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

Leogrande, Angelo (2024): Strategie innovative per la logistica: il valore del kitting e assembly nel settore idrotermosanitario.

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.

• Item Type: MPRA Paper
• Language: Italian
• Keywords: Kitting, Assembly, Idrotermosanitario, Machine Learning Regressions, Machine Learning Clustering.
• Subjects: L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L90 - General; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L91 - Transportation: General; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L92 - Railroads and Other Surface Transportation; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L93 - Air Transportation

References

Abdalzaher, M. S., Krichen, M., Yiltas-Kaplan, D., Ben Dhaou, I., & Adoni, W. Y. H. (2023). Early detection of earthquakes using iot and cloud infrastructure: A survey. Sustainability, 15(15), 11713.

Agarwal, A., Kenney, A. M., Tan, Y. S., Tang, T. M., & Yu, B. (2023). MDI+: A flexible random forest-based feature importance framework. arXiv preprint arXiv:2307.01932.

Ahmed, S., Parvathaneni, D., & Shareef, I. (2023). Reorganization of inventory to improve kitting efficiency and maximize space utilization. Manufacturing Letters, 35, 1366-1377.

Aldrich, C. (2020). Process variable importance analysis by use of random forests in a shapley regression framework. Minerals, 10(5), 420.

Ali, S. S., Kaur, R., Gupta, H., Ahmad, Z., & Jebahi, K. (2024). A decision-making framework for determinants of an organisation’s readiness for smart warehouse. Production Planning & Control, 35(14), 1887-1908.

Almasov, A., & Onur, M. (2023, June). Life-Cycle Production Optimization with Nonlinear Constraints Using a Least-Squares Support-Vector Regression Proxy. In SPE EuropEC-Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition. OnePetro.

Altieri, F., Pietracaprina, A., Pucci, G., & Vandin, F. (2021). Scalable distributed approximation of internal measures for clustering evaluation. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) (pp. 648-656). Society for Industrial and Applied Mathematics.

Amaldi, E., Consolo, A., & Manno, A. (2023). On multivariate randomized classification trees: l0-based sparsity, vc dimension and decomposition methods. Computers & Operations Research, 151, 106058.

Argente, J. N., Fortus, C. M., & Lacsamana, P. M. (2023). FAL Services: Improving Productivity and Efficiency through Maximizing Facilities using Third Party Logistics. Ani: Letran Calamba Research Report, 19(1), 1-1.

Asamov, T., & Ben-Israel, A. (2022). A probabilistic ℓ1 method for clustering high-dimensional data. Probability in the Engineering and Informational Sciences, 36(2), 433-448.

Baglio, M., Creazza, A., & Dallari, F. (2024). ‘Logistics 4.0’technologies in the 3PL industry: a maturity model. Production Planning & Control, 1-17.

Bedoui, A., & Lazar, N. A. (2020). Bayesian empirical likelihood for ridge and lasso regressions. Computational Statistics & Data Analysis, 145, 106917.

Berroir, F., Guernaccini, P., Boje, C., & Maatar, O. (2021, July). Reducing construction logistics costs and embodied carbon with CCC and kitting: a case study. In Proc. 29th Annual Conference of the International Group for Lean Construction (IGLC) (pp. 935-944).

Bicego, M., & Escolano, F. (2021, January). On learning random forests for random forest-clustering. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 3451-3458). IEEE.

Bogue, R. (2022). Warehouse robot market boosted by Covid pandemic and technological innovations. Industrial Robot: the international journal of robotics research and application, 49(2), 181-186.7

Bogue, R. (2024). The role of robots in logistics. Industrial Robot: the international journal of robotics research and application, 51(3), 381-386.

Bortolini, M., Faccio, M., Gamberi, M., & Pilati, F. (2020). Assembly kits with variable part physical attributes: warehouse layout design and assignment procedure. Assembly automation, 40(6), 857-868.

