Model to Reduce Delays in Natural Gas Installation Works by Applying Lean Tools

Model to Reduce Delays in Natural Gas Installation Works by Applying Lean Tools

  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-3
Year of Publication : 2023
Author : Fiorella Morales-Chuquihuanga, Hernán Quispe-Tarmeño, Alberto Flores-Perez, José C. Alvarez
DOI : 10.14445/22315381/IJETT-V71I3P219

How to Cite?

Fiorella Morales-Chuquihuanga, Hernán Quispe-Tarmeño, Alberto Flores-Perez, José C. Alvarez, "Model to Reduce Delays in Natural Gas Installation Works by Applying Lean Tools," International Journal of Engineering Trends and Technology, vol. 71, no. 3, pp. 184-196, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I3P219

Abstract
The construction industry is one of Peru's most relevant investment and production sectors, as it contributes to the economy and helps generate jobs and rent for the population. However, statistical research shows that 50.1% of the entire industrial sector reports not storing their materials. The remaining percentage do not use inventory management techniques, resulting in a great shortage of materials in their production. These incidents cause companies in the construction sector to generate delays in the completion of the works, preventing them from meeting the projected demand and causing severe economic losses for the companies. In this sense, to address the problem posed, a solution model was developed with lean tools: standardization of work, adjusted forecasting of demand, inventory management, approval, and homologation of suppliers. It is worth mentioning that the company under study initially obtained a total of 11% delay in the works. However, due to the implementation of the improvement model, a decrease of 6% was obtained due to scheduled replenishments, accurate demand forecasts, satisfied purchase orders and on-time delivery of materials. In conclusion, with this new solution, it is sought that similar companies can provide their services without presenting non-compliance in their work, generating an important level of competition in the market.

Keywords
Construction industry, Inventory management, Lean, Standard work, Supplier management.

