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Perceived environmental uncertainty (PEU) is a foundational concept in organization studies. The PEU typologies used in organizational research were developed using private sector managers. But, do public sector managers perceive the same uncertainty sources? We asked public sector managers in Hong Kong to identify and group uncertainty sources facing their organizations. Multidimensional scaling and cluster analysis yielded classes of uncertainty sources that differ from those developed using private sector managers.

The purpose of this paper is to empirically investigate effective management strategies for 14 sources of supply chain uncertainty, with a particular emphasis on uncertainties or strategies that involve ethical issues.

Manufacturing strategy theory, underpinned by alignment and contingency theory, is used as the theoretical foundation. Multi-case study data are collected from 12 companies in the Indonesian food industry, including four focal manufacturers, four first-tier suppliers, and four first-tier customers (retailers).

Within the context of appropriately aligned management strategies to address 14 sources of uncertainty, three ethical issues are empirically identified: first, collusion amongst suppliers to ration supplies and increase prices; second, unethical influences on government policy; and third, “abuse” of power by large retailers at the expense of smaller competitors. Joint purchasing is argued to be a key strategy for combatting the first of these ethical issues.

The study is limited to the Indonesian food industry, and so further research is needed in other cultures/contexts.

Management strategies that aim to reduce an uncertainty at its source lead to better overall supply chain performance than strategies that merely cope with uncertainty, which only have an impact on firm-level performance.

The ethical issues identified have implications for fair negotiations between customers and suppliers.

This study is unique in its in-depth case study-based empirical investigation of the management of multiple supply chain uncertainties; and in its discussion of ethical issues in this context.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

The purpose of this paper is to present a methodology to represent the uncertainty generated in performance measures (PMs) during the operational step or “use step” of the performance measurement process (PMP). The different steps of the methodology are described and exemplified through an application example.

An index that reflects the level of uncertainty originated by the factors and the strength of their inter-relationships is developed through the use of graph theory. A graph is developed considering all sources or factors of uncertainty that may be present in this process. Based on the graph, the methodology includes the use of a matrix and the determination of the associated permanent function which is used for determining the uncertainty index.

During the development of the methodology, it was found that the use of a scale that includes zero for assigning values of the elements of the matrix is not appropriate when using graph theory and permanent function calculation, since in this case the permanent function is insensible to changes in some matrix elements.

This paper identifies all the sources that can affect the quality of performance measurement values during the operational step of the PMP and proposes a method to characterize the strength of this uncertainty. Beyond alerting decision makers to the level of uncertainty associated with a PM, it also allows defining appropriate actions to improve PMs’ quality.

Dynamic demands and constraints imposed by a rapidly changing business environment make it increasingly necessary for companies in the food supply chain to cooperate with each other. The main questions individual (food) companies face are whether, why, how and with whom they should start supply chain management activities. Presents a qualitative research method for analyzing a supply chain network and for identifying effective chain redesign strategies. Presents a generic list of supply chain redesign strategies based on a multi‐disciplinary literature review. Proposes that in order to identify the most effective strategies in a specific chain scenario one should focus on the identification and management of the sources of uncertainties in the supply chain’s decision‐making processes. The application of the research method in three food supply chains resulted in a valuable tool that can be used in supply chain redesign projects, as it indicates potentially effective redesign strategies when a specific source of uncertainty is encountered in a supply chain.

This paper aims to investigate the nature and prevalence of environmental scanning as practiced by organizations in the United Arab Emirates (UAE) and explore the impact of perceived uncertainty on the way various information sources are used in managerial decision-making. Specifically, by targeting firms in the region, the paper examines whether the degree and frequency of scanning activities influence the relationship between perceived uncertainty and the use of environmental information in making strategic decisions.

The study uses a cross-sectional research design, with self-administered questionnaires as the main mode of data collection, and uses quantitative data analysis tools to answer the research questions.

The study finds that perceived environmental uncertainty significantly influences scanning efforts in an organization, which in turn influence the use of environmental information in decision-making. Additionally, findings suggest that while perceived source quality significantly influences frequency of scanning, the influence of perceived source accessibility on scanning is statistically insignificant.

The results of this study have several implications for academics in the field of strategic management and industry professionals involved in long range planning. Results indicate that scanning activities should be more extensive and frequent when perceived uncertainty is high and indicates the potential value of timely market research to assess demand, gather consumer feedback and respond to changing needs of the customers.

The study addresses an identified gap in the literature and adds to the existing body of work by exploring organizational practices in a unique cultural context and assessing a conceptual model that links perceived uncertainty and environmental scanning to strategic management practices.

This paper concerns with the measurement uncertainty estimation in the analog‐to‐digital conversion‐based instruments. By using an ad hoc developed software tool, the Monte Carlo method is applied in order to assess the uncertainties associated with the measurement results, overcoming the possible inapplicability of the pure theoretical approach prescribed in the ISO – “Guide to the Expression of Uncertainty in Measurement”. By implementing the software tool in the measurement instruments, the proposed approach can be utilized in order to make the instrument itself able to auto‐estimate the measurement uncertainties.

Many researchers have studied manufacturing flexibility, but supply chain flexibility is still an under‐investigated area. This paper aims to focus on supply flexibility, the aspects of flexibility related to the upstream supply chain. Its purpose is to investigate why and how firms increase supply flexibility.

An exploratory multiple case study was conducted. Seven Spanish manufacturers from various sectors (automotive, apparel, electronics and electrical equipment) were analysed.

The results show that firms need supply flexibility for a number of important reasons (manufacturing schedule fluctuations, JIT purchasing, manufacturer slack capacity, low level of parts commonality, demand volatility, demand seasonality and forecast accuracy), and that companies increase this type of flexibility by implementing two main strategies: “improved supplier responsiveness” and “flexible sourcing”. The results also suggest that the supply flexibility strategy selected depends on the type of uncertainty (mix, volume or delivery).

This paper has the limitations common to all case studies, such as the subjectivity of the analysis, and the questionable generality of the results. Moreover, there may be some sort of country bias because only Spanish firms have been analysed.

The study contributes to the existing literature by empirically investigating the main reasons for companies needing to increase supply flexibility and how they increase this flexibility, and suggesting some factors that could influence the selection of a particular supply flexibility strategy.

This paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (u_RP).

Firstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation of u_RP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of the u_RP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions to u_RP.

Results show that the proposed method can reasonably and objectively estimate the u_RP of the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, the u_RP of the selected industrial robot can be restricted by using the results of its key factors on u_RP.

This paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting the u_RP and thus useful in determining whether the RP of a tested industrial robot meets its requirements.

The aim of this paper is that of individualizing a framework for the management of uncertainty in supply chain finalized to reduce the firm risks.

Since a way for reducing the damages deriving from uncertainty sources is increasing the level of flexibility inside the supply chain, and the real option theory allows the increase of the flexibility level, in order to achieve the aim of this work, we utilize the real options theory to coverage of one or more risks inside the supply chain.

A useful theoretical framework has been individualized enabling the selection of possible options to protect the firm against the risk originating from every source of uncertainty. In particular, on two types of risks, using Matlab software, a test has been conducted that proves the ability of the outsource option to cover risks under examination.

In the paper a framework providing useful information for the supply chain management is presented.

The paper attempts to provide an original tool for the risks management deriving from production activities inside a supply chain.