Bottin, M., Faccio, M., Minto, R., & Rosati, G. (2021). Sales kit automated production: An integrated procedure for setup reduction in case of high products variety. Applied Sciences, 11(21), 10110.

Bracher, J., Ray, E. L., Gneiting, T., & Reich, N. G. (2021). Evaluating epidemic forecasts in an interval format. PLoS computational biology, 17(2), e1008618.

Bucci, A., Palomba, G., & Rossi, E. (2022). starvars: An R Package for Analysing Nonlinearities in Multivariate Time Series. THE R JOURNAL, 14, 208-226.

Bueno Viso, M. (2022). Automated AGS Kitting Station.

Bushra, A. A., & Yi, G. (2021). Comparative analysis review of pioneering DBSCAN and successive density-based clustering algorithms. IEEE Access, 9, 87918-87935.

Buzu, A. (2021). The effect of Warehousing management on Warehouse performance. Available at SSRN 3951785.

Caputo, A. C., Pelagagge, P. M., & Salini, P. (2021). A model for planning and economic comparison of manual and automated kitting systems. International Journal of Production Research, 59(3), 885-908.

Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert systems with applications, 40(1), 200-210.

Chakraborty, M., Shakir Mahmud, M., Gates, T. J., & Sinha, S. (2023). Analysis and prediction of human mobility in the United States during the early stages of the COVID-19 pandemic using regularized linear models. Transportation research record, 2677(4), 380-395.

Chang, S. C., Chuang, W. C., & Jeng, J. T. (2023). New Interval Improved Fuzzy Partitions Fuzzy C-Means Clustering Algorithms under Different Distance Measures for Symbolic Interval Data Analysis. Applied Sciences, 13(22), 12531.

Cheng, D., Xu, R., Zhang, B., & Jin, R. (2023). Fast density estimation for density-based clustering methods. Neurocomputing, 532, 170-182.

Choi, Y. G., Ahn, S., & Kim, J. (2023). Model-based clustering of mixed data with sparse dependence. IEEE Access.

Coe, N. M. (2021). Coping with commoditization: The third-party logistics industry in the Asia-Pacific. Competition & Change, 25(3-4), 281-307.

Côme, E., Jouvin, N., Latouche, P., & Bouveyron, C. (2021). Hierarchical clustering with discrete latent variable models and the integrated classification likelihood. Advances in Data Analysis and Classification, 15(4), 957-986.

Costa, M., Pinto, V. H., & Gonçalves, G. (2024, June). Wireless Localization System Applied to a Kitting Pick-to-light System. In International Conference Innovation in Engineering (pp. 252-263). Cham: Springer Nature Switzerland.

Czermański, E., Cirella, G. T., Oniszczuk-Jastrząbek, A., Pawłowska, B., & Notteboom, T. (2021). An energy consumption approach to estimate air emission reductions in container shipping. Energies, 14(2), 278.

Dahl, D. B., Andros, J., & Carter, J. B. (2023). Cluster analysis via random partition distributions. Statistical Analysis and Data Mining: The ASA Data Science Journal, 16(2), 135-148.

Dakhli, Z., & Lafaj, Z. (2022). A Lean-Based View. Lean Construction 4.0: Driving a Digital Revolution of Production Management in the AEC Industry, 82.

Dakhli, Z., & Lafhaj, Z. (2022). The Interplay between Construction Supply Chain and BIM through Kitting: A Lean-Based View. In Lean Construction 4.0 (pp. 82-97). Routledge.

de Amorim, R. C., & Makarenkov, V. (2021). Improving cluster recovery with feature rescaling factors. Applied Intelligence, 51, 5759-5774.

Deng, D. (2020, September). DBSCAN clustering algorithm based on density. In 2020 7th international forum on electrical engineering and automation (IFEEA) (pp. 949-953). IEEE.

Desu, S. S. T., Srijith, P. K., Rao, M. P., & Sivadasan, N. (2021, June). Adiabatic quantum feature selection for sparse linear regression. In International Conference on Computational Science (pp. 98-112). Cham: Springer International Publishing.

