IMPROVING DELIVERY SERVICE QUALITY OF LOCAL LOGISTIC SERVICE PROVIDER (LSP) COMPANY USING THE  SIX SIGMA DMAIC METHODOLOGY

Abdul Hakim Amiranto

doi:10.54443/ijebas.v4i6.2394

IMPROVING DELIVERY SERVICE QUALITY OF LOCAL LOGISTIC SERVICE PROVIDER (LSP) COMPANY USING THE SIX SIGMA DMAIC METHODOLOGY

Abdul Hakim Amiranto

This study focuses on improving the delivery service quality and operational performance of PT Jaya Connect Transportindo (PT JCT), a local logistics provider specializing in last-mile delivery for telecommunications equipment. Currently, PT JCT is facing several challenges, including a 9% defect rate and a sigma level of 3.38, resulting in a 91% service level performance. This performance falls short of the company’s goal to achieve a 4% defect rate and a 95% service level, which is the benchmark set by its top competitors. Key issues affecting PT JCT’s performance include a lack of operational staff, heavy reliance on a small number of partners, outdated tracking methods, and the absence of a system to evaluate partner performance. To address these challenges, this study applies the Six Sigma DMAIC methodology—Define, Measure, Analyze, Improve, and Control. Using tools such as the Fishbone Diagram, Statistical Process Control, and Process Capability Analysis, the research identifies the root causes of delivery issues, grouped into five categories: Man, Material, Method, Measurement, and Machine. The study proposes several practical solutions: adding more operational staff, expanding the network of delivery partners, introducing a real-time GPS tracking system, implementing a performance evaluation system for partners with clear KPIs, and ensuring vehicle reliability through regular inspections. These improvements are accompanied by a structured implementation plan to ensure their successful execution and long-term sustainability. By adopting these solutions, PT JCT can significantly reduce its defect rate, improve service quality, and achieve its performance targets. This research highlights how logistics companies can use Six Sigma tools to overcome operational challenges, enhance customer satisfaction, and maintain a competitive edge in a demanding market.

Keywords: Six Sigma, DMAIC, logistics service quality, process improvement, operational performance.

Wibowo, A. A., & Suryanegara, M. (2016). On developing the model of smart logistic transport in Indonesia. In Proceedings of the IEEE International Conference on Management of Innovation and Technology (ICMIT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICMIT.2016.7604991

Ślusarczyk, B., Tvaronavičienė, M., Ul Haque, A., & Oláh, J. (2020). Predictors of Industry 4.0 technologies affecting logistic enterprises’ performance: International perspective from economic lens. Technological and Economic Development of Economy, 26(6), 1263–1283. https://doi.org/10.3846/tede.2020.13376

Antony, J. (2006). Six sigma for service processes. Business Process Management Journal, 12(2), 234–248. https://doi.org/10.1108/14637150610657558

Maulana, M. I., & Wahyuni, H. C. (2021). Improving the quality of the goods delivery supply chain system with the integration of Lean Six Sigma and AHP methods. Procedia of Engineering and Life Science, 1(1), Seminar Nasional & Call Paper Fakultas Sains dan Teknologi (SENASAINS 1st), Universitas Muhammadiyah Sidoarjo.

Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: An overview. Procedia Manufacturing, 13, 1245–1252. https://doi.org/10.1016/j.promfg.2017.09.045

Mandal, P. (2012). Improving process improvement: Executing the analyze and improve phases of DMAIC better. International Journal of Lean Six Sigma, 3(3), 231–250. https://doi.org/10.1108/20401461211282727

PWC, (2023): Supply chain: Indonesia Chamber of Commerce & Industry predicts logistics to grow 6%. Available at < https://www.pwc.com/id/en/media-centre/infrastructure-news/july-2023/supply-chain-indonesian-chamber-of-commerce-and-industry-predicts-logistics-to-grow-6-percent.html> (Accessed on 1 November 2024)

Brugha, R., & Varvasovszky, Z. (2000). Stakeholder analysis: a review.. Health policy and planning, 15 3, 239-46 . https://doi.org/10.1093/HEAPOL/15.3.239.

