Abstract
Almost every industrial and service enterprise adopts some form of Environmental Health and Safety (HSE) practices. However, there is no unified measurement implementation framework to resist losses exacerbated due to the “lack of safety precautions”, which must be considered one of the most dangerous Lean wastes because it jeopardizes the investment in the Hex-Bottom-Line (HBLs). Despite the widespread nature of the Lean approach, there no unified and collected framework to track and measure the effectiveness of the safety measures’ progress. Therefore, the enterprises resort to establishing their own tailored safety framework that maintains their competitiveness and sustainability. The enterprises must provide insight into safety deficiencies (i.e., faults and losses suffered) that have been measured via downtime spans and costs (Lean waste), reflecting the poor Lean Safety Performance Level (LSPL). This paper aims to shed light on two issues: (1) the adverse impact of the “lack of safety precautions” on LSPL caused by the absence of (2) a Lean Safety framework included in the Measurement and Analysis phases of Define Measure Analyze Identify Control (DMAIC). This framework is based on forecasting losses and faults according to their consumption time. The proposed framework appreciates the losses’ severity (time consumption and costs) via Fault Mode and Effect Forecasting (FMEF) aided by Artificial Neural Networks through sequential steps known as Safety Function Deployment (SFD).
Highlights
Sustainability in competitiveness is the main objective of industrial engineering philosophies, especially “the Lean”, in providing better waste disposal results
Lean Safety Performance Level (LSPL) via relies someoninfluencing variables accordingtheto safety famous and related causes leading to it. This diagram will be managed via the proposed reliable tool considerations extracting from Safety Function Deployment (SFD), which is based on the ideality of each activity executed in that has the ability to perform its intended objectives over a long time from the first time and every time, the workplace andMode has and a direct correlation with HBL elements vs. loss function costs based on called Fault
This paper aims to measure the LSPL via some influencing variables according to safety considerations extracting from SFD, which is based on the ideality of each activity executed in the workplace and has a direct correlation with HBL elements vs. loss function costs based on the magnitude of the costs that have been spent to correct its deviation paths aided by Neural Networks (NN) model
Summary
Sustainability in competitiveness is the main objective of industrial engineering philosophies, especially “the Lean”, in providing better waste disposal results. The scientific interdisciplinary community resort to drawing attention to the consequences of neglecting to follow safety procedures (i.e., lack of safety precautions), which is considered the most dangerous Lean waste that must be tracked and controlled. The research mimics Pudar’s [1] interest in cyber fault activities via modeling its cyber-attacks (faults/wastes) and countermeasures. Pudar’s research considered looking at the faults due to the lack of safety leading to working disruption (i.e., downtime spans) due to workers’. This work adopts a stochastic tree approach [2] aided by dynamic Petri-net as one of the most intuitive tools in detecting the nature of periodic faults based on their costs and reparability (i.e., Lean Safety Performance Level). Pudar resorted to measuring its model’s performance via validated quantitative metrics used to describe a vulnerable/threatened system due to the lack of safety
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
