths that cannot easily be aligned to the demands of IM. While one could argue that an interest in the sort of processes that are standard in IM can be acquired and others could be “unlearned”, the personality of a human being is a rather enduring trait and cannot be changed in the short-term. Thus, individuals that continuously seek new experiences (and are extravert) should rather not be given an IM task. N is minimally 119 in this study, which as such not a small number. However, for more sophisticated analysis instruments, like covariance-based structural equation modeling, 200 cases are recommended as minimum. With such methods, a more comprehensive picture of the relationships between the four personal traits and IMP would be possible, for instance permitting to investigate relationships between the four PPIK traits and by this also contribute to the psychological literature on the relationship between these personal traits. The regression analysis we conducted is able to explain about one quarter of the variance in participants' results of the IM task (adjusted R-squared for Model E). While as such this is not a low percentage, it shows that—despite our efforts to operationalize participants' traits in an as comprehensive was as possible by using the PPIK model—obviously many other factors influence performance in IM, of which task characteristics might be crucial (besides features of the context in which the experiment was conducted; cf. Größler (2004)). There is, of course, a certain variety of real-life IM scenarios. Customer, supplier, and production characteristics can differ in many ways from the scenario implemented in the IM microworld Pugepo used in this research. Cost parameters, lead times, and capacity restrictions may vary. Therefore, another sensible way to extend this study would be to use IM tasks of (1) different complexity, since this might be a strong moderating factor, (2) different demand patterns, since uncertainty in demand might have a strong impact, and (3) different cost and revenue parameterization, since the economic valuation of stock-outs or excess inventory might be influencing as well.