Abstract
Industrial companies today are facing two important issues: the implementation of 4.0 technologies, that allow to automate and improve plant productivity, and the evaluation of more sustainable products and processes. In light of this, the present work aims at evaluating an industry 4.0 application, the automated guided vehicles (AGV), making considerations from a technological, environmental, economic, and social point of view. The case study is focused on the comparison between two scenarios in an Italian food company: the traditional past scenario, where three forklifts and manpower were used for managing the internal logistics of a production plant, and the current 4.0 scenario in which two AGV and an automatic system for controlling the shape and centring of pallets were introduced. Both solutions were environmentally compared with a life cycle assessment (LCA) approach, by using the software SimaPro 9.1, while, as far as the economic assessment is concerned, a life cycle costing (LCC) was carried out. Furthermore, social considerations on workers’ conditions in the 4.0 scenario were made. The assessment results can be useful to companies which are considering introducing AGVs in material handling, and also contribute to the scientific literature: it is the first time that LCA and LCC, both tools approved by the European community, are used to assess AGVs. Overall, this research is a starting point for answering to the question: “Can the AGV implementation create a more sustainable scenario in the final logistics operations of a food industry?”.
Similar content being viewed by others
Availability of data and material
Data used in this article are primary data given by the company.
References
Ivanov D, Tang C, Dolgui A, Battini D, Das A (2021) Researchers’ perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. Int J Prod Res 59:2055–2278
Bandyopadhyay S (2017) Intelligent vehicles and materials transportation in the manufacturing sector: emerging research and opportunities. intelligent vehicles and materials transportation in the manufacturing sector: emerging research and opportunities 1–230
Eurodrive SEW (2017) I vantaggi degli AGV (Automated Guided Vehicle). Available: https://blog.sew-eurodrive.it/i-vantaggi-degli-agv-automated-guided-vehicle. [Accessed 2021]
Stesi (2020) AGV: cosa sono e perché sono il futuro della logistica. Available: https://blog.stesi.it/gestione-magazzino/agv-cosa-sono-e-perche-sono-il-futuro-della-logistica. [Accessed 2021]
AGVE (2021) Perché scegliere un sistema AGV?. Available: https://www.agve.it/2019/09/20/perche-scegliere-un-sistema-agv/. [Accessed 2021]
Van Geest M, Tekinerdogan B, Catal (2021) Design of a reference architecture for developing smart warehouses inindustry 4.0. Comput Ind 124(103343)
System Ceramics (2019) AGV. Available: https://www.systemceramics.com/sites/default/files/prod/2019-09/2019_09%20AGV%20IT_EN.pdf
United Nations (2015) Risoluzione adottata dall’Assemblea Generale il 25 settembre 2015. Available: https://unric.org/it/wp-content/uploads/sites/3/2019/11/Agenda-2030-Onu-italia.pdf. [Accessed 02 03 2021]
NRCOM (2015) LCA: analisi completa del ciclo di vita del carrello. Available: http://www.forkliftsrl.it/lca-analisi-completa-del-ciclo-di-vita-del-carrello/. [Accessed 2021]
European Commission (2021) European platform on life cycle assessment (LCA). Available: https://ec.europa.eu/environment/ipp/lca.htm. [Accessed 2021]
Life Cycle Initiative (2021) Social life cycle assessment (S-LCA). Available: https://www.lifecycleinitiative.org/starting-life-cycle-thinking/life-cycle-approaches/social-lca/. [Accessed 2021]
European Commission (2021) Life cycle costing. Available: https://ec.europa.eu/environment/gpp/lcc.htm. [Accessed 2021]
Bechtsis D, Tsolakis N, Vlachos D, Iakovou E (2017) Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles. J Clean Prod 142:3970–3984
