Internal logistics process improvement using PDCA: A case study in the automotive sector

Amaral, Vitória P.; Ferreira, Ana C.; Ramos, Bruna Article Internal logistics process improvement using PDCA: A case study in the automotive sector Business Systems Research (BSR) Provided in Cooperation with: IRENET - Society for Advancing Innovation and Research in Economy, Zagreb Suggested Citation: Amaral, Vitória P.; Ferreira, Ana C.; Ramos, Bruna (2022) : Internal logistics process improvement using PDCA: A case study in the automotive sector, Business Systems Research (BSR), ISSN 1847-9375, Sciendo, Warsaw, Vol. 13, Iss. 3, pp. 100-115, https://doi.org/10.2478/bsrj-2022-0027 This Version is available at: https://hdl.handle.net/10419/318805 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 100 Business Systems Research | Vol. 13 No. 3 |2022 Internal Logistics Process Improvement using PDCA: A Case Study in the Automotive Sector Vitória P. Amaral COMEGI, Universidade Lusíada, Portugal Ana C. Ferreira COMEGI, Universidade Lusíada, Portugal ALGORITMI, MEtRICs, University of Minho, Portugal Bruna Ramos COMEGI, Universidade Lusíada, Portugal ALGORITMI, University of Minho, Portugal Abstract Background: The Plan-do-check-act (PDCA) cycle methodology for a continuous improvement project implementation aims for the internal logistics upgrade, which is especially important in the industrial context of a component manufacturing company for the automotive sector. Objectives: The goal is to quantify the gains from waste reduction based on the usage of the PDCA cycle as a tool in the implementation and optimisation of a milk run in an assembly line of a company in the automotive sector by determining the optimal cycle time of supply and the standardisation of the logistic supply process and the materials’ flow. Methods/Approach: The research was conducted through observation and data collection in loco, involving two main phases: planning and implementation. According to the phases of the PDCA cycle, the process was analysed, and tools such as the SIPOC matrix, process stratification, 5S, and visual management were implemented. Results: Using Lean tools, it was possible to reduce waste by establishing concise flows and defining a supply pattern, which resulted in a reduction of movements. The transportation waste was reduced by fixing the position of more than half of the materials in the logistic trailers. The developed Excel simulator provided the logistic train's optimal cycle time. Conclusions: The assembly line supplied by milk-run was fundamental to highlight a range of improvements in the process of internal supply, such as better integration of stock management systems, greater application of quality, or the adoption of better communication systems between the different areas and employees. Keywords: PDCA, Continuous improvement, Logistics, Milk-run, Automotive sector. JEL classification: L6 Industry Studies: Manufacturing (L60 General) Paper type: Research article Received: 25 Jan 2022 Accepted: 06 Nov 2022 Citation: Amaral, V. P., Ferreira, A. C., Ramos, B. (2022), “Internal Logistics Process Improvement using PDCA: A Case Study in the Automotive Sector” Business Systems Research, Vol. 13 No. 3, pp. 100-115. DOI: https://doi.org/10.2478/bsrj-2022-0027 101 Business Systems Research | Vol. 13 No. 3 |2022 Introduction Developed between 1940 and 1950, Lean quickly became a strong and dominant management reference in the industrial context (Garza-Reyes et al., 2018). It represents a philosophy focusing on value creation by employing continuous improvement tools and waste elimination (non-added value) of the production system along the supply chain (Boateng, 2019). Lean practices allow the continuous flow of a company's processes in an integrated way, contributing to its higher performance. The concept of Lean reflects the idea of 'creating more with less, enabling cost reduction, increased quality, and improved delivery times (Abreu et al., 2017). Lean thinking is associated with the Toyota Production System (TPS). The automotive industry was a pioneer in applying this system, which originated in Japan, at the Toyota car plant, just after the Second World War. At that time, the Japanese industry had very low productivity and a considerable lack of labour, which prevented the adoption of the mass-production model (Chiarini et al., 2018; Ohno, 1988). The goal is to make it right the first time, seek effectiveness in the production process, use the minimum necessary resources, reduce lead times, improve productivity and meet the customer requirements (Santos et al., 2015). Since value creation requires pressure on processes over time, the quality of services and products is a derivative of the quality of processes. In contrast, improvement cannot be regarded as a one-time project. Thus, systematic attempts to seek opportunities to eliminate defects’ causes and use new ways to conduct and introduce changes actively must be preconised (BrajerMarczak, 2014). The Lean approach focuses on reducing or eliminating waste, mainly overproduction, overprocessing, transport, movements, waiting, defects, and stock, which leads to product quality and productivity improvement (Oliveira et al., 2019). This methodology is supported by theoretical and empirical evidence from increasing the competitiveness of organisations through the application of tools