Safety Maintains Lean Sustainability and Increases Performance through Fault Control

: 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 e ﬀ ectiveness 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 su ﬀ ered) 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 E ﬀ ect Forecasting (FMEF) aided by Artiﬁcial Neural Networks through sequential steps known as Safety Function Deployment (SFD).


Introduction
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' faults. 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 at upstream stages, as cited by Faccio et al. [31]. The two main objectives of this paper are: (1) to identify and document maintenance activities' roadmap and (2) integrate safety procedures with Lean maintenance. To reach these objectives, maintenance classifies the faults into two categories, by process or by a human based on the time consumed of reparability activities, such as corrective (i.e., after the failure occurrence) or preventive (i.e., before the failure occurrence) [31]. The purpose of the Fairbanks article is to provide an overview of resilience engineering to stimulate innovations in safety and reflect the importance more of robust tools in the application of resilience engineering are needed [32]. The paper highlights that "lack of safety precautions" must be at the forefront of the waste list. The proposed framework strives to improve processes against safety hazards and accidents; the money spent on compensation claims is a waste. Therefore, the cost element is considered the main measurement of the Lean Safety Performance Level. In recent decades, the LM (lean manufacturing) [33] system neglects human resource management as a keystone of improvement, resulting in negative consequences in industrial performance [14]. On the contrary, the strength and skill set of workers leads to industrial growth and development, according to Narkhede and Gardas [34]. Waste is the other side of "lack of safety precautions" and is determined by identifying Non-Value-Added (NVA) activities and increased engagement of tools, equipment, workers, and materials that require simplification of processes, according to Wright. Sustainability is required to meet benchmarking figures that contribute towards optimum usage and conservation of natural resources, according to Nehete et al. [35]. In the lack of safety precautions, production performance is affected by integrated manufacturing systems without control [36]. Therefore, it should be covered in a defined interval of time as recommended by Khalil [37] and Ramesh et al. [38]. LSPL, measured by FMEF = f RPN (Occurrence, Severity Downtime, Detection Rate, Cost) where predicted by the NN based on some influence factors extracted from SFD.

The LSPL Measurement and Analysis Stages
The LSPL is integral to the DMAIC (i.e., at Measurement and Analysis phases) that are tackled through a unified framework (i.e., consisting of sequential stages) and has many functions, as illustrated in Figure 1, related to its costs, as discussed in Tables 1 and 2, and draws on some of the Lean rules discussed in Table 3: recommended by Sumant et al. [39]. The analysis stage was quoted by Prescott et al. [40].
Plan: Plan to remove the risks, via adequate programs (i.e., proposed stages) that have a reputation to eliminate risk causes.
Test and Track: Proposed stages performance compared to the plan. Control: Focus on understanding all risk causes throughout the proposed stages, which reveals emergent risk issues, taking into account the control action, and verify its performance.

Lean Safety Rules
Suggested Implementation Tool Specifying value: Value is realized by the end-user or the next requirement's step in some of the sequential processes, to meet its needs at a specific cost, time, and quality, and with fewer people's efforts (i.e., eliminate overprocessing) Gemba or workplace is a Japanese term meaning "the actual place", where valuecreating occurs to look for waste and opportunities to practice workplace kaizen or practical shop-floor improvement.
Identify and create a value stream: In a value stream, all activities are required to bring a specific goal (supplierproducer-customer). Making value flow: It flows through a Lean enterprise at the rate that the next or customer needs, and just in the amount needed without excess inventory.
Kanban is the name given to inventory control via using a pull system, which determines the suitable moving quantities in every process, between upstream processes. Pull not push: Only make as required. Pull the value according to the end-user's demand.
Jidoka can be defined as automation with a human touch, as opposed to a device that simply moves under the monitoring of an operator. OEE is defined as the effect implying, how effectively planned time was used for producing good parts.      Identify: Risks that have untoward effects on HBL elements safety. Analyze: Evaluate the probability of the risk consequences by analyzing its priority as recommended by Sumant et al. [39]. The analysis stage was quoted by Prescott et al. [40].
Plan: Plan to remove the risks, via adequate programs (i.e., proposed stages) that have a reputation to eliminate risk causes.
Test and Track: Proposed stages performance compared to the plan. Control: Focus on understanding all risk causes throughout the proposed stages, which reveals emergent risk issues, taking into account the control action, and verify its performance.
There is a "triplet" concept of outline risk, which is useful because it clarifies how to avoid, assess, and outline risk to produce three components of risk: undesired scenarios, their probability, and their consequences. Therefore, risk = f (mishap scenario, occurrence frequency, and consequence severity). Table 3. Lean Safety Performance Level (LSPL) and their implementation tools' definition.

