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Operations strategy is formed via complex processes that transpire in multiple directions at multiple organizational levels. While most previous studies focus on the “macro-level” process of strategy formation from the dominant top-down perspective, this study investigates the “micro-level” process of strategy formation that governs interactions among competitive priorities, objectives, and action plans within operations. Using 111 (59 top-down and 52 bottom-up) action plans collected from six German manufacturing plants, we build on Kim and Arnold's (1996) framework and propose an integrated process model of operations strategy formation that encompasses both top-down planning and bottom-up learning. We also identify a contingency factor that affects their balance: centralized versus decentralized organizational structure. Finally, based on the analysis of their respective strategic content, we provide evidence concerning the complementary roles of top-down and bottom-up action plans in operations strategy. [ABSTRACT FROM AUTHOR]

Abstract: Behavior is driven not only by individual “(economically) rational” deliberation, but also by shortcuts (decision biases) and by social preferences (the achievement of status, reciprocal relationships, and group identity). An important aspect of these behavioral drivers is that they may operate (at least in part) through emotions. Emotions influence behavior in ways that are relevant to performance in processes; for example, anger prompts employees to refuse cooperation, fear inhibits workers’ willingness to take initiatives (for example, in continuous improvement), and shame motivates them to change behavior. This study provides experimental evidence that the social preferences systematically trigger emotions. This happens not “linearly” (more achievement of a social preference might be expected to cause more positive emotions), but rather in more complex patterns that regulate social relationships. Arbitrary (but earned) status signals trigger pride, while status achievement from being lucky does not. An externally (not by an own fault) caused status loss triggers anger and disgust. Not receiving cooperation by another person triggers anger, but more so if the cooperation failure violates a reciprocity expectation; the happiness form receiving cooperation by another person is attenuated by guilt and sadness is the subject itself has previously refused cooperation to the other person. Events happening to salient in-group members trigger emotions as if they happened to subjects directly, including if the in-group member behaves in an embarrassing way. These results are relevant for front-line managers because they help predicting and interpreting emotional reactions of employees: awarding or withholding status, offering or demanding reciprocity, and the creation of in-groups have emotional effects that depend on the context in predictable ways. Our results also contribute to theory by connecting literatures on emotions, decision making, and behavioral operations. [Copyright &y& Elsevier]

New ventures often do not correctly foresee real market opportunities or the best way to address them. How to cope with unforeseen, unpredictable factors, also referred to as unknown unknowns, is critical for new ventures. Findings in the fields of innovation and project management have shown that dealing with the unpredictable requires management approaches different from those used for classical plan-and-achieve-the-target projects. Management approaches for novel initiatives include a combination of trial-and-error learning (i.e., flexible redefinition of the new venture business model as new information emerges) and selectionism (i.e., running multiple parallel trials and choosing the best performing approach ex post). The management approach must be chosen when the venture is set up. This requires a venture management team to diagnose at the outset whether unknown unknowns are present (or possible), although unknown unknowns cannot be identified initially by definition because they emerge over time. Anecdotal testimony from experienced venture managers and project managers suggests they have a feeling for where their knowledge is limited. However, such a claim is controversial. Some researchers think the concept of diagnosing unforeseeable influence factors is an oxymoron. Thus, the research question in this article is this: How can unforeseeable influence factors in a new venture be diagnosed at the outset? Research to date has insufficiently addressed the a priori identification of the type of uncertainty faced by a new venture. Based on models from decision theory, this article suggests dividing the overall problem of structuring the venture into subproblems for which the management team can identify knowledge gaps. Using a case study, the article describes how knowledge gaps were identified for the subareas of a new venture in a real situation and how this diagnosis was used to correctly identify the areas where unknown unknowns lurk. These areas were managed in a different way (i.e., with learning and experimentation) than the other subproblems (i.e., with targets and deadlines). As a result, the venture could successfully respond to unforeseeable events. The results of this study suggest that a decomposition of the overall venture management problem into subproblems is feasible and natural to managers, that a qualitative assessment of knowledge gaps and vulnerability to unknown unknowns is possible, and that a structured, process-like approach can be used to identify subproblems, to determine their uncertainty profiles, and to update the uncertainty profiles. These results are immediately useful to venture management and venture capitalists in setting up the venture's structure for effective response to uncertainty. The results advance research about uncertainty management by offering a systematic set of questions for the diagnosis of unknown unknowns before they can be formally described. The usefulness of this process can be tested further in more formal empirical research. [ABSTRACT FROM AUTHOR]

