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This paper aims to optimize the losses of the current manufacturing systems to perform the Just in Time (JIT) system and minimize the time, labor, and material in the manufacturing process. Pull systems, which have a famous name of the Smart Kanban system, are one of the methodologies to implement the JIT system. The setting of manufacturing system frameworks offers intriguing capabilities to improve the performance of pull-controlled creation frameworks. Smart Kanban can be utilized, rather than actual cards, and the data conversion about the manufacturing settings, is accessible through sensors. Since a Just-in-Time system does not include a buffer, any interruption to the system can cause the process to stop quite quickly, so security is necessary for the JIT improvement. However, those systems have recently been unguarded to security threats as they introduce general-purpose technologies. From the viewpoint of the system, a security measure is implemented for pull system methods to check for potential threats and analyze vulnerabilities of the pull system. The Flower Pollination Algorithm (FPA) is used as a novel multiobjective technique to optimize different types of cost and time. The experimental results show that the proposed algorithm has robust security efficiency, as well as reduces cost and time consumption.

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 &ldquo, lack of safety precautions&rdquo, 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&rsquo, 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 &ldquo, 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&rsquo, 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).