such as Kaizen, Kanban, 5 S methodology, Poka-Yoke and Andon systems, visual management tools, value stream mapping, supply flow balancing of parts and products, and many others (Bragança et al., 2013; Garza-Reyes et al., 2018; Puchkova et al., 2016; Randhawa & Ahuja, 2017; Veres et al., 2018). In the manufacturing environment, internal logistics is essentially responsible for the operations that affect the performance of assembly lines (Alnahhal et al., 2014) and guarantee efficient material flow (Goldsby & García-Dastugue, 2003). This requires important decisions due to the need to predict what, how much, by whom, where, when, and how to transport the materials, considering the supply and demand requirements (Kluska & Pawlewski, 2018). Several logistic solutions can be implemented to ensure the internal supply of materials in factories. The Milk-run system is a profitable management strategy that uses a logistic vehicle (or logistic train) to meet supply demands in a supply chain. It is a solution commonly used in production systems due to its successful results in providing waste reduction and transport efficiency (Kluska & Pawlewski, 2018; Nemoto et al., 2010). It’s a relatively easy and cost-effective solution to minimise the covered distances between the storage locations and the workstations (Gyulai et al., 2013). When these vehicles' capacity is maximised, their application's advantages directly contribute to implementing one-piece flow production systems (Gotthardt et al., 2019; Kluska et al., 2018). For its implementation, it is required to study the supply process, defined by the production cycle time and materials management. It is necessary to determine how often and in what quantity is necessary to transport the materials from the warehouse 102 Business Systems Research | Vol. 13 No. 3 |2022 (or a storage place, e.g., an intermediate supermarket) to a different point in a factory, attending the routes that provide consumable items in time to production. Following the idea that only consumed materials can be replaced, the principal goal of the milk-run process is to make materials flow faster through the production area, making deliveries in different locations within the same route and the same service period. Thus, milk-run systems are aligned with implementing Lean tools, contributing to reducing the seven wastes, mainly in transport, waiting for time, and stocks (Ivanov et al., 2018; Vicente et al., 2016). There are several methodologies that, when implemented in an integrated way, lead to increased performance outcomes. One of those methodologies is the Plando-check-act (PDCA) cycle. Developed by Edwards Deming in the 1950s, the PDCA cycle is a quality tool, especially useful for promoting continuous improvement (Isniah et al., 2020). It has been used to improve production systems and work management applications and to enhance business organisation. It has also offered the steps as drivers of continuous improvements and the key to a learning culture (Lerche et al., 2020). Four steps define the method - Plan, Do Check, and Action. In the Plan phase, the improvement opportunities are identified and then prioritised. Also, the goals are established, and the processes to achieve specific results are planned (Isniah et al., 2020; Realyvásquez-Vargas et al., 2018). In the second phase (Do), the action plan previously developed is implemented, putting in action all the data collection, measurement techniques, and tools for data analysis. Afterwards, all the results are analysed (Check) through a before-and-after comparison to verify the achieved gains. The last step is the Action stage, where the plan is created to improve and standardise the achieved results (Isniah et al., 2020; Realyvásquez-Vargas et al., 2018). According to Jagusiak-Kocik (2017), the PDCA cycle is a very adaptable methodology. It can be successfully used In continuous improvement processes, during the implementation of changes and innovative solutions, or even during a process improvement review. Mantay de Paula & Feroni (2021) applied the PDCA cycle and a milk-run system in a reverse logistics project in the food industry sector. According to the authors, the PDCA methodology reduced the customers' dissatisfaction with reverse logistics processes, facilitating the identification of the problem's root causes. The study describes the milk-run system efficiency as a solution in the goods returning process. The application of PDCA methodology and the milk run conduced to a faster process with increased levels of customer satisfaction since the product reuse rates and reverse costs of freight were optimised (De Paula & Feroni, 2021). In the automotive sector, for example, Rahim et al. (2016) used the PDCA cycle to improve the quality of the electrodeposition painting process, to reduce operating costs and lead time. By applying the PDCA cycle, the authors identified the most frequent defects and the implemented tools to improve the method quality. The cycle time was reduced by reducing the duration of a few work processes, resulting in less than 33% of direct person-hours and less than 50% in material consumption and consequent cost savings (Rahim et al., 2016). PDCA methodology is usually applied with the resort to visual management practices, 5S methodology, standard work, checklists application, and Six Sigma tools. With this