Dey, S., De, S., & Paul, S. (2021, January). A new approach of data clustering using quantum inspired particle swarm optimization based fuzzy c-means. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 59-64). IEEE.

Dhanaraj, R. K., Ramakrishnan, V., Poongodi, M., Krishnasamy, L., Hamdi, M., Kotecha, K., & Vijayakumar, V. (2021). Random forest bagging and x‐means clustered antipattern detection from SQL query log for accessing secure mobile data. Wireless communications and mobile computing, 2021(1), 2730246.

Eibeck, A., Shaocong, Z., Mei Qi, L., & Kraft, M. (2024). Research data supporting" A Simple and Efficient Approach to Unsupervised Instance Matching and its Application to Linked Data of Power Plants".

El Moussaoui, S., Lafhaj, Z., Leite, F., Fléchard, J., & Linéatte, B. (2021). Construction logistics centres proposing kitting service: Organization analysis and cost mapping. Buildings, 11(3), 105.

Elia, V., Gnoni, M. G., & Tornese, F. (2024). On-Demand Warehousing Platforms: Evolution and Trend Analysis of an Industrial Sharing Economy Model. Logistics, 8(4), 93.

Fan, Z., & Lu, L. (2022, April). Tourism Data Clustering Analysis Based on Random Forest Algorithm. In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 1211-1214). IEEE.

Fatima, U., Mohammed, D., & Shareef, I. (2024). A holistic approach to kitting cart optimization and steel receiving analysis for process improvement. Manufacturing Letters, 41, 1716-1727.

Ferrari, A., Mangano, G., Zenezini, G., & Carlin, A. (2022). A real simulation of automated warehouses processes: an academic experience with engineering students. ... SUMMER SCHOOL FRANCESCO TURCO. PROCEEDINGS.

Ferrari, A., Zenezini, G., Carlin, A., & Rafele, C. (2021). An integrated simulation modelling approach for a warehouse 4.0.

Ferreira, A. R. (2022). Estimation of knots location and number in the splines regression models using an optimization approach (Doctoral dissertation, Universidade de São Paulo).

Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American statistical Association, 97(458), 611-631.

Gajjar, J. M., & Thakkar, H. R. (2014). Improvement in material feeding system through introducing kitting concept in lean environment of MSME: a review study. INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH, 2(1), 891-900.

García-Ordás, M. T., Alaiz-Moretón, H., Casteleiro-Roca, J. L., Jove, E., Benítez-Andrades, J. A., García-Rodríguez, I., ... & Calvo-Rolle, J. L. (2023). Clustering techniques selection for a hybrid regression model: a case study based on a solar thermal system. Cybernetics and Systems, 54(3), 286-305.

Georgakis, A., Gatziolis, D., & Stamatellos, G. (2023). A primer on clustering of forest management units for reliable design-based direct estimates and model-based small area estimation. Forests, 14(10), 1994.

Geraeds, E. P., & Llamoca, E. L. C. (2023). Manpower prediction for kitting in high complex, low volume assembly lines.

Gergely, B., & Vargha, A. (2021). How to use model-based cluster analysis efficiently in person-oriented research. Journal for Person-Oriented Research, 7(1), 22.

Greenwell, B. M., Boehmke, B. C., & Gray, B. (2020). Variable Importance Plots-An Introduction to the vip Package. R J., 12(1), 343.

Gren, I. M., Brutemark, A., Jägerbrand, A. K., & Svedén, J. B. (2020). Costs of air pollutants from shipping: a meta-regression analysis. Transport reviews, 40(4), 411-428.

Guo, Y., Wang, W., & Wang, X. (2021). A robust linear regression feature selection method for data sets with unknown noise. IEEE Transactions on Knowledge and Data Engineering, 35(1), 31-44.

Gupta, M. K., & Chandra, P. (2020). An empirical evaluation of K-means clustering algorithm using different distance/similarity metrics. In Proceedings of ICETIT 2019: Emerging Trends in Information Technology (pp. 884-892). Springer International Publishing.

