Risk assessment based on a STPA–FMEA method: A case study of a sweeping robot

Despite rapid developments in the quality and safety of consumer products, the rise of intelligent household appliances, such as sweeping robots, has introduced new safety concerns. Considering “person–product–environment” elements and the complex systems of emerging consumer products, this study presents a new method of risk assessment for consumer products: systems theoretic process analysis (STPA)–failure mode and effects analysis (FMEA). As a case study, this method is applied to the safety control of a sweeping robot. The results suggest that this method can identify all the possible failure modes and injury scenarios among the product components, and the safety constraints in the hierarchical control structure of the interactive system. Moreover, the STPA–FMEA method combines user and environmental factors with the value of product risk events, based on the risk priority number (RPN). This provides an accurate and orderly system to reduce or eliminate the root causes of accidents and injuries. Finally, analysis of unsafe control behavior and its causes can be used to suggest improved safety constraints, which can effectively reduce the risk of some injury scenarios. This paper presents a new method of risk assessment for consumer products and a general five-level complex index system.     

Stochastic Judgments in the AHP: The Measurement of Rank Reversal Probabilities

This paper presents a methodology for analyzing Analytic Hierarchy Process (AHP) rankings if the pairwise preference judgments are uncertain (stochastic). If the relative preference statements are represented by judgment intervals, rather than single values, then the rankings resulting from a traditional (deterministic) AHP analysis based on single judgment values may be reversed, and therefore incorrect. In the presence of stochastic judgments, the traditional AHP rankings may be stable or unstable, depending on the nature of the uncertainty. We develop multivariate statistical techniques to obtain both point estimates and confidence intervals of the rank reversal probabilities, and show how simulation experiments can be used as an effective and accurate tool for analyzing the stability of the preference rankings under uncertainty. If the rank reversal probability is low, then the rankings are stable and the decision maker can be confident that the AHP ranking is correct. However, if the likelihood of rank reversal is high, then the decision maker should interpret the AHP rankings cautiously, as there is a subtantial probability that these rankings are incorrect. High rank reversal probabilities indicate a need for exploring alternative problem formulations and methods of analysis. The information about the extent to which the ranking of the alternatives is sensitive to the stochastic nature of the pairwise judgments should be valuable information into the decision-making process, much like variability and confidence intervals are crucial tools for statistical inference. We provide simulation experiments and numerical examples to evaluate our method. Our analysis of rank reversal due to stochastic judgments is not related to previous research on rank reversal that focuses on mathematical properties inherent to the AHP methodology, for instance, the occurrence of rank reversal if a new alternative is added or an existing one is deleted.     

Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids*

Despite the development of increasingly sophisticated and refined multicriteria decision-making (MCDM) methods, an examination of the experimental evidence indicates that users most often prefer relatively unsophisticated methods. In this paper, we synthesize theories and empirical findings from the psychology of judgment and choice to provide a new theoretical explanation for such user preferences. Our argument centers on the assertion that the MCDM method preferred by decision makers is a function of the degree to which the method tends to introduce decisional conflict. The model we develop relates response mode, decision strategy, and the salience of decisional conflict to user preferences among decision aids. We then show that the model is consistent with empirical results in MCDM studies. Next, the role of decisional conflict in problem formulation aids is briefly discussed. Finally, we outline future research needed to thoroughly test the theoretical mechanisms we have proposed.     

The Effects of Process and Outcome Similarity on Users' Evaluations of Decision Aids*

Decision aids (DA) used in online shopping contexts have been shown to improve users' product choices. Given that previous research (e.g., Byrne & Griffitt, 1973 ) has demonstrated the positive effects of perceived similarity on an individual's evaluation of others, this study investigates the effects of users' perceived similarity with a DA on their evaluations of that DA. More specifically, we investigate the effect of users' perceptions of the similarity between their own decision process and that followed by the DA to arrive at a recommendation (decision process similarity), as well as the similarity between the recommendations made by the DA and users' initial choices (outcome similarity), on their evaluations of the DA's usefulness and trustworthiness. The results of this study show that perceived process similarity exerts positive and significant effects on users' perceptions of the DA's usefulness and trustworthiness. However, the effects of perceived outcome similarity on trust are completely mediated by perceived process similarity. It is also observed that the level of the user's domain knowledge moderates the effects of perceived decision process similarity on both perceived usefulness and trustworthiness. These results have implications for DA design. It is important that designers consider the process by which users make decisions for themselves and align the DA's decision process with those of the user's, especially for the novice user. The full mediation of the effect of outcome similarity on trust by process similarity highlights how a similar decision process can mitigate some of the negative effects of outcome dissimilarity.     