approach, supplier–input–process– output–customer (SIPOC) matrices and control charts can be generated for further analysis (Oliveira et al., 2019; Realyvásquez-Vargas et al., 2018; Uluskan, 2019). The PDCA cycle has proven efficient in many industrial processes (Aichouni et al., 2021). This allows structuring the problem in several steps, making it possible to analyse the root causes of the problem in more detail and create corrective measures to mitigate them. 103 Business Systems Research | Vol. 13 No. 3 |2022 In the literature, several processes are used to study the PDCA cycle with the continuous improvement process. These case studies are reported in a wide range of industrial areas. Antunes Junior & Broday (2019) applied the PDCA cycle in a food company in southern Brazil to solve the problem of excessive waste of sauce used in frozen meals. It was possible to reduce waste by 86.75% by implementing improvements in the operation and sauce dispensing equipment. The PDCA was applied in a company's case study that assembles a set of keys, locks, and handles (Malega et al., 2021). The authors introduced measures to correct the long-term problems and made changes to individual and production control documents. Through the statistical analysis, the authors confirmed the effectiveness of the continuous improvement processes implemented in the first month. Milosevic et al. (2021) implemented the PDCA cycle with Lean tools to ensure the sustainability of the production process of welded excavator frames. The authors refer that the process had a significant performance improvement (10.67%). Due to a growing demand for electronic components, a manufacturing company in Mexico began to detect defects in the electronic board welding process (Realyvásquez-Vargas et al., 2018). The PDCA cycle was applied to three double production lines of the boards, where defects decreased by 65%, 79%, and 77%. The case study presented in this article represents the need to improve an internal process within the company, so it cannot be directly compared with other cases in the literature. However, similar to this case study, the studies above highlight the positive aspects of applying the PDCA cycle and using continuous improvement tools. This way, it is possible to normalise work processes, minimise waste that does not add value to the product, and allow for more efficient and healthy production workflow management. The main purpose of this paper is to explore the use of the PDCA cycle as a tool in the implementation and optimisation of a milk run in the assembly line of a company in the automobile sector by determining the optimal cycle time of supply and the standardisation of the logistic supply process and the materials’ flow. Also, it aims to demonstrate that Lean and Logistics can contribute to improving the internal supply process with simple and cost-effective approaches. The present research is in line with the work reported in the referred literature: applying continuous improvement tools to an industrial context, aiming to reduce cycle times and minimise labour and resource wastes. In addition to the PDCA approach, this paper is focused on applying current tools such as the SIPOC, milk-run systems, and simulators to identify the material needs in industry 4.0. Therefore, this paper falls within the field of applied research. Despite the particular context of the paper application, the study provides an important scientific contribution since the methodology and the developed simulator can be easily adapted to other assembly lines, not only in the electronic components production for the automotive sector. The paper was organised into six sections. After the introduction, section two describes the methodology framework, and section three characterises the case study. The Lean logistics tools implemented in the project were described in section four, mainly milk-run, Kanban, and visual management. The results validation and main conclusions are presented in sections five and six. Research Methodology: PDCA This study is classified as a case study and developed through observation and data collection in loco (Saunders et al., 2007). The continuous improvement project was developed within a company dedicated to producing electromechanical components for vehicles. 104 Business Systems Research | Vol. 13 No. 3 |2022 PDCA cycle was used as the reference method for the continuous improvement project implementation, which included different Lean tools: SIPOC matrix, Chart Control, 5S, and Visual Management. Thus, the wastes from the materials’ flow of the assembly line were effectively identified and measured to formulate and undertake suitable methods to optimise the supply strategy. In the Plan phase, the assembly line was selected as the study object (called from now on line Y), and the supply processes were assessed to identify improvement opportunities. The main problem was the lack of an adjusted cycle time for the internal supply process of the line. Thus, strategies were defined for the optimisation of the milkrun process. In the second stage, phase Do, the data needed to estimate the optimal cycle time were collected, and the defined strategies were developed to improve the milk-run supply process. In the Check phase, the milk-run supply process was again analysed to confirm and evaluate the results obtained by implementing the previous phase's improvements. To