Grimble, R., & Wellard, K. (1997). Stakeholder methodologies in natural resource management: a review of principles, contexts, experiences and opportunities. Agricultural Systems, 55, 173-193. https://doi.org/10.1016/S0308-521X(97)00006-1.

Aykroyd, R., Leiva, V., & Ruggeri, F. (2019). Recent developments of control charts, identification of big data sources and future trends of current research. Technological Forecasting and Social Change. https://doi.org/10.1016/J.TECHFORE.2019.01.005.

Heizer, J., Render, B., & Munson, C. (2017). Operations management: Sustainability and supply chain management (12th ed.). Boston: Pearson.

Rosing, M., White, S., Cummins, F., & Man, H. (2015). Business Process Model and Notation - BPMN. , 429-453. https://doi.org/10.1016/B978-0-12-799959-3.00021-5.

Flynn, B., Schroeder, R., & Sakakibara, S. (1994). A FRAMEWORK FOR QUALITY MANAGEMENT RESEARCH AND AN ASSOCIATED MEASUREMENT INSTRUMENT. Journal of Operations Management, 11, 339-366. https://doi.org/10.1016/S0272-6963(97)90004-8.

Bisgaard, S. (2007). Quality management and Juran's legacy. Quality and Reliability Engineering International, 23. https://doi.org/10.1080/08982110802317398.

Hamid, S., Isa, S., Chew, B., & Altun, A. (2019). Quality Management Evolution from the Past to Present: Challenges for Tomorrow. Organizacija, 52, 157 - 186. https://doi.org/10.2478/orga-2019-0011.

Gunasekaran, A., Subramanian, N., & Ngai, W. (2019). Quality management in the 21st century enterprises: Research pathway towards Industry 4.0. International Journal of Production Economics. https://doi.org/10.1016/J.IJPE.2018.09.005.

Tarí, J., Molina, J., & Castejón, J. (2007). The relationship between quality management practices and their effects on quality outcomes. Eur. J. Oper. Res., 183, 483-501. https://doi.org/10.1016/j.ejor.2006.10.016.

Ebrahimi, M., & Sadeghi, M. (2013). Quality management and performance: An annotated review. International Journal of Production Research, 51, 5625 - 5643. https://doi.org/10.1080/00207543.2013.793426.

Purba, H., Nindiani, A., Trimarjoko, A., Jaqin, C., Hasibuan, S., & Tampubolon, S. (2021). Increasing Sigma levels in productivity improvement and industrial sustainability with Six Sigma methods in manufacturing industry: A systematic literature review. Advances in Production Engineering & Management. https://doi.org/10.14743/apem2021.3.402.

Muralidharan, K. (2015). Sigma Level Estimation. In: Six Sigma for Organizational Excellence. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2325-2_12

Mehrjerdi, Y. (2011). Six‐Sigma: methodology, tools and its future. Assembly Automation, 31, 79-88. https://doi.org/10.1108/01445151111104209

Corradini, F., Fornari, F., Polini, A., Re, B., Tiezzi, F., & Vandin, A. (2021). A formal approach for the analysis of BPMN collaboration models. J. Syst. Softw., 180, 111007. https://doi.org/10.1016/j.jss.2021.111007.

Shinde, D., Ahirrao, S., & Prasad, R. (2018). Fishbone Diagram: Application to Identify the Root Causes of Student–Staff Problems in Technical Education. Wireless Personal Communications, 100, 653 - 664. https://doi.org/10.1007/s11277-018-5344-y.

M., & Atika, N. (2020). Application of six sigma method to minimize risk of rejection product: A case in cheese industry of company X. IOP Conference Series: Earth and Environmental Science, 472. https://doi.org/10.1088/1755-1315/472/1/012058.

Hia, S. (2023). Reveal Six Sigma Implementations in Indonesia Manufacturing Industries. Proceedings of the International Conference on Industrial Engineering and Operations Management. https://doi.org/10.46254/ap03.20220350.