Lean projects’ evaluation: the perceived level of success and barriers

Antomarioni, Sara; Ciarapica, Filippo Emanuele; Sanctis, Ilaria De; Ordieres‐Meré, Joaquín

doi:10.1080/14783363.2020.1731301

Cited by 8 publications

(12 citation statements)

References 75 publications

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“…The study evidenced all the 10 types of wastes that the literature review revealed. The study found similarities to self-serving bias and self-selection (self-serving/self-centered) to that of Antomarioni et al (2021) and Shi et al (2019) and further observed different biases to that identified by Singh and Rathi (2019), Gleeson et al (2019), Shi et al (2019), Tortorella et al (2021), Sahoo (2019), Negrão Léony Luis et al (2019), Ibrahim et al (2020), Cruz et al (2019), Gleeson et al (2019), Valente et al (2019) and Claudia and Geraldo (2020), refer to Table 4. The probable reasons could be that this study was a case study and others had multiple case studies.…”

Section: Discussionmentioning

confidence: 61%

“…Many authors considered or verified nonresponse bias, (Tortorella et al, 2020, Sahoo, 2019, Negrão Léony Luis et al, 2019, review bias and response bias (Ibrahim et al, 2020) in their case studies. The influences of perception's bias, self-serving bias, actorobserver bias (Antomarioni et al, 2020), operational bias (Cruz et al, 2019), observational bias (Gleeson et al, 2019), subjective bias (Valente et al, 2019) and technocratic bias (Claudia and Geraldo, 2020) were linked to Lean projects. However, the connections between the bias, Lean tools and wastes have not been studied previously.…”

Section: Discussionmentioning

confidence: 99%

“…However, bias is often not considered as a major barrier (Kahneman, 2011). In addition, connections between bias, Lean tools, and wastes have not been studied (Singh and Rathi, 2019, Antomarioni et al, 2020, Cruz et al, 2019. The objective of the paper is to focus on the cognitive biases influence on Lean and waste in the organisations that adopt Lean practices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many authors considered or verified nonresponse bias (Tortorella et al , 2021; Sahoo, 2019; Negrão Léony Luis et al , 2019), review and response biases (Ibrahim et al , 2020) in lean-related research to correct research errors. Antomarioni et al (2021) observed perception bias, self-serving and actor-observer biases to Lean practices while Cruz et al (2019) linked operational bias to Lean performances. Gleeson et al (2019) linked observational bias to Lean barriers, Valente et al (2019) linked subjective bias to decision-making in Lean and Claudia and Geraldo (2020) linked technocratic bias to Lean project performances.…”

Section: Introductionmentioning

confidence: 99%

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A relationship between bias, lean tools, and waste

Purushothaman

Seadon²,

Moore³

2021

IJLSS

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Purpose This study aims to highlight the system-wide potential relationships between forms of human bias, selected Lean tools and types of waste in a manufacturing process. Design/methodology/approach A longitudinal single-site ethnographic case study using digital processing to make a material receiving process Lean was adopted. An inherent knowledge process with internal stakeholders in a stimulated situation alongside process requirements was performed to achieve quality data collection. The results of the narrative analysis and process observation, combined with a literature review identified widely used Lean tools, wastes and biases that produced a model for the relationships. Findings The study established the relationships between bias, Lean tools and wastes which enabled 97.6% error reduction, improved on-time accounting and eliminated three working hours per day. These savings resulted in seven employees being redeployed to new areas with delivery time for products reduced by seven days. Research limitations/implications The single site case study with a supporting literature survey underpinning the model would benefit from testing the model in application to different industries and locations. Practical implications Application of the model can identify potential relationships between a group of human biases, 25 Lean tools and 10 types of wastes in Lean manufacturing processes that support decision makers and line managers in productivity improvement. The model can be used to identify potential relationships between forms of human biases, Lean tools and types of wastes in Lean manufacturing processes and take suitable remedial actions. The influence of biases and the model could be used as a basis to counter implementation barriers and reduce system-wide wastes. Originality/value To the best of the authors’ knowledge, this is the first study that connects the cognitive perspectives of Lean business processes with waste production and human biases. As part of the process, a relationship model is derived.

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Section: Discussionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A relationship between bias, lean tools, and waste

Purushothaman

Seadon²,

Moore³

2021

IJLSS

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“…The adoption of such technologies is now perceived as the key to industrial success, given the possibility of interconnecting machines and tools to improve performance and efficiency. However, the mere introduction of such innovations is not enough; the shared support of the whole company is necessary to achieve success [1]. In this context, if innovation can be considered an opportunity for companies, ad hoc training is the real challenge.…”

Section: Introductionmentioning

confidence: 99%

Learning-by-Doing Safety and Maintenance Practices: A Pilot Course

et al. 2022

Self Cite

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This paper presents an educational approach for teaching Industry 4.0 concepts to maintenance and safety operators involved in industrial processes. A Learning-by-doing approach was introduced to assess the impact of learning by doing and knowledge sharing on designing maintenance and safety solutions based on Industry 4.0 concepts to build experience and improve decision-making skills. To this end, we proposed a pilot course to train industrial operators in the field of new technologies so that they could continue their work effectively. Specifically, the development of the course began with a needs assessment of the perspective participants, followed by an outline of the objectives and course structure. The course was adapted to the different educational and technical backgrounds of the participants (i.e., experienced operators who were digital immigrants and non-experienced operators who were digital natives). The results of the course were assessed through a survey, which allowed us to evaluate the operators’ perception of the learning approach and the contribution to improving the operators’ competencies and abilities. The results highlighted that the educational approach facilitated the teaching of maintenance and safety principles, promoting operators’ attention and participation. The difference in the learning level that we observed between the younger and older operators was also highlighted by the survey results. A dichotomy was revealed between the younger operators, who showed a greater understanding of the explained technologies, and the older operators, who required longer learning times. In this way, both types of participant could benefit from mutual collaboration and teamwork to improve their respective weaknesses.

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