IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-04797036.html
   My bibliography  Save this paper

Strategie innovative per la logistica: il valore del kitting e assembly nel settore idrotermosanitario

Author

Listed:
  • Angelo Leogrande

    (LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro)

Abstract

L'articolo esplora l'importanza strategica dell'implementazione dei servizi di kitting e assembly per affrontare problematiche di assegnazione delle risorse in un magazzino operante nel settore idrotermosanitario. Si concentra sulla crescente complessità delle operazioni logistiche in un contesto caratterizzato da una domanda sempre più personalizzata e dalla necessità di garantire tempi di consegna rapidi. Attraverso un'analisi approfondita, il lavoro evidenzia come il kitting e l'assembly siano strumenti fondamentali per ottimizzare i flussi operativi, migliorare l'efficienza e soddisfare le aspettative dei clienti. Il kitting viene descritto come il processo di raggruppamento di componenti per assemblaggi specifici, contribuendo alla riduzione dei tempi operativi e minimizzando gli errori umani. L'assembly, d'altro canto, completa il ciclo producendo kit semi-finiti o finiti, pronti per la distribuzione. L'articolo analizza il valore di questa integrazione, mostrando come essa migliori la gestione degli spazi e la tracciabilità dei materiali, oltre a fornire un vantaggio competitivo. La ricerca adotta un approccio olistico, prendendo in esame sia gli aspetti tecnologici, come l'uso di software di gestione logistico avanzato, sia quelli collaborativi, evidenziando l'importanza del coordinamento tra risorse umane e materiali. Inoltre, include casi studio dettagliati che dimostrano i benefici tangibili delle soluzioni implementate, come la riduzione degli errori, l'aumento dell'efficienza e un impatto positivo sulla sostenibilità. Questo lavoro rappresenta un contributo significativo per le aziende che intendono migliorare la gestione logistica, con un focus su innovazione e ottimizzazione dei processi.