Hu, P., Zhang, J., & Li, N. (2021, October). Research on Flight Delay Prediction Based on Random Forest. In 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT) (pp. 506-509). IEEE.

Huang, Z., & Chen, D. (2021). A breast cancer diagnosis method based on VIM feature selection and hierarchical clustering random forest algorithm. IEEE Access, 10, 3284-3293.

Irfiyanda, C., Andreswari, R., & Hamami, F. (2022, November). Customer Segmentation Using Fuzzy C-Means Algorithm in Telco Industry. In 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA) (pp. 1-4). IEEE.

Jiang, Z. Z., Wan, M., Pei, Z., & Qin, X. (2021). Spatial and temporal optimization for smart warehouses with fast turnover. Computers & Operations Research, 125, 105091.

Ju, Y., & Hargreaves, C. A. (2021). The impact of shipping CO2 emissions from marine traffic in Western Singapore Straits during COVID-19. Science of The Total Environment, 789, 148063.

Jum’a, L., & Basheer, M. E. (2023). Analysis of warehouse value-added services using pareto as a quality tool: A case study of third-party logistics service provider. Administrative Sciences, 13(2), 51.

Kalkha, H., Khiat, A., Bahnasse, A., & Ouajji, H. (2022). Toward a reliable and responsive E-commerce with IoT. Procedia Computer Science, 198, 614-619.

Kanberoğlu, B., & Kökkülünk, G. (2021). Assessment of CO2 emissions for a bulk carrier fleet. Journal of Cleaner Production, 283, 124590.

Karaduman, H. A., Karaman-Akgül, A., Çağlar, M., & Akbaş, H. E. (2020). The relationship between logistics performance and carbon emissions: an empirical investigation on Balkan countries. International Journal of Climate Change Strategies and Management, 12(4), 449-461.

Karim, N. H., Abdul Rahman, N. S. F., Md Hanafiah, R., Abdul Hamid, S., Ismail, A., Abd Kader, A. S., & Muda, M. S. (2021). Revising the warehouse productivity measurement indicators: ratio-based benchmark. Maritime Business Review, 6(1), 49-71.

Kawa, A. (2021). Fulfilment as logistics support for E-tailers: An empirical studies. Sustainability, 13(11), 5988.

Khan, A. A., Shameem, M., Nadeem, M., & Akbar, M. A. (2021). Agile trends in Chinese global software development industry: Fuzzy AHP based conceptual mapping. Applied Soft Computing, 102, 107090.

Khan, G. A., Hu, J., Li, T., Diallo, B., & Zhao, Y. (2022). Multi-view low rank sparse representation method for three-way clustering. International Journal of Machine Learning and Cybernetics, 13, 233-253.

Kilic, H. S., & Durmusoglu, M. B. (2012). Design of kitting system in lean‐based assembly lines. Assembly automation, 32(3), 226-234.

Kinshakov, E., Parfenenko, Y., & Shendryk, V. (2021). Comparative analysis of methods for prediction continuous numerical features on big datasets. Technology audit and production reserves, 6(2), 62.

Klundt, E., Towers, N., & Bechkoum, K. (2024). Lean and Agile Supply Strategies in Distribution Centres to Deliver Value-Added Services (VAS). Logistics, 8(3), 67.

Koumetio Tekouabou, S. C., Diop, E. B., Azmi, R., & Chenal, J. (2023). Artificial intelligence based methods for smart and sustainable urban planning: a systematic survey. Archives of Computational Methods in Engineering, 30(2), 1421-1438.

Kujanpää, V. (2024). Logistics in lean construction: Exploration of processes, challenges and best practices.

Kule, C., Patil, S. B., & Vaity, S. (2020). Improvement in Material Feeding by Introducing Kitting in the Assembly Line. In Proceedings of International Conference on Intelligent Manufacturing and Automation: ICIMA 2020 (pp. 407-417). Springer Singapore.