Choi Y, Xirouchakis P (2015) A holistic production planning approach in a reconfigurable manufacturing system with energy consumption and environmental effects. Int J Comput Integr Manuf 28:379–394
Tsai W, Lu Y (2018) A framework of production planning and control with carbon tax under industry 4.0. Sustainability (Switzerland) 10:3221
Liu B, De Giovanni P (2019) Green process innovation through Industry 4.0 technologies and supply chain coordination. Ann Oper Res
Correia N, Teixeira L, Ramos AL (2020) Implementing an AGV system to transport finished goods to the warehouse. Adv Sci Technol Eng Syst 5:241–247
Graba M, Kelouwani S, Zeghmi L, Amamou A, Agbossou K, Mohammadpour M (2020) Investigating the impact of energy source level on the self-guided vehicle system performances, in the industry 4.0 context. Sustainability (Switzerland) 12:1–21
Søraa R, Fostervold M (2021) Social domestication of service robots: The secret lives of Automated Guided Vehicles (AGVs) at a Norwegian hospital. Int J Hum Comput Stud 152:102627
Zou W, Pan Q, Wang L (2021) An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery. Knowl Based Syst 218:106881
Yue L, Fan H, Ma M (2021) Optimizing configuration and scheduling of double 40 ft dual-trolley quay cranes and AGVs for improving container terminal services. J Clean Prod 292:126019
Guerrieri M, Mauro R, Pompigna A, Isaenko N (2021) Road design criteria and capacity estimation based on autonomous vehicles performances. first results from the european c-roads platform and A22 motorway. Transp Telecommun 22(2):230–243
Farooq B, Bao J, Raza H, Sun Y, Ma Q (2021) Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment. J Manuf Syst 59:98–116
dos Reis W, Morandin JO (2021) Sensors applied to automated guided vehicle position control: a systematic literature review. Int J Adv Manuf Technol 113:21–34
Barak S, Moghdani R, Maghsoudlou H (2021) Energy-efficient multi-objective flexible manufacturing scheduling. J Clean Prod 283:124610
Bizubac D, Hoermann B (2021) Digital disruptive innovation effects in the manufacturing industry. Rev Roum Sci Tech-El 66:41–46
Chiarini A (2021) Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance? Bus Strateg Environ
Brissi SG, Chong OW, Debs L, Zhang J (2021) A review on the interactions of robotic systems and lean principles in offsite construction. Eng Constr Archit Manag
AlZubi A, Alarifi A, Al-Maitah M, Alheyasat O (2021) Multi-sensor information fusion for Internet of Things assisted automated guided vehicles in smart city. Sustain Cities Soc 102539
Ivanov D, Tang C, Dolgui A, Battini D, Das A (2021) Researchers’ perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. Int J Prod Res 59(7):2055–2078
Li X, Hua G, Huang A, Sheu J, Cheng T, Huang F (2020) Storage assignment policy with awareness of energy consumption in the Kiva mobile fulfilment system. Transp Res Part E Logist Transp Rev 144:102158
Graba M, Kelouwani S, Zeghmi L, Amamou A, Agbossou K, Mohammadpour M (2020) Investigating the impact of energy source level on the self-guided vehicle system performances, in the industry 4.0 context. Sustainability (Switzerland) 12(20):1–21
Kaye S, Lewis I, Buckley L, Rakotonirainy A (2020) Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles. Transport Res F Traffic Psychol Behav 74:173–183
Abderrahim M, Bekrar A, Trentesaux D, Aissani N, Bouamrane K (2020) Manufacturing 4.0 operations scheduling with AGV battery management constraints. Energies 13:4948
Meißner M, Massalski L (2020) Modeling the electrical power and energy consumption of automated guided vehicles to improve the energy efficiency of production systems. Int J Adv Manuf Technol 110:481–498
Pérez F, Echeverría J, Lapeña R, Cetina C (2020) Comparing manual and automated feature location in conceptual models: A controlled experiment. Inf Softw Technol 125:106337
Niestrój R, Rogala T, Skarka W (2020) An energy consumption model for designing an AGV energy storage system with a PEMFC stack. Energies 13:3435
