Lean transformation and jidoka implemantation in a steel profile production company

The increasing costs aggravate the already challenging competitive conditions, putting businesses at a further disadvantage. In order to survive in the tough competitive environment and maintain price advantages and profit margins, companies must prioritize departing from traditional production models, using limited resources most efficiently, and reducing costs rather than increasing selling prices. The desire to deliver value to customers with timely, in the requested quantity, in customer-defined quality and at minimal cost has led to a profound transformation in the manufacturing sector. Lean production takes a place at the center of this change which aimed at eliminating wastes in production processes. The primary goal of the lean production philosophy is to eliminate or minimize waste in its processes. One of the 8 major wastes in processes is "Defective Production / Scrap" a problem encountered by many companies. Most companies continue their production with high scrap or defect rates. Companies aim to eliminate or minimize these losses by implementing various solutions. Jidoka, one of the fundamental pillar of Lean Production and the Toyota Production System, is One of the most effective techniques that can be employed to eliminate or minimize defective production waste. Jidoka is an automation system that detects faults, defects, etc., in machines or processes, stops the process, and prevents the error from being transferred to subsequent processes. Jidoka can be used not only with machines but also in labor-intensive processes. Moreover, it has begun to be implemented in software architectures recently. In this study, an Overall Equipment Effectiveness (OEE) system has been implemented in the steel profile production industry. First of all, a downtime methodology suitable for the business processes was created. As part of study, a distinction was made between planned and unplanned downtime type. Planned downtimes were identified along with the underlying reasons inherent in the nature of the production processes, along with general definitions applicable to all companies. Next step, an unplanned downtime class was created, and 5 main categories were identified as the top reasons. While determining these main categories, both top reasons applicable to all production companies and sector-specific top reason titles were created. After defining the downtime classifications, the next step was to determine the point where the downtime signal would be received and to carry out the conceptual design for the MES system to calculate availability. For performance calculation, it is necessary to determine the target cycle time for the line. Within this scope, bottleneck points and target cycle times for each product were determined by analyzing the products produced on the line. In cases where multiple product types are produced within a shift, the target cycle time is proportionally weighted based on the net production times of the products and determined as a weighted average. Due to the nature of the hot rolling process, the amount of final product exiting the line will not be equal to the raw material input to the line. The difference in quantity between input and output will be deducted from the quality calculation as production losses (engineering scrap). In addition, if the final product exiting the line is out of tolerance (scrap) or requires reprocessing, it is also designed to be deducted from the quality calculation. After all formulas were determined, the workflow of the created OEE methodology was coded and implemented in the MES system based on the conceptual design. With the establishment of a lean office, the company has embarked on a lean transformation process. In addition to integrating lean production practices (5S, autonomous maintenance, Kaizen, OEE, etc.) into the company, losses are also analyzed, and rapid precaution are taken. Within this scope, the downtime data for three factories within the company in 2022 were analyzed. After considering the analysis results and investment decisions, a pilot factory was selected. After selecting the target factory, downtime and scrap data for the factory were analyzed in detail. Despite the highest percentage of "Changeover" downtime in the downtime data, " Remaining Material on the Line" " downtime loss was chosen as the focal point. This is because there are already existing and ongoing improvement efforts related to the "Changeover" downtime, and the "Remaining Material on the Line" downtime not only creates capacity losses but also leads to quality losses (rolling scrap). Remaining material on the line arises from instabilities that can occur during the rolling process. Main causes include voltage fluctuations in power lines, sudden increases in machine currents, or material passing through a machine at a speed higher than the recipe values for the next machine, preventing the material from entering the next machine. To restart production that has stopped due to remaining material on the line, the remaining material on the line needs to be cleared. During these operations, production cannot take place, leading to capacity losses. Additionally, since the remaining material on the line cannot be reworked or used, it is scrapped (rolling scrap). Andon systems have been installed to quickly respond to increases in current values and these abnormalities on the line. During changeover, the product recipe is called from the control panel of the SCADA system, and the number of rolling stand to be used in the product to be produced and which rolling stand will be used are marked on the recipe. In addition, the current tolerance values for the selected machines are entered as a percentage. If the machine current values exceed the tolerance value in the recipe during production, warning signals are displayed on the screens of the control room. The operator takes necessary actions based on this warning. High machine speeds sometimes do not allow the operator to make necessary interventions, leading to both capacity and quality losses due to material sticking to the line. At this point, the Jidoka system comes into play to minimize the losses experienced. If, after passing through the intermediate shear point and entering the finishing group machines on the rolling line, the material cannot enter the rolling mill due to any reason and accumulates, the operator at the control tower detects this anomaly both through the production simulation screen and by monitoring the line live. The operator presses the button to trigger the intermediate shear. Upon the operator's press, the intermediate shear takes the "cutting" position, thus starting to cut the material coming from behind. This prevents further accumulation of material on the machine. Additionally, with less remaining material on the line, the time required to clear it will be reduced. As a result of the study, the relevant downtime decreased by 12.5%. There was also a 19.5% improvement in scrap quantity. The gains achieved in the study have demonstrated the benefits of integrating lean transformation with digital transformation for companies. Furthermore, the Jidoka technique has been shown to prevent not just quality losses, but also capacity losses. As a result of the gains achieved, it has been decided to extend the implementation to the company's other factories.