Suggested Citation

  • Angelo Leogrande, 2024. "Strategie innovative per la logistica: il valore del kitting e assembly nel settore idrotermosanitario," Working Papers hal-04797036, HAL.
  • Handle: RePEc:hal:wpaper:hal-04797036
    Note: View the original document on HAL open archive server: https://hal.science/hal-04797036v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04797036v1/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Valerio Elia & Maria Grazia Gnoni & Fabiana Tornese, 2024. "On-Demand Warehousing Platforms: Evolution and Trend Analysis of an Industrial Sharing Economy Model," Logistics, MDPI, vol. 8(4), pages 1-16, September.
    2. Ing-Marie Gren & Andreas Brutemark & Annika K. Jägerbrand & Jennie Barthel Svedén, 2020. "Costs of air pollutants from shipping: a meta-regression analysis," Transport Reviews, Taylor & Francis Journals, vol. 40(4), pages 411-428, July.
    3. Bedoui, Adel & Lazar, Nicole A., 2020. "Bayesian empirical likelihood for ridge and lasso regressions," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    4. Antonio Casimiro Caputo & Pacifico Marcello Pelagagge & Paolo Salini, 2021. "A model for planning and economic comparison of manual and automated kitting systems," International Journal of Production Research, Taylor & Francis Journals, vol. 59(3), pages 885-908, February.
    5. Fabian Lorson & Andreas Fügener & Alexander Hübner, 2023. "New team mates in the warehouse: Human interactions with automated and robotized systems," IISE Transactions, Taylor & Francis Journals, vol. 55(5), pages 536-553, May.
    6. Arkadiusz Kawa, 2021. "Fulfilment as Logistics Support for E-Tailers: An Empirical Studies," Sustainability, MDPI, vol. 13(11), pages 1-11, May.
    7. Luay Jum’a & Muath Esam Basheer, 2023. "Analysis of Warehouse Value-Added Services Using Pareto as a Quality Tool: A Case Study of Third-Party Logistics Service Provider," Administrative Sciences, MDPI, vol. 13(2), pages 1-23, February.
    8. Yannick Rothacher & Carolin Strobl, 2024. "Identifying Informative Predictor Variables With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 49(4), pages 595-629, August.
    9. Johannes Bracher & Evan L Ray & Tilmann Gneiting & Nicholas G Reich, 2021. "Evaluating epidemic forecasts in an interval format," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-15, February.
    10. Wu, Jinran & Wang, You-Gan & Tian, Yu-Chu & Burrage, Kevin & Cao, Taoyun, 2021. "Support vector regression with asymmetric loss for optimal electric load forecasting," Energy, Elsevier, vol. 223(C).
    11. Yang, Yu-Chen & Lin, Tsung-I & Castro, Luis M. & Wang, Wan-Lun, 2020. "Extending finite mixtures of t linear mixed-effects models with concomitant covariates," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    12. Eduard Klundt & Neil Towers & Kamal Bechkoum, 2024. "Lean and Agile Supply Strategies in Distribution Centres to Deliver Value-Added Services (VAS)," Logistics, MDPI, vol. 8(3), pages 1-20, July.
    13. Zhang, Yuyang & Schnell, Patrick & Song, Chi & Huang, Bin & Lu, Bo, 2021. "Subgroup causal effect identification and estimation via matching tree," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    14. Mohamed S. Abdalzaher & Moez Krichen & Derya Yiltas-Kaplan & Imed Ben Dhaou & Wilfried Yves Hamilton Adoni, 2023. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey," Sustainability, MDPI, vol. 15(15), pages 1-38, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmad Ali Atieh & Alhareth Abu Hussein & Saheer Al-Jaghoub & Ahmad Fathi Alheet & Murad Attiany, 2025. "The Impact of Digital Technology, Automation, and Data Integration on Supply Chain Performance: Exploring the Moderating Role of Digital Transformation," Logistics, MDPI, vol. 9(1), pages 1-24, January.
    2. Kai Yang & Xue Ding & Xiaohui Yuan, 2022. "Bayesian empirical likelihood inference and order shrinkage for autoregressive models," Statistical Papers, Springer, vol. 63(1), pages 97-121, February.
    3. Chiang, Wen-Hao & Liu, Xueying & Mohler, George, 2022. "Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates," International Journal of Forecasting, Elsevier, vol. 38(2), pages 505-520.
    4. Paolo Giudici & Emanuela Raffinetti, 2025. "RGA: a unified measure of predictive accuracy," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(1), pages 67-93, March.
    5. Sun, Xuting & Kuo, Yong-Hong & Xue, Weili & Li, Yanzhi, 2024. "Technology-driven logistics and supply chain management for societal impacts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    6. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    7. Lu, Shixiang & Xu, Qifa & Jiang, Cuixia & Liu, Yezheng & Kusiak, Andrew, 2022. "Probabilistic load forecasting with a non-crossing sparse-group Lasso-quantile regression deep neural network," Energy, Elsevier, vol. 242(C).
    8. Tim K. Tsang & Qiurui Du & Benjamin J. Cowling & Cécile Viboud, 2024. "An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    10. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    11. Aleksandr Shishkin & Amanda Bleichrodt & Ruiyan Luo & Pavel Skums & Gerardo Chowell & Alexander Kirpich, 2024. "Short-Term Predictions of the Trajectory of Mpox in East Asian Countries, 2022–2023: A Comparative Study of Forecasting Approaches," Mathematics, MDPI, vol. 12(23), pages 1-17, November.
    12. Sarabeth M. Mathis & Alexander E. Webber & Tomás M. León & Erin L. Murray & Monica Sun & Lauren A. White & Logan C. Brooks & Alden Green & Addison J. Hu & Roni Rosenfeld & Dmitry Shemetov & Ryan J. Ti, 2024. "Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    13. Ruisi Nan & Jingwei Wang & Hanfang Li & Youxi Luo, 2025. "Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach," Mathematics, MDPI, vol. 13(14), pages 1-20, July.
    14. Elżbieta Szaruga & Elżbieta Załoga, 2022. "Qualitative–Quantitative Warning Modeling of Energy Consumption Processes in Inland Waterway Freight Transport on River Sections for Environmental Management," Energies, MDPI, vol. 15(13), pages 1-21, June.
    15. Nikos I Bosse & Sam Abbott & Anne Cori & Edwin van Leeuwen & Johannes Bracher & Sebastian Funk, 2023. "Scoring epidemiological forecasts on transformed scales," PLOS Computational Biology, Public Library of Science, vol. 19(8), pages 1-23, August.
    16. Luis A. Barboza & Shu Wei Chou Chen & Marcela Alfaro Córdoba & Eric J. Alfaro & Hugo G. Hidalgo, 2023. "Spatio‐temporal downscaling emulator for regional climate models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    17. Liu, Che & Li, Fan & Zhang, Chenghui & Sun, Bo & Zhang, Guanguan, 2023. "A day-ahead prediction method for high-resolution electricity consumption in residential units," Energy, Elsevier, vol. 265(C).
    18. Kathryn S Taylor & James W Taylor, 2022. "Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-25, March.
    19. Jurgen A. Doornik & Jennifer L. Castle & David F. Hendry, 2021. "Modeling and forecasting the COVID‐19 pandemic time‐series data," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2070-2087, September.
    20. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • L9 - Industrial Organization - - Industry Studies: Transportation and Utilities
    • L90 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - General
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-04797036. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.