Farooq B, Bao J, Ma Q (2020) Flow-shop predictive modeling for multi-automated guided vehicles scheduling in smart spinning cyber–physical production systems. Electronics (Switzerland) 9:799
Ijeh I (2020) A collision-avoidance system for an electric vehicle: a drive-by-wire technology initiative. SN Appl Sci 2:3
Oyekanlu EA et al (2020) A review of recent advances in automated guided vehicle technologies: Integration challenges and research areas for 5G-based smart manufacturing applications. IEEE Access 8:202312–202353
Le CH et al (2020) Challenges and conceptual framework to develop heavy-load manipulators for smart factories. Int J Mechatr Appl Mech 2(8):209–216
Dharmasiri P, Kavalchuk I, Akbari M (2020) Novel implementation of multiple automatedground vehicles trafficreal time control algorithm forwarehouse operations: Djikstra approach. Oper Supply Chain Manag 13:396–405
Sahin B, Soylu A (2020) Multi-layer, multi-segment iterative optimization for maritime supply chain operations in a dynamic fuzzy environment. IEEE Access 9162104:144993–145005
Firlej M, Taeihagh A (2020) Regulating human control over autonomous systems. Regul Gov
Nunes V, Barbosa G (2020) Simulation-based analysis of AGV workload used on aircraft manufacturing system: A theoretical approach. Acta Sci Technol 42:e47034
Correia N, Teixeira L, Ramos A (2020) Implementing an AGV system to transport finished goods to the warehouse. Adv Sci Technol Eng Syst 5(2):241–247
Yue L, Fan H, Zhai C (2020) Joint configuration and scheduling optimization of a dual-trolley quay crane and automatic guided vehicles with consideration of vessel stability. Sustainability (Switzerland) 12:1
Perussi J, Gressler F, Seleme R (2019) Supply chain 4.0: Autonomous vehicles and equipment to meet demand. Int J Supply Chain Manag 8:33–41
Xu W, Guo S (2019) A multi-objective and multi-dimensional optimization scheduling method using a hybrid evolutionary algorithms with a sectional encoding mode. Sustainability (Switzerland) 11:1329
Li G, Lin R, Li M, Sun R, Piao S (2019) A master-slave separate parallel intelligent mobile robot used for autonomous pallet transportation. Appl Sci (Switzerland) 9:368
Kanakavalli P, Vommi V, Medikondu N (2018) Fuzzy heuristic algorithm for simultaneous scheduling problems in flexible manufacturing system. Manag Sci Lett 8:12
Hemmati Far M, Haleh H, Saghaei A (2018) A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy. Sci Irani 25(1):339–358
Hu W, Mao J, Wei K (2017) Energy-efficient rail guided vehicle routing for two-sided loading/unloading automated freight handling system. Eur J Oper Res 258(3):943–957
Bechtsis D, Tsolakis N, Vouzas M, Vlachos D (2017) Industry 4.0: Sustainable material handling processes in industrial environments. Comput Aid Chem Eng 40:2281–2286
Krüger J, Wang L, Verl A, Bauernhansl T, Carpanzano E, Makris S, Fleischer J, Reinhart G, Franke J, Pellegrinelli S (2017) Innovative control of assembly systems and lines. CIRP Ann 66:707–730
Yilmaz O, Oztaysi B, Durmusoglu M, Oner S (2017) Determination of material handling equipment for lean in-plant logistics using fuzzy analytical network process considering risk attitudes of the experts. Int J Ind EngTheory Appl Pract 24:81–122
Schmidt J, Meyer-Barlag C, Eisel M, Kolbe L, Appelrath H (2015) Using battery-electric AGVs in container terminals - Assessing the potential and optimizing the economic viability. Res Transp Bus Manag 17:99–111
Choi YC, Xirouchakis P (2015) A holistic production planning approach in a reconfigurable manufacturing system with energy consumption and environmental effects. Int J Comput Integr Manuf 28:379–394
Chiu M, Yeh L, Lai GJ, Huang BM (2010) Developing an auto-tracking automated guided vehicle carrier using a wave-varied detecting method. Proc Inst Mech Eng C J Mech Eng Sci 224:1349–1357
Ujvari S, Hilmola O-P (2009) Semi-autonomous vehicles with routing flexibility - Functionality and application areas. Int J Serv Oper Manag 5:444–462