References
[1] Daniel Delgado Camacho et al., “Applications of Additive Manufacturing in the Construction Industry – A Forward-Looking Review,” Automation in Construction, vol. 89, pp. 110–119, 2018. Google Scholar | CrossRef | Publisher Link
[2] BCPRData (2019-2020). Domestic Demand Indicator. [Online]. Available: https://estadisticas.bcrp.gob.pe/estadisticas/series/mensuales/resultados/PN01774AM/html
[3] INEI, National Production, Technical Report, 2020. [Online]. Available: //m.inei.gob.pe/media/principales_indicadores/10-informe-tecnico-produccion-nacional-ago-2020.pdf
[4] Adnan Enshassi, Sherif Mohamed, and Saleh Abushaban, “Factors Affecting the Performance of Construction Projects in the Gaza Strip,” Journal of Civil Engineering and Management, vol. 15, no. 3, pp. 269–280, 2009. Google Scholar | CrossRef | Publisher Link
[5] Jennifer Li, David Greenwood, and Mohamad Kassem, “Blockchain in the Built Environment and Construction Industry: A Systematic Review, Conceptual Models and Practical Use Cases,” Automation in Construction, vol. 102, pp. 288–307, 2019. Google Scholar | CrossRef | Publisher Link
[6] Nutchapongpol Kongchasing, and Gritsada Sua-Iam, “The Major Causes of Construction Delays Identified Using the Delphi Technique: Perspectives of Contractors and Consultants in Thailand,” International Journal of Civil Engineering, vol. 19, pp. 319-338, 2020. Google Scholar | CrossRef | Publisher Link
[7] Shamas‐Ur‐Rehman Toor, and Stephen O. Ogunlana, “Problems Causing Delays in Major Construction Projects in Thailand,” Construction Management and Economics, vol. 26, no. 4, pp. 395–408, 2008. Google Scholar | CrossRef | Publisher Link
[8] Raihan Maskuriy et al., “Industry 4.0 for the Construction Industry—How Ready Is the Industry?,” Applied Sciences, vol. 9, no. 14, 2819. Google Scholar | CrossRef | Publisher Link
[9] Yulia Panova, and Per Hilletofth, "Managing Supply Chain Risks and Delays in Construction Project," Industrial Management & Data Systems, vol. 118, no. 7, pp. 1413-1431, 2018. Google Scholar | CrossRef | Publisher Link
[10] Giulia Baruffaldi et al., "Warehousing Process Performance Improvement: A Tailored Framework for 3PL," Business Process Management Journal, vol. 26, no. 6, pp. 1619-1641, 2020. Google Scholar | CrossRef | Publisher Link
[11] Santu Kar, and Kumar Neeraj Jha, “Investigation into Lead Time of Construction Materials and Influencing Factors,” Journal of Construction Engineering and Management, vol. 147, no. 3, 2021. Google Scholar | CrossRef | Publisher Link
[12] Phuoc Luong Le et al., “Integrated Construction Supply Chain: An Optimal Decision-Making Model with Third-Party Logistics Partnership,” Construction Management and Economics, pp. 133–155, 2021. Google Scholar | CrossRef | Publisher Link
[13] Sherif Mostafa et al., “Leagile Strategies for Optimizing the Delivery of Prefabricated House Building Projects,” International Journal of Construction Management, vol. 20, no. 8, pp. 867–881, 2020. Google Scholar | CrossRef | Publisher Link
[14] M. S Bajjou, and A. Chafi, “The Potential Effectiveness of Lean Construction Principles in Reducing Construction Process Waste: An Input-Output Model,” Journal of Mechanical Engineering and Sciences, vol. 12, pp. 4141-4160, 2018. Google Scholar | CrossRef | Publisher Link
[15] Patrick Dallasega, Erwin Rauch, and Marco Frosolini, “A Lean Approach for Real-Time Planning and Monitoring in Engineer-to-Order Construction Projects,” Buildings, vol. 8, no. 3, p. 38, 2018. Google Scholar | CrossRef | Publisher Link
[16] Zhenwu Zhang, Lili Zhuang, and Chao-Po Lin, “Roles of MicroRNAs in Establishing and Modulating Stem Cell Potential,” International Journal of Molecular Sciences, vol. 21, no. 15, p. 3894, 2019 Google Scholar | CrossRef | Publisher Link
[17] Sheikh-Zadeh, A., and Rossetti, M. D. “Classification Methods for Problem Size Reduction in Spare Part Provisioning,” International Journal of Production Economics, vol. 219, pp. 99-114, 2019. Google Scholar | CrossRef | Publisher Link
[18] Abu Hashan Md Mashud, “An EOQ Deteriorating Inventory Model with Different Types of Demand and Fully Backlogged Shortages,” International Journal of Logistics Systems and Management, vol. 36, no. 1, pp. 16-45, 2020. Google Scholar | CrossRef | Publisher Link
[19] G. Karakatsoulis, and K. Skouri, “Optimal Reorder Level and Lot Size Decisions for an Inventory System with Defective Items,” Applied Mathematical Modelling, vol. 92, pp. 651-668, 2021. Google Scholar | CrossRef | Publisher Link
[20] Imre Dobos, and Gyöngyi Vörösmarty, “Inventory-Related Costs in Green Supplier Selection Problems with Data Envelopment Analysis (DEA),” International Journal of Production Economics, vol. 209, pp. 374-380, 2019. Google Scholar | CrossRef | Publisher Link
[21] Adnan Aktepe, Emre Yanık, and Süleyman Ersöz, “Demand Forecasting Application with Regression and Artificial Intelligence Methods in a Construction Machinery Company,” Journal of Intelligent Manufacturing, vol. 32, no. 6, pp. 1587–1604, 2021. Google Scholar | CrossRef | Publisher Link
[22] Bernhard Roßmann et al., “The Future and Social Impact of Big Data Analytics in Supply Chain Management: Results from a Delphi Study,” Technological Forecasting and Social Change, vol. 130, pp. 135-149, 2018. Google Scholar | CrossRef | Publisher Link