A fuzzy set approach for R&D portfolio selection using a real options valuation model

Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero–one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.     

An Application of the AXIS Solution Framework to Multiple Objective Aggregate Production Planning

This paper provides data on the first application of a prototype of the AXIS solution framework. AXIS (algorithms combined with knowledge systems in an interactive sequence) is a framework for interactively combining structured algorithms that seek a best solution with knowledge-based expert systems that seek expert heuristic solutions. This paper tests the framework using an interactive multiple objective integer programming algorithm combined with heuristics taken from the domain of aggregate production planning. The results indicate the AXIS framework can be successful in generating high quality solutions, in vastly reduced solution times compared to the structured algorithms, at much lower costs compared to the expert heuristics working alone.     

The Effects of Interruptions,Task Complexity,and Information Presentation on Computer‐Supported Decision‐Making Performance

Interruptions are a frequent occurrence in the work life of most decision makers. This paper investigated the influence of interruptions on different types of decision‐making tasks and the ability of information presentation formats, an aspect of information systems design, to alleviate them. Results from the experimental study indicate that interruptions facilitate performance on simple tasks, while inhibiting performance on more complex tasks. Interruptions also influenced the relationship between information presentation format and the type of task performed: spatial presentation formats were able to mitigate the effects of interruptions while symbolic formats were not. The paper presents a broad conceptualization of interruptions and interprets the ramifications of the experimental findings within this conceptualization to develop a program for future research.     

Decision Support by Interval SMART/SWING—Incorporating Imprecision in the SMART and SWING Methods

Interval judgments are a way of handling preferential and informational imprecision in multicriteria decision analysis (MCDA). In this article, we study the use of intervals in the simple multiattribute rating technique (SMART) and SWING weighting methods. We generalize the methods by allowing the reference attribute to be any attribute, not just the most or the least important one, and by allowing the decision maker to reply with intervals to the weight ratio questions to account for his/her judgmental imprecision. We also study the practical and procedural implications of using imprecision intervals in these methods. These include, for example, how to select the reference attribute to identify as many dominated alternatives as possible. Based on the results of a simulation study, we suggest guidelines for how to carry out the weighting process in practice. Computer support can be used to make the process visual and interactive. We describe the WINPRE software for interval SMART/SWING, preference assessment by imprecise ratio statements (PAIRS), and preference programming. The use of interval SMART/SWING is illustrated by a job selection example.     

A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods*

Often, data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights on the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These methods are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.     

Intersystem Common Cause Analysis of a Diesel Generator Failure

The methodology and results reported in this paper are based on an analysis of a hypothetical accident occurring in a two unit power plant with shared systems (i.e., the diesel generator, the emergency service water, and the residual heat removal service water systems). The accident postulated is a loss of coolant accident (LOCA) in one out of two nuclear units in conjunction with a loss of offsite power (LOOP) and a failure of one out of four diesel generators to start. To analyze the intersystem effects, we needed to develop and apply a new methodology, intersystem common cause analysis (ICCA). The ICCA methodology revealed problems which were not identified by the traditional intrasystem failure modes and effects analysis (FMEA) performed earlier by the design teams. The first potential problem arises if one unit experiences a LOCA and diesel generator failure while one loop of its residual heat removal system is in the suppression pool cooling mode (SPCM); in this event, it is likely that minimum emergency core cooling system (ECCS) requirements will not be met. The second potential problem arises if a diesel generator fails while both units are simultaneously subjected to a controlled forced shutdown (a LOCA need not be postulated for either unit); in this event, it is likely that one unit will be required to use a heat removal path identified as off-normal in the final safety analysis report (FSAR) for the two unit plant. These and other potential concerns identified through application of the ICCA presented here were resolved early in the design phase.     

Automating Knowledge Acquisition and Refinement for Decision Support: A Connectionist Inductive Inference Model*

An important application of expert systems technology is to provide support for nonstructured decision making. Usually, nonstructured decision making is characterized by heavy reliance on heuristic knowledge, which is very difficult to articulate or document, and therefore traditional knowledge acquisition approaches are not very successful. The quality and effectiveness of an expert system supporting unstructured decision making is affected when traditional knowledge acquisition approaches are used. To alleviate this problem a model is proposed that combines inductive inference and neural network computing, and an example is presented that illustrates the potential of this model in unstructured decision support.     

Combining Neural Networks and Statistical Predictions to Solve the Classification Problem in Discriminant Analysis*

A number of recent studies have compared the performance of neural networks (NNs) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation.     

