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Based on a conceptual framework of the linkages between strategic manufacturing goals and complexity, the purpose of this paper is to investigate adaptation processes in manufacturing firms to increasing external complexity.

Hypotheses are tested with statistical analyses (group comparisons and structural equation models) that are conducted with data from the third round of the International Manufacturing Strategy Survey.

The study shows that manufacturing firms face different degrees of complexity. Firms in a more complex environment tend to possess a more complex internal structure, as indicated by process configuration, than firms in a less complex environment. Also depending on the degree of complexity, different processes of adaptation to increases in external complexity are initiated by organisations.

Research studies taking into account the dynamics of adaptation processes would be helpful in order to draw further conclusions, for instance, based on longitudinal analyses or simulation studies.

Depending on the level of complexity a firm has been confronted with in the past, different adaptation processes to further growing complexity can be initiated. Firms in high complexity environments have to re‐configure their strategic goals; firms in low complexity environments have to build‐up internal complexity to cope with demands from the outside.

The paper distinguishes between adaptation processes in low and high complexity environments and provides explanations for the differences.

The purpose of this paper is to propose that the effectiveness of organizational design-manufacturing integration (ODMI) practices is contingent upon the degree of complexity of the manufacturing environment. The paper submits that the level of use of ODMI ought to match the level of complexity of the manufacturing environment. The paper puts forward the hypothesis that when a misfit occurs between ODMI and complexity (high use of ODMI practices in low complexity environments or low use of ODMI practices in high complexity environments) manufacturing operational performance declines.

The paper tests the hypothesis based on a survey database of 725 manufacturers from 21 countries. The measurement model was assessed with confirmatory factor analysis and the hypothesis was tested with linear regression.

A misfit between the level of ODMI use (job rotation and co-location) and manufacturing complexity (product and process complexity) has a negative effect on manufacturing operational performance dimensions of quality, delivery and flexibility. Post hoc analyses also suggest that firms that operate in different environments in what concerns the rate of change in process technologies suffer differentiated negative impacts of ODMI-complexity misfit.

Future studies could extend this research to other dimensions of design-manufacturing integration, such as technological practices.

Manufacturers with high levels of complexity should invest strongly in ODMI practices. However, manufacturers with low levels of complexity should invest in these practices with caution since the expected payoffs may not outweigh the effort.

The study assesses fit as a simultaneous set of contingency factors, applying profile-deviation analysis to ODMI and operational performance relationships. By focusing on plant-level manufacturing complexity, this study complements existing studies of product development complexity which tend to focus on project-level complexity.

This study aims to draw the attention of business process management (BPM) research and practice to the textual data generated in the processes and the potential of meaningful insights extraction. The authors apply standard natural language processing (NLP) approaches to gain valuable knowledge in the form of business process (BP) complexity concept suggested in the study. It is built on the objective, subjective and meta-knowledge extracted from the BP textual data and encompassing semantics, syntax and stylistics. As a result, the authors aim to create awareness about cognitive, attention and reading efforts forming the textual data-based BP complexity. The concept serves as a basis for the development of various decision-support solutions for BP workers.

The starting point is an investigation of the complexity concept in the BPM literature to develop an understanding of the related complexity research and to put the textual data-based BP complexity in its context. Afterward, utilizing the linguistic foundations and the theory of situation awareness (SA), the concept is empirically developed and evaluated in a real-world application case using qualitative interview-based and quantitative data-based methods.

In the practical, real-world application, the authors confirmed that BP textual data could be used to predict BP complexity from the semantic, syntactic and stylistic viewpoints. The authors were able to prove the value of this knowledge about the BP complexity formed based on the (1) professional contextual experience of the BP worker enriched by the awareness of cognitive efforts required for BP execution (objective knowledge), (2) business emotions enriched by attention efforts (subjective knowledge) and (3) quality of the text, i.e. professionalism, expertise and stress level of the text author, enriched by reading efforts (meta-knowledge). In particular, the BP complexity concept has been applied to an industrial example of Information Technology Infrastructure Library (ITIL) change management (CHM) Information Technology (IT) ticket processing. The authors used IT ticket texts from two samples of 28,157 and 4,625 tickets as the basis for the analysis. The authors evaluated the concept with the help of manually labeled tickets and a rule-based approach using historical ticket execution data. Having a recommendation character, the results showed to be useful in creating awareness regarding cognitive, attention and reading efforts for ITIL CHM BP workers coordinating the IT ticket processing.

While aiming to draw attention to those valuable insights inherent in BP textual data, the authors propose an unconventional approach to BP complexity definition through the lens of textual data. Hereby, the authors address the challenges specified by BPM researchers, i.e. focus on semantics in the development of vocabularies and organization- and sector-specific adaptation of standard NLP techniques.

The purpose of this paper is to integrate process complexity as an object of analysis within effective quality management (QM).

This paper systematically analyzes different conceptions of complexity theory and characterizes process complexity from a QM perspective producing new insights how to address process complexity for continuous improvement.

The authors identified and specified four complexity characteristics, which we integrated in a holistic process complexity model (PCM). The author further developed the idea of internal and external process complexity and demonstrated that internal complexity needs to balance external complexity. Based on the PCM, internal process complexity can be analyzed and suitable management approaches can be selected, while conventional QM practices showed to be inefficient or even contra-productive when applied in the context of process complexity.