Li, J., Anser, M. K., Tabash, M. I., Nassani, A. A., Haffar, M., & Zaman, K. (2023). Technology-and logistics-induced carbon emissions obstructing the Green supply chain management agenda: Evidence from 101 countries. International Journal of Logistics Research and Applications, 26(7), 788-812.

Li, T., Sun, S., Sattar, T. P., & Corchado, J. M. (2014). Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches. Expert Systems with applications, 41(8), 3944-3954.

Liu, H., Deng, A., Wang, X., & Yue, G. (2022, December). Density Peaks Clustering Algorithm Based on Density Stratification and Subcluster Fusion. In Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence (pp. 1-7).

Liu, J., & Duru, O. (2020). Bayesian probabilistic forecasting for ship emissions. Atmospheric environment, 231, 117540.

Liu, J., Anavatti, S., Garratt, M., & Abbass, H. A. (2022). Modified continuous ant colony optimisation for multiple unmanned ground vehicle path planning. Expert Systems with Applications, 196, 116605.

Liu, S., Maljovec, D., Wang, B., Bremer, P. T., & Pascucci, V. (2016). Visualizing high-dimensional data: Advances in the past decade. IEEE transactions on visualization and computer graphics, 23(3), 1249-1268.

Liu, Z., Wen, T., Sun, W., & Zhang, Q. (2021). Feature-weighting and clustering random forest. International Journal of Computational Intelligence Systems, 14(1), 257-265.

Lorson, F., Fügener, A., & Hübner, A. (2023). New team mates in the warehouse: Human interactions with automated and robotized systems. Iise Transactions, 55(5), 536-553.

Maatar, O., Trost, R., De Bruyne, I., Van Dromme, H., & Berroir, F. (2022, September). Smart logistics for urban construction sites (CCC). In IOP Conference Series: Earth and Environmental Science (Vol. 1078, No. 1, p. 012046). IOP Publishing.

Mabeya, F. (2022). Improved Warehouse for SMT Material Management using Modern Technology Retrieval System and Better Traceability (Master's thesis, Minnesota State University, Mankato).

Maddumala, V. R. (2020). A Weight Based Feature Extraction Model on Multifaceted Multimedia Bigdata Using Convolutional Neural Network. Ingénierie des Systèmes d'Information, 25(6).

Maderna, R., Poggiali, M., Zanchettin, A. M., & Rocco, P. (2020, May). An online scheduling algorithm for human-robot collaborative kitting. In 2020 IEEE international conference on robotics and automation (ICRA) (pp. 11430-11435). IEEE.

Mandal, A., Perrot, M., & Ghoshdastidar, D. (2022). A Revenue Function for Comparison-Based Hierarchical Clustering. arXiv preprint arXiv:2211.16459.

Marquart, I., & Koca Marquart, E. (2021). RFCC: Random Forest Consensus Clustering for Regression and Classification. Available at SSRN 3807828.

Montoya Zapata, S., Klement, N., Silva, C., Gibaru, O., & Lafou, M. (2023, September). Simulation of a Kitting System for the Replenishment of an Automotive Assembly Line. In International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing (pp. 177-188). Cham: Springer Nature Switzerland.

Montoya-Zapata, S., Klement, N., Silva, C., Gibaru, O., & Lafou, M. (2024). Multi-agent system for perturbations in the kitting process of an automotive assembly line. Engineering Applications of Artificial Intelligence, 135, 108679.

Mudyazhezha, E. E. (2024). Reduction of Post-Harvest Losses in the Maize Value Chain: A Review of Warehousing Literature. The Zimbabwe Journal of Business, Economics and Management, 3(1).

Muñoz-Villamizar, A., Velázquez-Martínez, J. C., Haro, P., Ferrer, A., & Mariño, R. (2021). The environmental impact of fast shipping ecommerce in inbound logistics operations: A case study in Mexico. Journal of Cleaner Production, 283, 125400.