Lean Safety Rules Suggested Implementation Tool
Specifying value: Value is realized by the end-user or the next requirement's step in some of the sequential processes, to meet its needs at a specific cost, time, and quality, and with fewer people's efforts (i.e., eliminate overprocessing) Gemba or workplace is a Japanese term meaning "the actual place", where value-creating occurs to look for waste and opportunities to practice workplace kaizen or practical shop-floor improvement. Identify and create a value stream: In a value stream, all activities are required to bring a specific goal (supplier-producer-customer).
Making value flow: It flows through a Lean enterprise at the rate that the next or customer needs, and just in the amount needed without excess inventory.
Kanban is the name given to inventory control via using a pull system, which determines the suitable moving quantities in every process, between upstream processes. Pull not push: Only make as required. Pull the value according to the end-user's demand.
Jidoka can be defined as automation with a human touch, as opposed to a device that simply moves under the monitoring of an operator.
OEE is defined as the effect implying, how effectively planned time was used for producing good parts. Table 1 shows the different elements of the cost of poor proficiency that represent Lean Safety Performance Level (LSPL) clearly and appeared in the last column of Table 2 to obtain an ideality index that helps in forecasting faults according to their cost types (direct or indirect and internal or external). Table 2 illustrates the KPI of the proposed LSPL as recommended by the NSC at the future work section [41] and its related costs. The last column in Table 2 indicates the cost indicator types, which are Appl. Sci. 2020, 10, 6851 7 of 28 related to the HBL elements. The LSPL aims to save the Lean implementation via some concepts discussed in Table 3.
All indicators reviewed in Table 2 are guided and tackled by using a proposed Safety Function Deployment (SFD) as mimicking to the QFD steps [42], which is based on the enterprises' expectations and safety-critical factors.
The research findings show a proven between Lean Safety and sustainability (i.e., HBL elements in a stationary and safe case), mainly because the enterprises focused on the value concept. The tools of LSPL are illustrated in Table 3, which focuses on reducing the variation of VA during its progress, by following a proven approach for gaining significant improvement in performance (DMAIC). There are five rules for implementing Lean Safety via SFD to gain desirable values as illustrated in Table 4. The LSPL tackles safety frameworks as a remedy against a lack of implementation strategies, to present SM that stimulates the DMAIC by improving its Lean sustainability features. The LSPL focuses on pursuing radical changes in the people's enlightenment about faults classification, as illustrated in Figure 2, and its impact on HBL, thereby enhancing the profitability and urged them to improve their performance (proficiency level), the proven tool used in fault analysis is the Ishikawa or fishbone diagram.
It is used to find and derive all possible causes or root causes behind any uncertainty factor. The researchers believe that improving performance relies on forecasting mishaps via determining the famous and related causes leading to it. This diagram will be managed via the proposed reliable tool that has the ability to perform its intended objectives over a long time from the first time and every time, called Fault Mode and Effect Forecasted (FMEF). It is used to find and derive all possible causes or root causes behind any uncertainty factor. The researchers believe that improving performance relies on forecasting mishaps via determining the famous and related causes leading to it. This diagram will be managed via the proposed reliable tool that has the ability to perform its intended objectives over a long time from the first time and every time, called Fault Mode and Effect Forecasted (FMEF).