Failure Modes and Effects Analysis: An Evaluation of Group versus Individual Performance

Failure modes and effects analysis (FMEA) is one of the most frequently used tools in process and product design: it is used in quality and reliability planning, and event and failure mode analysis. It has a long history of use and is a formally prescribed procedure by a number of prominent standards organizations. In addition, it's popular use has evolved as a less formal and widely interpreted tool in the area of Lean/Six Sigma (LSS) process improvement. This paper investigates one of the most important issues related to FMEA practice—the quality of individual vs. group performance in ranking failure modes. In particular, we compare FMEA rankings generated by: (i) individuals, (ii) group consensus, and (iii) non‐collaborative aggregation of group input (a synthesized group ranking). We find that groups outperform individuals and that synthetic groups perform as well as group consensus. We explain the implications of this result on the coordination of the design of products and processes amongst distributed organizations. The increasing distribution of product design efforts, both in terms of geography and different organizations, presents an opportunity to improve coordination using distributed synthetic group‐based FMEA.     

TWO DESIGN PRINCIPLES FOR KNOWLEDGE-BASED SYSTEMS*

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem-solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role.     

Structuring Conflict in Individual,Face-to-Face,and Computer-Mediated Group Decision Making: Carping Versus Objective Devil's Advocacy*

Researchers and practitioners have long been interested in the effects of cognitive conflict techniques on individual and group decision making. One widely used and studied technique, devil's advocacy (DA), has been found to enhance decision-making performance for both individuals and groups. Devil's advocacy begins with a recommended decision, followed by a critique of the decision that questions its assumptions. Researchers have not yet examined the effects of the objectivity of the devil's advocacy comments in computer-mediated environments. This paper reports the results of a laboratory experiment that focused on this question by comparing the effects of an objective, nonemotional DA to an emotional, “carping” DA within individuals and groups using either computer-mediated or face-to-face communication. In a manner consistent with prior research, both DA treatments were operationalized through the use of paper-based consulting reports. The results suggest that individuals and computer-mediated groups develop and consider more solution alternatives than face-to-face groups, and that subjects given the objective DA treatment produce higher quality decisions than those given the carping DA treatment. Face-to-face groups in the carping DA treatment considered the fewest alternative solutions in their decision-making process, reached the lowest solution quality, yet reached decision consensus in the fewest voting rounds. The practical implications of the results suggest that questioning statements made by a devil's advocate should be objective, regardless of group communication condition. Carping devil's advocacy appears to stifle group decision outcomes when groups are using face-to-face communication.     

IT security planning under uncertainty for high-impact events

While many IT security incidents result in relatively minor operational disruptions or minimal recovery costs, occasionally high-impact security breaches can have catastrophic effects on the firm. Unfortunately, measuring security risk and planning for countermeasures or mitigation is a difficult task. Past research has suggested risk metrics which may be beneficial in understanding and planning for security incidents, but most of these metrics are aimed at identifying expected overall loss and do not directly address the identification of, or planning for, sparse events which might result in high-impact loss. The use of an upper percentile value or some other worst-case measure has been widely discussed in the literature as a means of stochastic optimization, but has not been applied to this decision domain. A key requirement in security planning for any threat scenario, expected or otherwise, is the ability to choose countermeasures optimally with regard to tradeoffs between countermeasure cost and remaining risk. Most of the planning models in the literature are qualitative, and none that we are aware of allow for the optimal determination of these tradeoffs. Therefore, we develop a model for optimally choosing countermeasures to block or mitigate security attacks in the presence of a given threat level profile. We utilize this model to examine scenarios under both expected threat levels and worst-case levels, and develop budget-dependent risk curves. These curves demonstrate the tradeoffs which occur if decision makers divert budgets away from planning for ordinary risk in an effort to mitigate the effects of potential high-impact outcomes.     

A Multicriteria Outranking Modeling Approach for Credit Rating

Rating models are widely used by credit institutions to obtain estimates for the probabilities of default for their clients (firms, organizations, individuals) and to assess the risk of credit portfolios. Several statistical and data mining methods are used to develop such models. In this article, the potential of an outranking multicriteria decision‐aiding approach is explored. An evolutionary algorithm is used to fit a credit rating model on the basis of the ELimination Et Choix Traduisant la REalité trichotomique method. The methodology is applied to a large sample of Greek firms. The results indicate that outranking models are well suited to credit rating, providing good classification results and useful insight on the relative importance of the evaluation criteria.