[23] Tomas Eloy Salais-Fierro et al., “Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry,” Applied Sciences, vol. 10, no. 3, p. 829, 2020. Google Scholar | CrossRef | Publisher Link
[24] Mustafa Akpınar, and Nejat Yumuşak, “Daily Basis Mid-Term Demand Forecast of City Natural Gas Using Univariate Statistical Techniques,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 35, no. 2, pp. 725-741, 2020. Google Scholar | CrossRef | Publisher Link
[25] Nicole Franziska Richter et al., “Organizational Structure Characteristics’ Influences on International Purchasing Performance in Different Purchasing Locations,” Journal of Purchasing and Supply Management, vol. 25, no. 4, p. 100523, 2019. Google Scholar | CrossRef | Publisher Link
[26] Rahul S. Mor et al., “Productivity Gains through Standardization-of-Work in a Manufacturing Company,” Journal of Manufacturing Technology Management, vol. 30, no. 6, pp. 899-919, 2019. Google Scholar | CrossRef | Publisher Link
[27] Shuwei Jing et al., “Investigating the Effect of Value Stream Mapping on Procurement Effectiveness: A Case Study,” Journal of Intelligent Manufacturing, vol. 32, pp. 935-946, 2021. Google Scholar | CrossRef | Publisher Link
[28] Florian Bienhaus, and Abubaker Haddud, “Procurement 4.0: Factors Influencing the Digitisation of Procurement and Supply Chains,” Business Process Management Journal:Bradford, vol. 24, no. 4, pp. 965-984, 2018. Google Scholar | CrossRef | Publisher Link
[29] A. Mohammed et al., “An Integrated Methodology for a Sustainable Two-Stage Supplier Selection and Order Allocation Problem,” Journal of Cleaner Production, vol. 192, pp. 99–114, 2018. Google Scholar | CrossRef | Publisher Link
[30] Antonio Moreno-Torres Gálvez, “WACC Regulatory Application, ICE Economic Bulletin,” Spanish Commercial Information, no. 3137, pp. 57-68, 2021. Google Scholar | Publisher Link
[31] Saikat Anjan Roy et al., “A Framework for Sustainable Supplier Selection with Transportation Criteria,” International Journal of Sustainable Engineering, vol. 13, no. 2, pp. 77–92, 2020. Google Scholar | CrossRef | Publisher Link
[32] Shubham Gupta, Umang Soni, and Girish Kumar, “Green Supplier Selection Using Multi-Criterion Decision Making Under Fuzzy Environment: A Case Study in Automotive Industry,” Computers & Industrial Engineering, vol. 136, pp. 663-680, 2019. Google Scholar | CrossRef | Publisher Link
[33] Sourav Chatterjee, and Roshin P Raj, “Dominant Modes of Climate Variabilities in the North Atlantic Region from Emperical Orthogonal Analysis of Sea Level Pressure anomaly during 1993-2016,” National Centre for Antarctic and Ocean Research, Goa, India, PANGAEA, vol. 15, no. 6, pp. 1729-1744, 2019.
CrossRef | Publisher Link
[34] Roozbeh Hosseini, and Ali Shourideh, “Retirement Financing: An Optimal Reform Approach,” Econometrica, vol. 87, no. 4, pp. 1205–1265, 2019. Google Scholar | CrossRef | Publisher Link
[35] Paul Lira Briceño, “Evaluation of Investment Projects,” Peruvian University of Applied Sciences (UPC), 2021. Google Scholar | CrossRef | Publisher Link
[36] R. Sundararajan, S. Vaithyasubramanian, and A. Nagarajan, “Impact of Delay in Payment, Shortage and Inflation on an EOQ Model with Bivariate Demand,” Journal of Management Analytics, vol. 8, no. 2, pp. 1–28, 2020. Google Scholar | CrossRef | Publisher Link
[37] S.Maheswaran et al., "Lean Implementation In Production Unit (Round Tool Manufacture)," SSRG International Journal of Mechanical Engineering, vol. 1, no. 7, pp. 1-10, 2014. CrossRef | Publisher Link
[38] S. Nallusamy, and Parthasarathi Chakraborty, "Minimization of Rejection Rate and Lead Time in Medium Scale Foundry Industry by using Lean Manufacturing Practices," SSRG International Journal of Mechanical Engineering, vol. 7, no. 11, pp. 1-12, 2020. CrossRef | Publisher Link
[39] Orhan Ekren, and Banu Yetkin Ekren, “Size Optimization of a PV/Wind Hybrid Energy Conversion System with Battery Storage Using Response Surface Methodology,” Applied Energy, vol. 85, no. 11, pp. 1086–1101, 2008. Google Scholar | CrossRef | Publisher Link
[40] OSINERGMIN, Information for Consumers and Interested in Gas Natural Residential, 2019. [Online]. Available: http://gasnatural.osinerg.gob.pe/contenidos/consumidores_residenciales/registro_instalacion.html#interior_seccion_total
[41] Walter Andía Valencia, “Profitability Indicator of Projects: The Net Present Value (Van) or the Economic Value Added (eva),” Industrial data, vol. 14, no. 1, pp. 15-18, 2011. Google Scholar | Publisher Link
[42] Shaik Dawood A.K et al., "Role of Lean Manufacturing Tools in Soft Drink Company," SSRG International Journal of Mechanical Engineering, vol. 5, no. 1, pp. 1-7, 2018. Google Scholar | CrossRef | Publisher Link
[43] P. D. D. Dominic, S. Kaliyamoorthy, and M. Saravana Kumar, “Efficient Dispatching Rules for Dynamic Job Shop Scheduling,” International Journal of Advanced Manufacturing Technology, vol. 24, no. 1-2, pp. 70–75, 2004. Google Scholar | CrossRef | Publisher Link
[44] Ghio Castillo, and Virgilio A., Productivity in Construction Projects: Diagnosis, Critique and Proposal, Lima: PUCP, Fondo Editorial, 2001.
[45] P González et al., “Learning based on Simulation and the Contribution of Educational Theories,” Revised Spacious, vol. 39, no. 20, p. 37, 2018. Google Scholar | Publisher Link