Mushagalusa, C. A., Fandohan, A. B., & Glèlè Kakaï, R. (2022). Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance. Journal of Probability and Statistics, 2022(1), 2833537.

Neeraj, K. N., & Maurya, V. (2020). A review on machine learning (feature selection, classification and clustering) approaches of big data mining in different area of research. Journal of critical reviews, 7(19), 2610-2626.

Ngatchou-Wandji, J., & Bulla, J. (2011). On choosing a mixture model for clustering.

Nguyen, S. D., Nguyen, V. S. T., & Pham, N. T. (2021). Determination of the optimal number of clusters: A fuzzy-set based method. IEEE Transactions on Fuzzy Systems, 30(9), 3514-3526.

Nilsson, E., & Jayaraman, V. (2021). Selection of Warehouse Automation System (s) for component storage-in a multiple assembly line manufacturing context.

Pantula, P. D., Miriyala, S. S., & Mitra, K. (2020). An evolutionary neuro-fuzzy C-means clustering technique. Engineering Applications of Artificial Intelligence, 89, 103435.

Qian, L., Plant, C., & Böhm, C. (2021, December). Density-based clustering for adaptive density variation. In 2021 IEEE International Conference on Data Mining (ICDM) (pp. 1282-1287). IEEE.

Raghuram, P., & Arjunan, M. K. (2022). Design framework for a lean warehouse–a case study-based approach. International Journal of Productivity and Performance Management, 71(6), 2410-2431.

Raja, R., & Venkatachalam, S. (2022). Adoption of digital technology in global third-party logistics services providers: A review of literature. FOCUS: Journal of International Business, 9(1), 105-129.

Ramnath, B. V., Kumar, C. S., Mohamed, G. R., Venkataraman, K., Elanchezhian, C., & Sathish, S. (2014). Analysis of occupational safety and health of workers by implementing ergonomic based kitting assembly system. Procedia Engineering, 97, 1788-1797.

Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023). Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artificial Intelligence Review, 56(8), 8219-8264.

Rapp, T., Peters, C., & Dachsbacher, C. (2020). Visual analysis of large multivariate scattered data using clustering and probabilistic summaries. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1580-1590.

Ren, Y., Pu, J., Yang, Z., Xu, J., Li, G., Pu, X., ... & He, L. (2024). Deep clustering: A comprehensive survey. IEEE Transactions on Neural Networks and Learning Systems.

Rocamora, F. R., Camaclang, C., & Cervantes, J. (2023). A Feasibility of Outsourcing Warehouse and Kitting and Services in Automotive Companies at District 2, Laguna. Ani: Letran Calamba Research Report, 19(1), 1-1.

Rothacher, Y., & Strobl, C. (2024). Identifying informative predictor variables with random forests. Journal of Educational and Behavioral Statistics, 49(4), 595-629.

Sahoo, R., Bhowmick, B., & Tiwari, M. K. (2023). Developing a model to optimise the cost of consolidated air freight considering the varying scenarios. International Journal of Logistics Research and Applications, 26(8), 1035-1059.

Sarimi, A. F., Nasir, N., Rashid, H., & Azis, N. A. (2023). Development and simulation via flexsim for kitting trolley design of rear car seat assembly process. Journal of Applied Engineering Design & Simulation (JAEDS), 3(2), 27-37.

Schwartz, H., Gustafsson, M., & Spohr, J. (2020). Emission abatement in shipping–is it possible to reduce carbon dioxide emissions profitably?. Journal of Cleaner Production, 254, 120069.

Scornet, E. (2023, February). Trees, forests, and impurity-based variable importance in regression. In Annales de l'Institut Henri Poincare (B) Probabilites et statistiques (Vol. 59, No. 1, pp. 21-52). Institut Henri Poincaré.

Shi, W., Tong, C., Zhang, A., Wang, B., Shi, Z., Yao, Y., & Jia, P. (2021). An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China. Communications biology, 4(1), 126.