Research Methodology
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 NN model. Therefore, it is

Research Methodology
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 NN model. Therefore, it is proposed to establish the House of Safety (HoS) modeled on House of Quality (HoQ), which consists of five sequential steps filled out via 185 questionnaires about tackling safety tackled.
(1) monitoring all processes to maintain the deviation of processes within less than 1%. (2) Establish a feasibility study on corrective actions for faults' causes at the moment it appeared (i.e., in time). (3) All processes uploaded and data monitored and updated via the ERP information system). (4) All faults have been identified in a tailored safety list illustrated in Figure 1. (5) Trying to be less expensive within 100% implementation of safety procedures in the enterprise. These steps were tackled through Safety Function Deployment (SFD) to extract the influencing factors, which must be forecasted and controlled as illustrated in Tables 5-8.
Step 1 of the SFD indicates the importance of LSPL in industrial society, which ranked first by 25% to the variables used to increase the safety case in the industry.
Step 2 of the SFD indicates the importance of formulating unified framework interests with the full inspection with time based on the Local ERP system analysis.
Step 3 of the SFD indicates the importance of faults' documenting throughout cycle time to maintain health and safety with respect to failure in efficient use. This target needs to construct an implementation sequential step based on valid data collected.
Step 4 of the SFD indicates the importance of statistical validation by respecting technology, especially in evaluating the equipment efficiency and the importance of proficiency value. Figure 3 discusses the dynamic process identification and using FMEA to evaluate the fault severity, while Figures 4 and 5 illustrate the flowchart of FMEF that used to track and predict the sustainable performance level via the safety tip-off of six sequential steps.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 12 of 30 Step 4 of the SFD indicates the importance of statistical validation by respecting technology, especially in evaluating the equipment efficiency and the importance of proficiency value. Figure 3 discusses the dynamic process identification and using FMEA to evaluate the fault severity, while Figures 4 and 5 illustrate the flowchart of FMEF that used to track and predict the sustainable performance level via the safety tip-off of six sequential steps.
(1) Identify the safety instructions related to Fault Modes before they happen for every Value-Added (valueadded) or Non-Value-Added (NVA) activities (2) Determines the effect and severity of these faults according to consuming time and its costs.   (1) Identify the safety instructions related to Fault Modes before they happen for every Value-Added (value-added) or Non-Value-Added (NVA) activities (2) Determines the effect and severity of these faults according to consuming time and its costs.        A preliminary stage of adopting (undertaking) LSPL embeds a sustainability constraint to ass the identification and prioritization with respect to modern HBL and the common Lean Six Sigma (LSS) tools are exposed in Table 9.  A preliminary stage of adopting (undertaking) LSPL embeds a sustainability constraint to ass the identification and prioritization with respect to modern HBL and the common Lean Six Sigma (LSS) tools are exposed in Table 9.

