Evaluation of the Human Error Probability in Cellular Manufacturing

Gidwani

et al. 2019

BIJ

Purpose The purpose of this paper is to visualize the prioritization among essential factors of cellular manufacturing system (CMS) implementation using the analytic hierarchy process (AHP) and analytic network process (ANP) methods. Design/methodology/approach Based on literature review, 4 enabler dimensions and 17 CM factors were identified which were validated by experts from academia and industry. Then, AHP and ANP models are proposed in evaluating CMS implementation dimensions and factors. The results are validated using sensitivity analysis. Findings These models give firms a straightforward and simple to utilize way to deal with CMS efficiently. The two strategies were appeared to be powerful in choosing a strategy for CMS implementation. The two strategies brought about nearly similar outcomes. Both methods consider the particular necessities of the organization through its own accessible ability. Practical implications The techniques exhibited in this paper can be utilized by a wide range of organizations for adopting CMS that have a higher impact on performance and thus overall productivity. The two techniques are explained in a step-by-step approach for easier adoption by practitioners. Originality/value The strength of the present study is that it is one of the first few to be conducted in perspective for CM implementation factors analysis.

Section: Literature Reviewmentioning

confidence: 99%

Implementation model for cellular manufacturing system using AHP and ANP approach

Gidwani

et al. 2019

BIJ

“…Human limitations are the main causes of human related errors and failures. Categorized from memory, perception to motion aspect of human, about 16 types of errors are proposed in [51]. In smart manufacturing environment, although many operations are executed automatically, human errors are still of great importance, especially omissions and the incorrect selection of variants.…”

Section: Human Limitation Managementmentioning

confidence: 99%

“…Based on these modes, a CPS safety analysis and simulation platform called CP-SAP is developed and a cyber-physical human dynamic fault tree is designed in [52]. In order to access human errors, human errors probability (HEP) and human reliability probability (HRP) are proposed as indicators for the relative occurrence of errors and respectively faultless actions [51]. The definition of the above two concepts is as follows:…”

Section: Human Limitation Managementmentioning

confidence: 99%

Anthropocentric Approach for Smart Assembly: Integration and Collaboration

Tan

Tong

et al. 2019

Journal of Robotics

Recently, anthropocentric or human-centric approaches renewed its importance in smart manufacturing especially for assembly applications where human dexterity and informal knowledge are dispensable at present and in the near future. This paper analyzes the integration, design, and collaboration issues regarding anthropocentric researches in the past few years mainly in assembly domain. First of all, towards closed-loop system integration, the researches on integrating human in the cyber-physical system are elaborated and summarized. Then, human-centric designs (HCD) especially in shop-floor assembly field are analyzed. To emphasize human-robot hybrid assembly, several related issues including collaboration paradigm, task planning and assignment, interaction interfaces, and safety are discussed. At last, a survey on human cognitive and social limitation management is elaborated. Related research challenges and directions are discussed.

“…However, so far, much of the work of HF/E specialists, to analyze the origins of error‐related quality deficiencies, has focused on improving the immediate human‐system interface and relatively less on broader organizational factors. Evidence of this can be found in several recent studies where the human reliability analysis (HRA) approach was used to identify and quantify assembly errors (Blackert et al, 2019; Böllhoff et al, 2016; Caputo et al, 2017; Pasquale et al, 2018; Torres et al, 2021a). As a result, more recent systems thinking‐based risk assessment methods have been given less consideration when analyzing error‐related quality deficiencies in manufacturing environments.…”

Section: Introductionmentioning

confidence: 99%

“…Evidence of this can be found in several recent studies where the human reliability analysis (HRA) approach was used to identify and quantify assembly errors (Blackert et al, 2019;Böllhoff et al, 2016;Caputo et al, 2017;Pasquale et al, 2018;Torres et al, 2021a). As a result, more recent systems thinking-based risk assessment methods have been given less consideration when analyzing error-related quality deficiencies in manufacturing environments.…”

Section: Introductionmentioning

confidence: 99%

Applying AcciMap and STAMP to the analysis of human error in complex manual assembly

Torres

Nadeau

Landau

2022

Hum Ftrs & Erg Mfg Svc

In sectors such as aerospace manufacturing, human errors in the assembly of complex products can negatively impact quality, productivity, and safety. Until now, the analysis of assembly errors has focused more on the immediate human‐system interface and less on broader organizational factors. This article presents a case study‐based analysis of assembly errors in the aeronautical industry using the systemic methods AcciMap and Systems‐Theoretic Accident Model and Processes (STAMP). We seek to provide the company with elements to build a quality improvement strategy that considers human factors and ergonomics from a systemic perspective. The data and information necessary to conduct the analysis came from a project carried out at an aerospace manufacturing facility over a period of 12 months. The team had direct and recurrent access to primary data sources and communication with various stakeholders. A total of 31 influencing factors were identified with AcciMap at different levels within the manufacturing system. STAMP made it possible to model the sociotechnical control structure of the assembly process and identify several control flaws leading to hazards. The analysis shows that systemic methods require a high level of understanding of the manufacturing system and access to relatively high amounts of data and information. Therefore, direct contact with the field and stakeholders is crucial. Training quality specialists on systemic methods could support its use and help to close the gap between theory and practice. Globally, the field of quality in manufacturing could benefit from using systemic methods when deemed necessary.