Green lean method to identify ecological waste in a nectar factory

Andrei Bancovich Erquínigo

https://orcid.org/0000-0002-9657-0408

Peru

National University of San Marcos image/svg+xml

Industrial Engineering Student - UNMSM

Jorge Ortiz Porras

https://orcid.org/0000-0002-9605-3670

Peru

National University of San Marcos image/svg+xml

Mechatronic Engineer - UNI, PhD in Business Management - UNMSM, Associate and Postgraduate Professor at the Faculty of Industrial Engineering of the UNMSM.

Harold Quintana Saavedra

https://orcid.org/0000-0001-9672-4887

Peru

Pontifical Catholic University of Peru image/svg+xml

Industrial Engineering Student - PUCP

Paola Crispin Chamorro

https://orcid.org/0000-0001-7085-662X

Peru

Peruvian University of Applied Sciences image/svg+xml

Civil Engineering Student - UPC

Rosiand Manrique Alva

https://orcid.org/0000-0002-2480-851X

Peru

National University of San Marcos image/svg+xml

Bachelor's Degree in Occupational Safety and Health Engineering - UNMSM

Pedro Vilca Carhuapuma

https://orcid.org/0009-0006-9496-029X

Peru

National University of San Marcos image/svg+xml

Graduate in Textile Engineering - UNMSM

Submitted: 2023-04-22

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Accepted: 2023-07-20

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Published: 2023-07-31

DOI: https://doi.org/10.4995/ijpme.2023.19598

Copyright (c) 2023 Andrei Bancovich Erquínigo, Jorge Ortiz Porras, Harold Quintana Saavedra, Paola Crispin Chamorro, Rosiand Manrique Alva, Pedro Vilca Carhuapuma

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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Keywords:

Green Lean, Food production, Waste identification, Sustainable process

Supporting agencies:

This research was not funded

Abstract:

Nowadays, the waste of resources has become one of the biggest problems for industries, due to the serious environmental, social and economic consequences it generates. Therefore, to ensure a production based on sustainable processes, it’s essential to have a responsible management of resources, being the first step one of the most important ones, the identification. Thus, the present research work aims to develop and implement a method based on the integration of Green and Lean methodologies to systematically identify ecological waste, taking as a case study a nectar factory in Lima - Peru. Through the implementation of tools such as Environmental Value Stream Mapping, Process Mapping or Failure Mode and Effects Analysis, it was found that the company generated a waste of 1584 litres of water and 38.5 kg of conditioned fruit every month. The identification of green waste is vital, as it is the first link in a long chain that contributes directly to improving the company's efficiency, profitability and reputation, as well as protecting the environment and promoting sustainable development.
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References:

Ahmad, S., Abdullah, A., & Talib, F. (2021). Lean-green performance management in Indian SMEs: a novel perspective using the best-worst method approach. Benchmarking, 28(2), 737-765. https://doi.org/10.1108/BIJ-05-2020-0255

Alexander, P., Antony, J., & Rodgers, B. (2019). Lean Six Sigma for small- and medium-sized manufacturing enterprises: a systematic review. International Journal of Quality and Reliability Management. Emerald Group Holdings Ltd. https://doi.org/10.1108/IJQRM-03-2018-0074

Bennett, M. A., McDermott, R., & Beauregard, M. (2017). The Basics of FMEA. The Basics of FMEA. Productivity Press. https://doi.org/10.1201/b16656

Carbajal, G. (2019). Modelo Lean-Green en el proceso de producción dentro de la industria de confecciones textiles de lencería. [Tesis de Titulación]. Universidad Peruana de Ciencias Aplicadas. http://hdl.handle.net/10757/626171

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

Coudard, A., Corbin, E., de Koning, J., Tukker, A., & Mogollón, J. M. (2021). Global water and energy losses from consumer avoidable food waste. Journal of Cleaner Production, 326. https://doi.org/10.1016/j.jclepro.2021.129342

Facchini, F., Silvestri, B., Digiesi, S., & Lucchese, A. (2023). Agri-food loss and waste management: Win-win strategies for edible discarded fruits and vegetables sustainable reuse. Innovative Food Science & Emerging Technologies, 83, 103235. https://doi.org/10.1016/j.ifset.2022.103235

Garnier, C., Guiga, W., Lameloise, M. L., & Fargues, C. (2023). Water reuse in the food processing industries: A review on pressure-driven membrane processes as reconditioning treatments. Journal of Food Engineering, 344, 111397. https://doi.org/10.1016/j.jfoodeng.2022.111397

Garza-Reyes, J. A., Torres Romero, J., Govindan, K., Cherrafi, A., & Ramanathan, U. (2018). A PDCA-based approach to Environmental Value Stream Mapping (E-VSM). Journal of Cleaner Production, 180, 335-348. https://doi.org/10.1016/j.jclepro.2018.01.121

Gholami, H., Jamil, N., Mat Saman, M. Z., Streimikiene, D., Sharif, S., & Zakuan, N. (2021). The application of Green Lean Six Sigma. Business Strategy and the Environment, 30(4), 1913-1931. https://doi.org/10.1002/bse.2724

Giles, J., Obilcnik, H., Paschoalinoto, N., & Bruno, D. O. (2022). Lean Manufacturing sostenible - metodología de aplicación del Mapeo de Flujo de Valor Sostenible (Sus-VSM). Revista Brasileira De Mecatrônica, 4(3), 01-18. https://revistabrmecatronica.sp.senai.br/ojs/index.php/revistabrmecatronica/article/view/154