Simões, M. J., Pinto, T., & Silva, C. (2023, November). Optimization of the Energy Consumption for Robotic Kitting in the Automotive Industry. In Iberian Robotics conference (pp. 483-494). Cham: Springer Nature Switzerland.

Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524.

Starke, M., & Geiger, C. (2022). Field setup and assessment of a cloud-data based crane scale (ccs) considering weight-and local green wood density-related volume references. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 43(1), 29-45.

Starovoitov, V. V., & Golub, Y. I. (2021, September). Data normalization in machine learning. In Informatics (Vol. 18, No. 3, pp. 83-96).

Tan, C. W., Robin, K. R., Chuang, K. H., & Humpal, A. (2021). SourceAmerica Design Challenge Accessible Kitting and Packaging Station.

Thai, V. T., & Norlander, H. (2021). Analysing the consequences of a future implementation of a new logistics concept-A case study at Volvo Trucks.

Thamrin, N., & Wijayanto, A. W. (2021). Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island: Analisis cluster dengan menggunakan hard clustering dan soft clustering untuk pengelompokkan tingkat kesejahteraan kabupaten/kota di pulau Jawa. Indonesian Journal of Statistics and Its Applications, 5(1), 141-160.

Tian, C., & Hao, Y. (2020). Point and interval forecasting for carbon price based on an improved analysis-forecast system. Applied Mathematical Modelling, 79, 126-144.

Tornese, F., Unnu, K., Gnoni, M. G., & Pazour, J. A. (2020). On-demand warehousing: main features and business models. XXV Summer School.

Vaka, D. K. (2020). Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM).

Wang, H. (2006). Nearest neighbors by neighborhood counting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6), 942-953.

Wang, Z., Ye, Z., Du, Y., Mao, Y., Liu, Y., Wu, Z., & Wang, J. (2022, October). AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE.

Wei, F. F., Chen, W. N., Mao, W., Hu, X. M., & Zhang, J. (2023). An Efficient Two-Stage Surrogate-Assisted Differential Evolution for Expensive Inequality Constrained Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Williams, D. R., & Rodriguez, J. E. (2022). Why overfitting is not (usually) a problem in partial correlation networks. Psychological Methods, 27(5), 822.

Winkelhaus, S., & Grosse, E. H. (2022). Smart warehouses—a sociotechnical perspective. In The Digital Supply Chain (pp. 47-60). Elsevier.

Wu, H., & Li, Y. F. (2022). Clustering spatially correlated functional data with multiple scalar covariates. IEEE Transactions on Neural Networks and Learning Systems, 34(10), 7074-7088.

Wu, J., Wang, Y. G., Tian, Y. C., Burrage, K., & Cao, T. (2021). Support vector regression with asymmetric loss for optimal electric load forecasting. Energy, 223, 119969.

Xie, M., Xie, L., & Zhu, P. (2021). An Efficient Feature Weighting Method for Support Vector Regression. Mathematical Problems in Engineering, 2021(1), 6675218.

Xiong, J., Liu, X., Zhu, X., Zhu, H., Li, H., & Zhang, Q. (2020). Semi-supervised fuzzy c-means clustering optimized by simulated annealing and genetic algorithm for fault diagnosis of bearings. Ieee Access, 8, 181976-181987.

Xu, J., Teng, J., & Yao, A. C. C. (2022). Relaxing the feature covariance assumption: Time-variant bounds for benign overfitting in linear regression. arXiv preprint arXiv:2202.06054.

Xu, Q., Zhang, Q., Liu, J., & Luo, B. (2020). Efficient synthetical clustering validity indexes for hierarchical clustering. Expert Systems with Applications, 151, 113367.

Xu, T., Zhu, R., & Shao, X. (2022). On variance estimation of random forests. stat, 1050, 26.

Yang, J., Han, S., & Chen, Y. (2023). Prediction of traffic accident severity based on random forest. Journal of Advanced Transportation, 2023(1), 7641472.

Yang, L., & Wu, T. T. (2023). Model-based clustering of high-dimensional longitudinal data via regularization. Biometrics, 79(2), 761-774.