validate the implementations, a brief satisfaction survey was developed to evaluate the impact of the changes from the operators' perspective and the assembly line productivity. In the last PDCA stage, the Act phase, an action plan was created to maintain the obtained results regarding the continuous improvement of the supply process for assembly line Y. Case Study Description The case study was carried out for seven months, focusing on analysing the company’s internal supply process (Figure 1). The company uses a milk-run system for the internal supply to deliver different materials in small batches from a central warehouse to the assembly lines, with standard routes and predetermined cycle times. Figure 1 Milk-run supply process circuit Source: Authors’ work The assembly line supply process occurs continuously during the three 8 hours shifts. The logistic operators are responsible for supplying the train and the assembly lines, ensuring that the required components are provided following the production orders. Material management is attained through a Kanban system between the warehouse and the supermarket. Only the materials that have been consumed are replaced. This enables higher vehicle loading rates, low inventory levels, and delivery accuracy, maximising the efficiency of manufacturing continuous flow. The logistic train is also responsible for properly collecting and disposing of wastes from the assembly line and forwarding reusable materials to the recycling area. Thus, the milk-run system is simultaneously used to supply the assembly line and, in the reverse logistic processes, transport materials, such as plastic boxes and other 105 Business Systems Research | Vol. 13 No. 3 |2022 packaging items, as well as reusable materials that have an internal flow (e.g., injected plastic parts trays). Despite the well-defined logistic train route and the use of the supply vehicle, a detailed analysis of the line Y supply process showed discrepancies between the theoretical and effective cycle time. Implementation of Continuous Improvement Tools Different Lean tools were applied during the project development, according to the PDCA cycle steps and considering a continuous improvement approach. Milk-run supply process optimisation The strategy was to effectively identify improvement opportunities for optimising the milk-run process. To do so, a SIPOC matrix was developed. As shown in Table 1, two main improvement opportunities were identified: (1) the methodology to supply the logistic train and the assembly line; and (2) the management of both waste and reusable materials. Table 1 SIPOC matrix to identify improvement opportunities Suppliers Inputs Process Outputs Customers Supermarket Materials to transport Supply the logistic train with material Stocked logistic train Logistic train Logistic train Availability of materials to the assembly line Supply the assembly line Stocked assembly line Assembly line Assembly line Residual materials and reusable materials Supply the logistic train with residual and reusable materials Logistics train loaded with residual and reusable materials Logistic train Logistic train Residual materials and reusable materials Discard residual materials and return reusable materials Discarded waste materials and returned reusables Availability of waste and reusable materials Source: Authors’ work To determine the optimal cycle time for the milk run, it was decided to stratify the process by dividing the cycle time into supply activities, reverse logistic activities, and movements (Figure 2). The assembly line operations' data were collected using a Radio-Frequency Identification (RFID) controller. Data collection was based on a sample of 30 observations, randomly performed during the morning and afternoon shifts. 106 Business Systems Research | Vol. 13 No. 3 |2022 Figure 2 Stratification of cycle time in the milk-run supply process Source: Authors’ work With the collected data, a control chart was prepared (Figure 3) to compare the monthly average cycle times with the theoretical value predetermined by the company managers, a cycle time of 1 hour. The control chart was developed with the historical data from the nine previous months, providing insight into the instability of the milk-run process. There is a discrepancy of 76.7% between the theoretical cycle time (1 hour) and the average calculated value, which corresponds to approximately 1 hour and 47 minutes. This assessment reflects the complexity of the assembling process and the urgent need to study the supply method because different products require managing between 40 to 70 different materials per cycle. Figure 3 Control chart of the milk-run process before improvements, considering the average cycle time, the average standard deviation, and its upper and lower limits Source: Authors’ work 107 Business Systems Research | Vol. 13 No. 3 |2022 It was also necessary to perform a motion study analysis based on the movement diagram of the assembly line to identify possible wastes during the supply process. With this data, a simulator in MS Excel was developed (Figure 4). This simulator considered the assembly line settings, specifying the batches of materials transported by logistic train at each cycle. To do so, several steps were taken into account: 1) Separate materials that are supplied in bags but must be supplied in tubes or carton boxes