We examine emotional algorithms and their role in a fundamental dilemma that confronts human groups—whether actors should take care of ‘me’ (compete) or take care of ‘we’ (cooperate). We argue that human emotions, triggered in algorithmic fashion through four common, although culturally specified, mechanisms, powerfully direct humans to compete or cooperate. Drawing on evolutionary psychology, we first define and characterize these hard-wired emotional algorithms, presenting evidence for their independent influence. Their regulatory influence on human groups, however, can only be appreciated once we examine them as a system. We show how, as a system, these algorithms help explain the dynamic balance that members of human groups can (and often must) achieve between competition and cooperation. We derive three propositions regarding how these algorithms play out in groups. We suggest that understanding these dynamics can help leaders better manage cooperation and competition in organizational groups. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Engineering change orders (ECO5) are part of almost every development process, consuming a significant part of engineering capacity and contributing heavily to development and tool costs. Many companies use a support process to administer ECOs, which fundamentally determines ECO costs. This administrative process encompasses the emergence of a change (e.g., a problem or a market-driven feature change), the management approval of the change, up to the change's final implementation. Despite the tremendous time pressure in development projects in general and in the ECO process in particular, this process can consume several weeks, several months, and in extreme cases even over 1 year. Based on an in-depth case study of the climate control system development in a vehicle, we identify five key contributors to long ECO lead times: a complex approval process, snowballing changes, scarce capacity and congestion setups and batching, and organizational issues. Based on the case observations, we outline a number of improvement strategies an organization can follow to reduce its ECO lead times. [ABSTRACT FROM AUTHOR]

Engineering change orders (ECOs) are important drivers of development costs and lead time. This article analyzes the process of administering engineering change orders in the case of the climate control system development within a large vehicle development project. This administrative process encompasses the emergence of a change (e.g., a problem or a market-driven feature change), its management approval, and final implementation. Despite strong time pressure, this process can take several weeks, several months, and, in extreme cases, even over 1 year. Such a long lead time is especially remarkable as the actual processing time for the change typically does not exceed 2 weeks. Based on our case study, we develop an analytical framework that explains how such an extreme ratio between theoretical processing time and actual lead time is possible. The framework identifies congestion, stemming from scarce capacity coupled with processing variability, as a major lead time contributor. We outline five improvement strategies that an organization can use in order to reduce ECO lead time, namely, flexible capacity, balanced workloads, merged tasks, pooling, and reduced setups and batching. [ABSTRACT FROM AUTHOR]

The financial value of research projects is difficult to assess because they are highly uncertain. Often, the result is either an overly conservative approach to strategic innovation, based on net present value analyses, or an overly aggressive approach based on optimistic qualitative portfolios. R&D project evaluation requires recognizing threats as well as opportunities from uncertain events, and incorporating flexibility in managerial action in response to them. Real options pricing analysis is a widely discussed tool for evaluating such managerial flexibility. The limitation of options pricing lies in its requirement for complete financial markets, in which a replicating asset can be found that reproduces (or, at least, is correlated with) the project's payoffs in all possible states of the world. However, the major risks of research projects are typically project specific and cannot be replicated in external markets. In this situation, a decision tree is a better tool to represent managerial options during execution of the project, and to evaluate its value. A decision tree is equivalent to options pricing for risks that can be priced in the financial markets (if trading of securities is explicitly included), and moreover, it can incorporate risks and flexibility that are not traded in financial markets. Using decision trees, we demonstrate a quantitative evaluation of compound growth optionsfrom research at BestPharma, a large international pharmaceutical company. A growth option is a future opportunity that may arise from a current R&D investment. The growth option may not be related to the primary purpose of the R&D project, or not even be directly foreseeable. Kester (1984) has argued that growth options may account for a large part of project value. BestPharma faced the problem of choosing among several strategic research initiatives. They developed a decision tree representation of the projects, which helped to provide transparency about projec... [ABSTRACT FROM AUTHOR]