Harmon, P. (2014). Business Process Change: A Business Process Management Guide for Managers and Process Professionals: Third Edition. Elsevier Inc. https://doi.org/10.1016/C2013-0-15339-1

Hartmann, C., Lazzarini, G., Funk, A., & Siegrist, M. (2021). Measuring consumers' knowledge of the environmental impact of foods. Appetite, 167. https://doi.org/10.1016/j.appet.2021.105622

Kirby, R. M., Bartram, J., & Carr, R. (2003). Water in food production and processing: Quantity and quality concerns. Food Control, 14(5), 283-299. https://doi.org/10.1016/S0956-7135(02)00090-7

López, I., Figueroa, A., & Corrales, J. (2016). Un mapeo sistemático sobre predicción de calidad del agua mediante técnicas de inteligencia computacional. Revista Ingenierías Universidad de Medellín, 15(28), 35-52. https://doi.org/10.22395/rium.v15n28a2

Martin, K., & Osterling, M. (2014). Value Stream Mapping: How to Visualize Work and Align Leadership for Organizational Transformation. McGraw Hill Professional.

Muñiz-López, H. S., Uresti-Marín, R. M., & Castañón-Rodríguez, J. F. (2021). Uso de las tecnologías de la información y la comunicación como estrategia para reducir el desperdicio de frutas y verduras. CienciaUAT, 178-195. https://doi.org/10.29059/cienciauat.v16i1.1528

Organización de las Naciones Unidas para la Agricultura y la Alimentación. (2019). Pérdidas y desperdicios de alimentos en el mundo. http://www.fao.org/3/CA6030ES/CA6030ES.pdf

Organización de las Naciones Unidas para la Agricultura y la Alimentación. (2018). La agricultura y la gestión del agua. http://www.fao.org/3/i9553es/i9553es.pdf

Ortiz, J., Bancovich, A., Candia, T., Huayanay, L., Moore, R. & Tinoco, O. (2023). Green Lean Six Sigma model for waste reduction of raw material in a nectar manufacturing company of Lima, Peru. Journal of Industrial Engineering and Management, 16(2), 169-185. https://doi.org/10.3926/jiem.4916

Pampanelli, A. B., Found, P., & Bernardes, A. M. (2014). A Lean & Green Model for a production cell. Journal of Cleaner Production, 85, 19-30. https://doi.org/10.1016/j.jclepro.2013.06.014

Rodríguez, Y., Abreu, R. & Franz, M. (2019). Mapeo del Flujo de Valor para el análisis de sostenibilidad en cadenas de suministro agro-alimentarias. Ingeniería Industrial, 40(3), 316-328. https://www.redalyc.org/articulo.oa?id=360461152010

Saetta, S., & Caldarelli, V. (2023). Lean and Green: The Green Foundry Simulation Model. In Procedia Computer Science, 217, 1622-1630. Elsevier B.V. https://doi.org/10.1016/j.procs.2022.12.362

Salvadorinho, J., & Teixeira, L. (2021). Organizational knowledge in the I4.0 using BPMN: A case study. In Procedia Computer Science, 181, 981-988. Elsevier B.V. https://doi.org/10.1016/j.procs.2021.01.266

Sanchez-Marquez, R., Guillem, J.M.A., Vicens-Salort, E., & Vivas, J.J. (2020). A systemic methodology for the reduction of complexity of the balanced scorecard in the manufacturing environment. Cogent Business & Management, 7(1), 1720944. https://doi.org/10.1080/23311975.2020.1720944

Schuster, M. & Torero, M. (2016). Toward a sustainable food system: Reducing food loss and waste. In 2016 Global Food Policy Report. Chapter 3. Pp. 22-31. Washington, D.C.: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/9780896295827_03

SEDAPAL. (2020). Informe de Sostenibilidad 2020. Gobierno del Perú. https://cdn.www.gob.pe/uploads/document/file/1951191/Informe%20Sostenibilidad%202020.pdf.pdf?v=1623874866

Singh, R. K., Kumar Mangla, S., Bhatia, M. S., & Luthra, S. (2022). Integration of green and lean practices for sustainable business management. Business Strategy and the Enviroment, 31(1), 353-370. https://doi.org/10.1002/bse.2897

Skaf, L., Franzese, P. P., Capone, R., & Buonocore, E. (2021). Unfolding hidden environmental impacts of food waste: An assessment for fifteen countries of the world. Journal of Cleaner Production, 310. https://doi.org/10.1016/j.jclepro.2021.127523

Sunder M.V., Ganesh, L.S., & Marathe, R.R. (2019). Lean Six Sigma in Consumer Banking: An Empirical Inquiry. International Journal of Quality & Reliability Management. https://doi.org/10.1108/IJQRM-01-2019-0012

Torielli, R. M., Abrahams, R. A., Smillie, R. W., & Voigt, R. C. (2011). Using lean methodologies for economically and environmentally sustainable foundries. China Foundry. ISSN: 16726421

Zhu, X. Y., Zhang, H., & Jiang, Z. G. (2020). Application of green-modified value stream mapping to integrate and implement lean and green practices: A case study. International Journal of Computer Integrated Manufacturing, 33(7), 716-731. https://doi.org/10.1080/0951192X.2019.1667028

Show more Show less