on the logistic train and assembly line; 2) Sum of all the different materials quantities that are supplied per cycle by the logistic train; 3) Calculate the time for milk run and assembly line replenishment, indicating the daily average restocking activity time. From the simulation program and the defined cycle time, a new work pattern could be determined according to the line needs, promoting process normalisation and movement waste reduction. In addition, labels were developed to identify packaging materials, respecting the quantities necessary for the assembly line to minimise reverse logistics flows. Figure 4 Representation of MS Excel simulator interface for 10 of the 24 simulated cycles for the logistic train Source: Authors’ work Visual management Visual management and 5S methodology were applied to the assembly line and the logistic train to guarantee process standardisation. On the assembly line, the excess material that contributed to waste was removed through the implementation of 5S. When implementing this methodology, the maximum batch quantities of materials can be estimated by considering the requirements of each workstation. Also, different positions were defined for support material in the assembly line. In the logistic trailers, the 5S was implemented to facilitate visual management and vehicle restocking, resulting in a more agile shift change, reducing wastes of overproduction, transport, movements, and the stock itself. 1 2 3 4 5 6 7 8 9 10 Tube Roll 10 411 14 311 11 13 012 9 1 7 2 2 3 5 2 4 1 5 4 3 3 2 5 3 3 4 3 2 5 4 4 5 3 4 4 5 3 5 5 4 5 1 0 1 0 1 0 1 0 1 0 1 6 8 5 5 7 7 4 6 7 7 7 35 34 35 34 35 35 34 35 35 34 35 31 30 31 30 32 29 31 30 32 29 31 00:24 04:01 01:36 04:25 05:37 01:12 04:25 04:25 05:13 00:00 04:49 03:34 00:14 00:14 01:42 00:29 00:29 00:43 01:13 00:29 00:58 00:14 01:13 00:46 00:26 01:20 01:20 00:53 02:14 01:20 01:20 01:47 01:20 00:53 02:14 01:28 00:25 01:40 02:05 01:15 01:40 01:40 02:05 01:15 02:05 02:05 01:40 01:45 00:25 00:25 00:00 00:25 00:00 00:25 00:00 00:25 00:00 00:25 00:00 00:12 00:17 01:43 02:18 01:26 01:26 02:00 02:00 01:09 01:43 02:00 02:00 01:46 00:03 02:14 02:11 02:14 02:11 02:14 02:14 02:11 02:14 02:14 02:11 02:13 00:10 05:10 05:00 05:10 05:00 05:20 04:50 05:10 05:00 05:20 04:50 05:05 16:49 16:13 16:19 18:38 14:57 18:09 16:52 18:35 13:14 18:58 16:52 6:19 6:19 6:19 6:19 6:19 6:19 6:19 6:19 6:19 6:19 6:19 23:09 22:32 22:38 24:57 21:16 24:28 23:11 24:55 19:33 25:17 23:12 Cycles Logistic Train: Average Time to supply - Logistic Train Plastic Box (3) Carton Box ESD (1) Carton Box ESD (2) Blister (2) Blister (1) Movement TOTAL: Blister PCB Blister PCB Plastic Box (3) Carton Box ESD (1) Carton Box ESD (2) Bag Plastic Box (1) Plastic Box (2) Batch Blister (2) Tray (1) Blister (1) Bag Plastic Box (1) 114 Business Systems Research | Vol. 13 No. 3 |2022 Procedia Manufacturing, Vol. 41, pp. 803–810. 23. 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PORTUGUESE SOC OCCUPATIONAL SAFETY HYGIENE, March, Guimarães, Portugal, pp. 23-24. 115 Business Systems Research | Vol. 13 No. 3 |2022 About the authors Vitória P. Amaral (M.Sc.) is a Continuous Improvement Engineer at SolidAl - Condutores Eléctricos, S.A, a Portuguese company specialising in integrated energy transmission and distribution solutions that manufactures power conductors and power cables from low voltage to high voltage up to 400kV. She enrolled at the Universidade Lusíada in 2019, when she started her Master's Degree in Engineering and Industrial Management. In 2020, she developed her master's dissertation in an industrial context, entitled: “Internal Supply Logistics: Case study of an automotive components Industry”. The author can be contacted at [email protected]. Ana C. Ferreira (PhD) is an Assistant Professor at the Faculty of Engineering and Technologies at Lusíada Norte University (Vila Nova de Famalicão Campus). She is a Post-Doc Researcher and collaborator member at ALGORITMI Centre, within the Industrial Engineering and Management research line, and an integrated member of the Mechanical Engineering and Resource Sustainability Center (MEtRICs), both centres from the School of Engineering at the University of Minho. She has 54 papers indexed in Scopus within different fields of knowledge. She has been involved in several research areas, including energy conversion and management; optimisation of renewable cogeneration systems; industrial cost analysis; and, more recently, Lean Management and Logistics applications in industrial contexts. The author can be contacted at acferreir[email protected].pt. Bruna Ramos (PhD) is an Assistant Professor at the Faculty of Engineering and Technologies at Lusíada University, the campus of Vila Nova de Famalicão, and an Invited Professor equivalent to the Assistant Professor at the Production and Systems Department at the University of Minho. Bruna is also an integrated researcher at COMEGI (Center for Research in Organizations, Markets, and Industrial Management) within the Technology Management Group in Process Management – Monitoring, Optimisation, Energy and Modelling, and Industrial Management - Lean, IMS, and Logistics research lines. She collaborates at ALGORITMI Centre, within the System Engineering and Operational Research (SEOR) research line. She has eight indexed papers in Scopus and four index papers in Web of Science. The author can be contacted at [email protected].