Failure Mode and Effect Forecasting (FMEF) Formulation
Sustainable Lean is equivalent to system reliability R(t) against fault occurrence, availability, and maintainability, which are important factors to guarantee the safety level. Sustainable Lean is affected by faults and malfunctions occur in the workplace. Therefore, the prediction and diagnosis of the faults are the core of this paper. Faults related to deviation behavior according to their form whether systematic or random, time behavior appears from draft to permanent path through noise and extent appears in local or global VSM. The Lean sustainability of many identical activities is defined by Equation (1).
The fault occurrence rate defined as the instantaneous rate of malfunction or unplanned downtime at emergency case is defined by the Equation (2): The severity S v level proportion to a maintenance level (the repairability consuming time), whether planned or not as illustrated in Figure 6 to repair specific fault, is defined by the Equation (3) as follows: where T Ri is time to repair the malfunction.
The severity level proportion to a maintenance level (the repairability consuming time), whether planned or not as illustrated in Figure 6 to repair specific fault, is defined by the Equation (3) as follows: where is time to repair the malfunction. The concept of Ideality is introduced in the methodology of creative problem solving, which is very close to the value in value analysis. One variant is known as the "Theory of Inventive Problem Solving". While stipulating that a proposed framework has the main "VA activity function".
The delivery of it is necessarily accompanied by loss functions (i.e., NNVA and NVA costs and time) that can be controlled via the proposed roadmap embeds with an effective framework. The better is the framework the fewer are the number of the loss functions (i.e., any undesired costs or downtimes) that addressed via ideality index_ _ as defined by the Equation (4). This value is considered a seed of using the Neural Networks of the optimization stage of fault tracking and forecasting based on specific scenarios (i).
The question should be: how to execute the VA in a way that is not minimalist (NNVA). An Ideality defined by Equation (5) indicates the effectiveness of the proposed framework is calculated using the ratio of the number of valid causes to the total number of potential causes and averaged over the tackling scenarios for data collected in Tables 9 and 10.
where = number of correction scenarios investigated to reduce losses and faults opportunities in certain activity. = number of potential causes of malfunction due to fault identified by the framework for scenario i. = cost of potential causes of malfunction due to fault identified by the framework for scenario i. The concept of Ideality is introduced in the methodology of creative problem solving, which is very close to the value in value analysis. One variant is known as the "Theory of Inventive Problem Solving". While stipulating that a proposed framework has the main "VA activity function".
The delivery of it is necessarily accompanied by loss functions (i.e., NNVA and NVA costs and time) that can be controlled via the proposed roadmap embeds with an effective framework. The better is the framework the fewer are the number of the loss functions (i.e., any undesired costs or downtimes) that addressed via ideality index_Py i _ as defined by the Equation (4). This value is considered a seed of using the Neural Networks of the optimization stage of fault tracking and forecasting based on specific scenarios (i).
The question should be: how to execute the VA in a way that is not minimalist (NNVA). An Ideality defined by Equation (5) indicates the effectiveness of the proposed framework is calculated using the ratio of the number of valid causes to the total number of potential causes and averaged over the tackling scenarios for data collected in Tables 9 and 10.
where N = number of correction scenarios investigated to reduce losses and faults opportunities in certain activity. n i = number of potential causes of malfunction due to fault identified by the framework for scenario i. C i = cost of potential causes of malfunction due to fault identified by the framework for scenario i. w i = weight of potential causes of malfunction under scenario I consideration extracted from SFD result in Table 8 (Step 4). Ideality i = number of correct potential causes of malfunctions due to fault obtained by the framework for scenario i. (1) Fault Opportunities: (Specific loss of any of HBL functions), related to opportunities aforementioned in Table 1.
(2) Fault mode "effect": A description of the consequence and ramification of any HBL faults, to rank these faults according to a severity scale. A typical Fault Mode may have several "effects" depending on a review of which manufacturer, manufacturer, or any of stakeholders are considered (i.e., analyzed and tailored according to needs via brainstorming recommendations).
(3) Severity rating η: (seriousness of the effect) Severity is the numerical rating (e.g., 1:10) of the impact on customers, manufacturer or any of HBL elements, related with loss function (i.e., nonideality, which use expenses indicator as a costs' reference estimated according to Table 1). Severity against the maintainability level or mean time to repair the fault MTTR.
(4) Fault mode "causes": A description of the proficiency' losses (high ramifications of direct and root causes) that results in the Fault Mode, which can be formulated via classical cause and effect diagram.
(5) Occurrence rating ω: An estimated number of tenfold relative frequencies of the cumulative number of specific causes over the intended period "threatening the sustainability of the safety case" (i.e., frequency codification and tracked out via mining in the local dataset).
This step needs for creating a time schedule for predicted faults and codify via closely monitoring its behavior at a specified period using any of forecasting procedures, such as ARIMA or using the codes of artificial intelligent as Neural Network (e.g., time of the birth of the fault: t, fault's lifespan: δt, severity: η, occurrence: ω and loss cost estimations: θ) (7) Detection rating (forecasted via ARIMA) [45]: A numerical rating (i.e., 1:10, 1 being detectable via forecasting every time, 10 being impossible to detect via forecasting) of the probability that a given set of the investigation will discover a specific cause of Fault Mode to resist consequences.
(8) Risk Priority Number (RPN; descending Order ): = S everity × O ccurrence × D etecting is a response (9) Action planning: A high-risk framework that is not followed with corrective actions has little or no value, other than having a chart for an audit. Therefore, the FMEF is created. If ignored, you have probably wasted much of your valuable time. A good action plan focused on reducing the RPN by adopting the obvious safety roadmap has many VA functions.
The main result of the design stage of the proposed LSPL framework is to obtain RPN from FMEF steps and record it in time to calculate the consuming repair time and document that with its expenses deduced from Table 1.