This research is adapted to fit the needs of production processes. The PCM is designed from a QM perspective.

The developed model allows companies to specify and characterize process complexity in order to reflect on the appropriateness of their process management approaches. Furthermore, it gives an additional perspective on process analysis for tapping the full potential of process improvement programs.

This paper combines complexity theory with QM.

The purpose of this paper is to investigate the effects of self-efficacy, process feedback and task complexity on decisions by managers to continue or discontinue a new product after receiving negative performance feedback.

This paper uses a classroom experiment design and uses logistic regression and a chi-square test to analyze the data.

The findings of this paper show that self-efficacy, process feedback and task complexity have not only main effects but also interactive effects on managers’ go or no-go decisions; further, the main effects are mediated by interactions. The effect of self-efficacy is moderated by process feedback and task complexity. Process feedback and task complexity also have an interactive effect on decisions about new products by decision-makers.

This paper extends the theory of escalation of commitment (EOC) by showing that self-efficacy, process feedback and task complexity can influence decision-makers’ go or no-go decisions after they have received negative performance feedback.

This paper provides useful guidelines for managers on how to reduce the likelihood of EOC.

The originality and value of this paper lie in its being the first to examine the effects of process feedback and task complexity on the EOC.

The purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.

To test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).

Several important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.

This research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

Complexity has been called the 21st-century supply chain (SC) challenge. Most SC managers view it as a necessary evil, ever-present, costly and tough to manage, and few prioritize it. Still, anecdotes suggest some leverage it to drive operational excellence. This study aims to explore how they do it, delving into the development of a complexity management capability, under what circumstances it emerges and its effect on competitiveness.

To better understand why, and how, companies develop (or not) a distinctive SC complexity management capability, this study employed an inductive study of 10 leading European companies, each operating a complex SC.

Although SC complexity raises costs, increases disruptions and makes decision-making difficult, few companies have made complexity management a priority. Among those, most focus on reducing or absorbing complexity to improve operational excellence. A few invest to develop a distinctive SC complexity management capability. They manage complexity for market success. The interaction among competitive pressures, managerial attitudes and investments delineate a dynamic capability development process.

Despite extensive research on complexity drivers, the tools used to manage SC complexity and the impact of SC complexity on performance, the interplay among factors that promote, or hinder, the development of an SC complexity capability continues to be poorly understood. By mapping the complexity capability development process, this study explicates a more nuanced approach to managing SC complexity that can yield a competitive edge.

SC complexity prevails because the dynamic, iterative complexity capability development process is overlooked. Managers can use the complexity capability roadmap to assess the cost/benefits of pursuing a distinctive complexity management capability more accurately.

This study demystifies the development of a complexity management capability, showing how some companies develop the capability to distinguish between value-added and value-dissipating complexity and thus become empowered to leverage SC complexity for competitive advantage.

The purpose of this paper is to present a structure for identifying complexity that is not needed in an enterprise, and describe a methodology for eliminating it. Whether it is process complexity, product complexity, or organizational complexity, investments in managing higher levels of complexity often offer businesses significant value by enabling them to offer more and better products and services to a broader range of customers. However, along with higher levels of complexity has come an increased requirement to distinguish between that complexity which is needed and that which is needless.

This paper first presents a structure for categorizing different kinds of complexity, with a detailed focus on needless complexity that is categorized into four types. Next, specific factors are developed that can be used to identify needless complexity in an organization. Finally, a methodology is presented that organizations can utilize in order to eliminate needless complexity.

Needless complexity can be created where it never should have existed in the first place, and other times needless complexity exists as an historical relic left over from a time when it actually was needed. Using a structured approach, needless complexity can be identified and eliminated to yield significant business benefits.

This paper provides a framework for differentiating needless complexity from needed complexity, and assessing the landscape of needless complexity in an organization. It also provides an approach for identifying opportunities to reduce needless complexity using the needless complexity diagnostic.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

In the era of industry 4.0, managing the design is a challenging mission. Within a dynamic environment, several disciplines have adopted the complex adaptive system (CAS) perspective. Therefore, this paper aims to explore how we may deepen our understanding of the design process as a CAS. In this respect, the key complexity drivers of the design process are discussed and an organizational decomposition for the simulation of the design process as CAS is conducted.

The proposed methodology comprises three steps. First, the complexity drivers of the design process are presented and are matched with those of CAS. Second, an analysis of over 111 selected papers is presented to choose the appropriate model for the design process from the CAS theory. Third, the paper provides methodological guidelines to develop an organizational decision support system that supports the complexity of the design process.

An analysis of the key drivers of design process complexity shows the need to adopt the CAS theory. In addition to that, a comparative analysis between all the organizational methodologies developed in the literature leads the authors to conclude that agent-oriented Software Process for engineering complex System is the appropriate methodology for simulating the design process. In this respect, a system requirements phase of the decision support system is conducted.

The originality of this paper lies in the fact of analysing the complexity of the design process as a CAS. In doing so, all the richness of the CAS theory can be used to meet the challenges of those already existing in the theory of the design.