Yang, W., Wang, X., Lu, J., Dou, W., & Liu, S. (2020). Interactive steering of hierarchical clustering. IEEE Transactions on Visualization and Computer Graphics, 27(10), 3953-3967.

Yang, Y. C., Lin, T. I., Castro, L. M., & Wang, W. L. (2020). Extending finite mixtures of t linear mixed-effects models with concomitant covariates. Computational Statistics & Data Analysis, 148, 106961.

Yi, J., Yan, H., Wang, H., Yuan, J., & Li, Y. (2023, October). DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4916-4922).

Yi, Y., Sun, D., Li, P., Kim, T. K., Xu, T., & Pei, Y. (2022). Unsupervised random forest for affinity estimation. Computational visual media, 8, 257-272.

Yuan, C. (2023, July). Emergency Transportation and Structural Optimization Issues of E-commerce Logistics Network Parcels. In 2023 International Conference on Data Science and Network Security (ICDSNS) (pp. 1-7). IEEE.

Yusup, M. F. B. (2022). Impact Analysis Of Implementation Of Bonded Warehouse Policy In Makassar Port New Port On Logistic Costs. Maritime Park Journal of Maritime Technology and Society, 17-25.

Zapata, S. M., Klement, N., Silva, C., Gibaru, O., & Lafou, M. (2022, June). Collective intelligence application in a kitting picking zone of the automotive industry. In International Joint Conference on Mechanics, Design Engineering & Advanced Manufacturing (pp. 410-420). Cham: Springer International Publishing.

Zhang, C., Zhang, W., Luo, W., Gao, X., & Zhang, B. (2021). Analysis of influencing factors of carbon emissions in China’s logistics industry: a GDIM-based indicator decomposition. Energies, 14(18), 5742.

Zhang, J., Wang, S., He, W., Li, J., Cao, Z., Wei, B., & Wang, M. (2022). Material kitting in selective assembly: A manual order picking system based on augmented reality. The International Journal of Advanced Manufacturing Technology, 123(1), 675-686.

Zhang, Y., Schnell, P., Song, C., Huang, B., & Lu, B. (2021). Subgroup causal effect identification and estimation via matching tree. Computational Statistics & Data Analysis, 159, 107188.

Zhang, Y., Wang, G., Chung, F. L., & Wang, S. (2021). Support vector machines with the known feature-evolution priors. Knowledge-Based Systems, 223, 107048.

Zhao, J., Zheng, Y., Seppänen, O., Tetik, M., & Peltokorpi, A. (2021). Using real-time tracking of materials and labor for kit-based logistics management in construction. Frontiers in Built Environment, 7, 713976.

Zhao, L., Zhao, F., & Che, W. W. (2023). Distributed adaptive fuzzy fault-tolerant control for multi-agent systems with node faults and denial-of-service attacks. Information Sciences, 631, 385-395.

Zhou, B., & He, Z. (2020). A material handling scheduling method for mixed-model automotive assembly lines based on an improved static kitting strategy. Computers & Industrial Engineering, 140, 106268.

Zhou, L., Simon, J. B., Vardi, G., & Srebro, N. (2023). An agnostic view on the cost of overfitting in (kernel) ridge regression. arXiv preprint arXiv:2306.13185.

Zhou, S., Liu, F., & Song, W. (2021). Estimating the optimal number of clusters via internal validity index. Neural Processing Letters, 53, 1013-1034.

Zhou, Y., Ma, N., Wang, Q., Wang, Z., Chen, C., Tao, J., ... & Cheng, Y. (2022). Bimodal distribution of size-resolved particle effective density: Results from a short campaign in a rural environment over the North China Plain. Atmospheric Chemistry and Physics, 22(3), 2029-2047.

Zhuang, Y., Chen, X., & Yang, Y. (2023, July). Likelihood adjusted semidefinite programs for clustering heterogeneous data. In International Conference on Machine Learning (pp. 43326-43346). PMLR.