Lean Safety Performance Level (LSPL) Case Study
The costs and consuming maintainability time were classified according to Tables 1 and 2. The experts according to questionnaire analysis decided that the performance level should be among 65% to 99.9% [46], and distributed according to the illustration Table 11. The proposed LSPL framework stages were adopted via U.S.C.C (a consultant office owned by Zagazig University, Egypt). The losses and costs data for the medium and small industrial organization's scale has been collected from 495 departments belongs 18 ERP's enterprise systems of different industries from July 2014 to November 2019 in some industrial Egyptian cities through physical visits and the online questionnaires that oriented to the safety's managers who participated in this survey voluntarily. The influencing measurement variables for the planning (i.e., Measuring and Analysis) for the Lean Safety LSPL framework as illustrated in Table 12. These variables have been tackled as illustrated in Table 13, which are related to HBL abuses.  Table 1) ELP2: Reduced the NNVA Occurrence Rating ω in participating enterprises (review Table 10 and Equation (1)) ELP3: Reduced the NNVA Severity Rating η level in participating enterprises (review Table 10) ELP4: Increased the ideality value (review Equation (4)) to deploy the performance level/monthly ELP5: Measure the cost of poor proficiency that generated due to NNVA faults to be controlled Out of 495 questionnaires, 197 (40.2%) responses were received. Incomplete questionnaires were discarded. The final study sample consisted of 185 (37.7%) valid returned questionnaires that were implemented in different 18 enterprises. The characteristics summarized of the respondent's enterprises indicate that the majority of them are cartons' industries (48.3%), metal industry (19.1%), textile industries (15.4%), bathtubs fabrications (9.4%), electronics and other electrical equipment (4.2%), and others factories represent (3.4%). Reliability has been tested based on Cronbach's alpha value illustrated in Table 13. For the reliability test, Cronbach's alpha value for safety precautions activities performance had the highest (0.936) while the Lean performance was the lowest (0.861). Thus, all of the Cronbach's alpha values (extracted from R statistical software) were significant at p < 0.05.
The principal component analysis (PCA) and the confirmatory factor analysis (CFA) used to identify the most meaningful basis and to check the similarities and differences of the data validation. Eigenvalues and percent of variance explained for each stage at the LSPL framework are illustrated for 185 enterprises' sectors interests in the implementation of LSPL, and the cumulative percentages of explained variance were 66.509 for the stages illustrated in Table 13. The loading values of each influence variable ranged from 0.619 to 0.889 as illustrated in Table 13 and deduced from SFD (Table 8, Step 4). However, all variables that appeared at any stage of Table 12, and had a loading value less than 0.5, were removed from the implementation illustrated in Table 14.
The recorded "102,592 " activities for a VA and NNVA of one from participated enterprises from July 2014 to November 2019 are around the whole safety practices illustrated in Table 14, which illustrates the costs related with potential incidents or injuries (i.e., The cost is the summation of maintainability costs plus the cost of consuming downtime associated with fault opportunity). There are some questions to be answered to determine the performance level of implemented LSPL. These questions are listed below :