Ujvari S, Hilmola OP (2006) Advanced manufacturing simulation: Minor system details can be major issues in the real world. Ind Manag Data Syst 106:1166–1186
Rastegarpanah A et al (2021) Towards robotizing the processes of testing lithium-ion batteries. Proc Inst Mech Eng Part I J Syst Control Eng 235:1309–1325
Sathiya V, Chinnadurai M, Ramabalan S, Appolloni A (2021) Mobile robots and evolutionary optimization algorithms for green supply chain management in a used-car resale company. Environ Dev Sustain 23:9110–9138
Tsolakis N, Zissis D, Papaefthimiou S, Korfiatis N (2021) Towards AI driven environmental sustainability: an application of automated logistics in container port terminals. Int J Prod Res
Mohsin S, Khan I, Ali M (2019) Ergonomics-based working flexibility for automated guided vehicle (AGV) operators. Int J Adv Manuf Technol 103:529–547
Mrugalska B, Stetter R (2019) Health-aware model-predictive control of a cooperative AGV-based production system. Sensors (Switzerland) 19
Lu S, Xu C, Zhong R, Wang L (2018) A passive RFID tag-based locating and navigating approach for automated guided vehicle. Comput Ind Eng 125:628–636
Kabir Q, Suzuki Y (2018) Increasing manufacturing flexibility through battery management of automated guided vehicles. Comput Ind Eng 117:225–236
Mousavi M et al (2017) Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PLoS One 12
Kavakeb S et al (2015) Green vehicle technology to enhance the performance of a European port: A simulation model with a cost-benefit approach. Transp Res Part C Emerg Technol 60:169–188
Stefanini R, Vignali G (2020) Shelf life analysis of a ricotta packaged using modified atmosphere packaging or high pressure processing. Int J Food Eng 16:5–6
Stefanini R, Borghesi G, Ronzano A, Vignali G (2021) Plastic or glass: a new environmental assessment with a marine litter indicator for the comparison of pasteurized milk bottles. Int J Life Cycle Assess 26:767–784
COMIECO (2020) 25° Rapporto di raccolta, riciclo e recupero di carta e cartone (Dati anno 2019)
COREPLA (2020) Rapporto di sostenibilità 2019
ISPRA (2020) Rapporto rifiuti urbani edizione 2019. Available: https://www.isprambiente.gov.it/it/pubblicazioni/rapporti/rapporto-rifiuti-urbani-edizione-2019. [Accessed 2021]
Cacace F, Bottani E, Rizzi A, Vignali G (2020) Evaluation of the economic and environmental sustainability of high pressure processing of foods. Innov Food Sci Emerg Technol 60:102281. https://doi.org/10.1016/j.ifset.2019.102281
Andersson E (2018) Kollmorgen. Available: https://www.kollmorgen.com/it-it/blogs/_blog-in-motion/articles/emma-andersson/la-safety-vision-degli-agv-zero-incidenti/. [Accessed 2021]
SimaPro, Standardised EPDs (2021). [online] https://simapro.com/business/life-cycle-assessments/standardised-epds/
Flash battery (2017) Batteria al piombo vs batteria la litio: perché passare da una batteria al piombo ad una al litio?. Available: https://www.flashbattery.tech/perche-passare-da-una-batteria-al-piombo-ad-una-batteria-al-litio/. [Accessed 2021]
Traelet (2018). Available: https://www.traelet.com/piombo-vs-litio-le-differenze
National Institute for Public Health and the Environment (2011) LCIA: the ReCiPe model. Available: https://www.rivm.nl/en/life-cycle-assessment-lca/recipe. [Accessed 2021]
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval
The article involves no studies on human or animal subjects.
Consent to participate
The authors consent to participate.
Consent for publication
The authors provide their consent to publish this article.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Stefanini, R., Vignali, G. Environmental and economic sustainability assessment of an industry 4.0 application: the AGV implementation in a food industry. Int J Adv Manuf Technol 120, 2937–2959 (2022). https://doi.org/10.1007/s00170-022-08950-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-022-08950-6