2.
How much is the enterprise cost on Faults identified? (3041.13).

5.
What is the (approximate) LSPL for LSPL implementation? (4.5 marks over 6 (75%)).  The decision: This industry is in a moderate risk situation according to Table 11.
The main issue from implementing the proposed framework is finding a safety scale for different industries modeled on the defect scale that is named by DPMO tables. The last two columns in Table 14 illustrate the consumption of repair time and the ideality according to cost types appeared in the second column of the same table according to a specific case study. The performance of the enterprises in following and implementing special instruction recommended by the LSPL stages is an audit by ideality multiple by the time consumption of the repairing activities (i.e., downtime), which is considered a representative point for the enterprise evaluation according to this variable and start tracking it via using NN. The non-proficiency/year (faults) are illustrated in Table 15.  Figure 7 illustrates the significance of HBL elements via measure ideality response value discussed in Equation (5) to instruct the Neural Networks at the tracking and controlling stages of the proposed LSPL framework. The figure further demonstrates the high impact of the interaction between environment and enterprise culture toward the Lean Safety approach, as illustrated in Figure 8, which is more than a social policy interference and affects the technology on management modeled on [47]. Figure 9 illustrates the interference of social and policy on ideality value for the Lean Safety approach, while Figure 10 illustrates the interference of the environment with technology.
The results illustrated in Table 16 for the goodness of fit of the test stage for the measurement performance for the LSPL implementation are summarized. The values of SRMR, RMSEA, x 2 , and the p-value were satisfactory, while the values of GFI and AGFI were not.   Figure 9 illustrates the interference of social and policy on ideality value for the Lean Safety approach, while Figure 10 illustrates the interference of the environment with technology.    Figure 9 illustrates the interference of social and policy on ideality value for the Lean Safety approach, while Figure 10 illustrates the interference of the environment with technology.    Figure 9 illustrates the interference of social and policy on ideality value for the Lean Safety approach, while Figure 10 illustrates the interference of the environment with technology.  The results illustrated in Table 16 for the goodness of fit of the test stage for the measurement performance for the LSPL implementation are summarized. The values of SRMR, RMSEA, x 2 , and the p-value were satisfactory, while the values of GFI and AGFI were not.  Figure 10. Impact of Environment and enterprises technology interference.  Table 17 illustrates the correlations between influencing variables, while the off-diagonal elements represent the eigenvalue. The mean square roots of variances should be greater than the correlation between a particular influencing variable and other influencing variables. The statistics illustrated in Table 15 satisfied the overall requirement as lending to discriminant validity and evidence to construct validity [46].

Sustainable Lean Safety Performance Enhancement
The improvement was done by tracking the activities in time-at-risk cases during the studying interval. This work resorted to using an optimization tool such as Artificial Neural Networks (ANN) because there are no linear dependencies between input and output data (i.e., evaluate all possible values of a certain "unknown" function) by solely establishing the nonlinear relations between input or output datasets, based on the learning process itself. The ANN has the ability to force using the Simple Moving Average (SMA) to monitor the VA and NNVA activities with time. Finally, at the end of the run, will obtain the array of SMA values for each time-cost at a moment t. Table 18 illustrates the Neural Network input data. The number of neurons is 21, while the second layer of network models has 19 neurons. The regression analysis was implemented on a specific training data set loaded on the local dataset to determine highly accuracy running performance with correlation coefficient R, which approximates a value of 0.999. The performance of tracking the faults interval via MSE of 0.027 at Epoch 3 and the R between the target and output for validation data was 0.9744. The results of testing for ANN used in this work illustrated in Figure 11, where the convergence becomes valid when the R between standard values calculated from Table 13 and predicted output is > 80%, to reduce the defects related with faults similar to the Lindstrom et al. approach to reach zero faults [47]. data set loaded on the local dataset to determine highly accuracy running performance with correlation coefficient R, which approximates a value of 0.999. The performance of tracking the faults interval via MSE of 0.027 at Epoch 3 and the R between the target and output for validation data was 0.9744. The results of testing for ANN used in this work illustrated in Figure 11, where the convergence becomes valid when the R between standard values calculated from Table 13 and predicted output is > 80%, to reduce the defects related with faults similar to the Lindstrom et al. approach to reach zero faults [47].  Figure 11. Training result of the proposed network. Figure 12 illustrates the standard value deduced from Table 13 vs. the output plot for the trained ANN simulated by all training dataset stored on the ERP's enterprise system via running the code in Appendix A. The performance of the network can be improved if training data increasingly take into account the effect of Fault Tree Analysis (FTA), as discussed by Shafiee [48], where authors suffered from collecting the data, where it is collected in the manufacturing environment, not a laboratory environment.   Figure 12 illustrates the standard value deduced from Table 13 vs. the output plot for the trained ANN simulated by all training dataset stored on the ERP's enterprise system via running the code in Appendix A. The performance of the network can be improved if training data increasingly take into account the effect of Fault Tree Analysis (FTA), as discussed by Shafiee [48], where authors suffered from collecting the data, where it is collected in the manufacturing environment, not a laboratory environment. data set loaded on the local dataset to determine highly accuracy running performance with correlation coefficient R, which approximates a value of 0.999. The performance of tracking the faults interval via MSE of 0.027 at Epoch 3 and the R between the target and output for validation data was 0.9744. The results of testing for ANN used in this work illustrated in Figure 11, where the convergence becomes valid when the R between standard values calculated from Table 13 and predicted output is > 80%, to reduce the defects related with faults similar to the Lindstrom et al. approach to reach zero faults [47].  Figure 11. Training result of the proposed network. Figure 12 illustrates the standard value deduced from Table 13 vs. the output plot for the trained ANN simulated by all training dataset stored on the ERP's enterprise system via running the code in Appendix A. The performance of the network can be improved if training data increasingly take into account the effect of Fault Tree Analysis (FTA), as discussed by Shafiee [48], where authors suffered from collecting the data, where it is collected in the manufacturing environment, not a laboratory environment.  The entire process of SMA computation for the functions that have high influence values and appear in Table 13 (i.e., the TaTS1 variable) are illustrated in Figure 11 and tracked closely. When considered other influencing variables, the performance is enhanced as illustrated in Figure 12. Before creating and training an ANN to predict future values of process deviation according to time that modeled on SMA steps. Some portion of the dataset generated and trained the proposed Neural Network on the dataset being generated. The statement's code snippets that perform training samples generation are listed below. This code was modeled on the steps of Abed et al. at IEOM 2018 [43].

Conclusions
The paper aims at establishing a unified safety procedures framework works through the proposed roadmap that derivative of DMAIC and enhance its Measurement and Analysis phases and called the Sub-Road-Map (LSPL), which activating through a proposed framework named the LSPL that follows the Lean for its excellence in cost controlling due to fault tracking. The proposed framework needs extra efforts from the workers and their enterprise's enlightenment, to audit the relationship of costs (e.g., maintainability costs plus consuming downtime) as articulated in Table 12 and their costs, articulated in Table 1. The triggering of the proposed algorithm by forecasting the precise faults of the performance level of the Lean Safety approach via an ideality value extracting from FMEF steps to determine their severity, occurrence, and detecting it as discussed in Table 10 to feed the Neural Network code to predict the behavior of the enterprises toward their faults control before exacerbate in a timely fashion via followed process deviation as illustrated in Figures 11 and 12.
Oddly enough, it was found in the analysis of the questionnaire's data collected from 2014 to 2019, the enterprises' behaviors tend to be more task-oriented (Theory N) [46], as illustrated in Figures 4 and 5, emanating from Figure 1. The LSPL and its LSPL framework reduce the enterprise's costs related to downtimes to 0.037%. Consequently, the fault per million opportunities that corresponding FPMO table is 5.78/6, which declares the Lean Safety Performance Level to 96.333%, which according to Table 11 illustrates that the enterprise becomes near safe and under ongoing control.