How to cite this paper
Kmiecik, M. (2025). Supply and demand prediction by 3PL for assortment planning.Management Science Letters , 15(2), 97-112.
Refrences
Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga‐Zielinski, B., & Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resources Research, 48(1).
Alegado, R. T., & Tumibay, G. M. (2020). Statistical and machine learning methods for vaccine demand forecasting: A comparative analysis. Journal of Computer and Communications, 8(10), 37.
Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation (pp. 106-112). IEEE.
Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 29, 105340.
Bousqaoui, H., Slimani, I., & Achchab, S. (2021). Comparative analysis of short-term demand predicting models using ARIMA and deep learning. International Journal of Electrical and Computer Engineering, 11(4), 3319.
Brandt, P. T., & Williams, J. T. (2001). A linear Poisson autoregressive model: The Poisson AR (p) model. Political Anal-ysis, 9(2), 164-184.
Burnham, K. P., & Anderson, D. R. (2021). Multimodel inference: understanding AIC and BIC in model selection. 2004. Sociological Methods and Research, 261-304.
Caniato, F., Kalchschmidt, M., & Ronchi, S. (2011). Integrating quantitative and qualitative forecasting approaches: or-ganizational learning in an action research case. Journal of the Operational Research Society, 62, 413-424.
Carbone, A. (2009, September). Detrending moving average algorithm: a brief review. In 2009 IEEE Toronto Internation-al Conference Science and Technology for Humanity (TIC-STH) (pp. 691-696). IEEE.
Chalise, S. (2021). Estimation of Global Solar Radiation Potential using Hybrid Models: A Case Study of Nepal (Doctoral dissertation, Pulchowk Campus).
Christoffersen, P. F., & Diebold, F. X. (2000). How relevant is volatility forecasting for financial risk manage-ment?. Review of Economics and Statistics, 82(1), 12-22.
Choi, H., Suh, S. I., Kim, S. H., Han, E. J., & Ki, S. J. (2021). Assessing the performance of deep learning algorithms for short-term surface water quality prediction. Sustainability, 13(19), 10690.
Cox, A., Chicksand, D., & Ireland, P. (2005). Overcoming demand management problems: the scope for improving reac-tive and proactive supply management in the UK health service. Journal of public procurement, 5(1), 1-22.
Croxton, K. L., Lambert, D. M., García-Dastugue, S. J., & Rogers, D. S. (2002). The demand management process. The In-ternational Journal of logistics management, 13(2), 51-66.
Currie, C. S., Dokka, T., Harvey, J., & Strauss, A. K. (2018). Future research directions in demand management. Journal of Revenue and Pricing Management, 17, 459-462.
Darko, E. O., & Vlachos, I. (2022). Creating valuable relationships with third-party logistics (3PL) providers: a multiple-case study. Logistics, 6(2), 38
da Veiga, C. P., da Veiga, C. R. P., Puchalski, W., dos Santos Coelho, L., & Tortato, U. (2016). Demand forecasting based on natural computing approaches applied to the foodstuff retail segment. Journal of Retailing and Consumer Services, 31, 174-181.
Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering ap-plications of artificial intelligence, 23(4), 586-594.
Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. In-ternational Journal of Engineering Business Management, 10, 1847979018808673.
Fuqua, D., & Hespeler, S. (2022). Commodity demand forecasting using modulated rank reduction for humanitarian logis-tics planning. Expert Systems with Applications, 206, 117753.
Gligor, D. (2014). The role of demand management in achieving supply chain agility. Supply Chain Management: An In-ternational Journal, 19(5/6), 577-591.
Guo, Z., Liu, H., Zhang, L., Zhang, Q., Zhu, H., & Xiong, H. (2022, August). Talent demand-supply joint prediction with dynamic heterogeneous graph enhanced meta-learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2957-2967).
Gupta, A., & Kumar, A. (2020, June). Mid term daily load forecasting using ARIMA, wavelet-ARIMA and machine learn-ing. In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-5). IEEE
Gürler, Ü., Alp, O., & Büyükkaramikli, N. Ç. (2014). Coordinated inventory replenishment and outsourced transportation operations. Transportation Research Part E: Logistics and Transportation Review, 70, 400-415.
Harvey, A. C. (1984). A unified view of statistical forecasting procedures. Journal of forecasting, 3(3), 245-275.
Hassanzadeh, Z., Mahdavi, I., Tajdin, A., & Fazlollahtabar, H. (2022). Collaboration analysis for a three-tier sustainable logistics network considering 3PL using BCVR technique. Environment, Development and Sustainability, 1-20.
Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10.
Huemer, L. (2012). Unchained from the chain: Supply management from a logistics service provider perspective. Journal of Business Research, 65(2), 258-264.
Hunter, J. S. (1986). The exponentially weighted moving average. Journal of quality technology, 18(4), 203-210.
Huo, B., Ye, Y., & Zhao, X. (2015). The impacts of trust and contracts on opportunism in the 3PL industry: The moderat-ing role of demand uncertainty. International Journal of Production Economics, 170, 160-170.
Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2016). Global suppy chain and operations management. A decision-oriented introduction to the creation of value, 2.
Jiang, L., Guo, Y., Su, J., Jian, J., & He, Y. (2019). Sub-coordination in a competing supply chain with a 3PL provid-er. IEEE Access, 7, 158148-158159.
Jin, M., Wang, H., Zhang, Q., & Zeng, Y. (2020). Supply chain optimization based on chain management and mass cus-tomization. Information Systems and e-Business Management, 18, 647-664.
Júnior, D. S. D. O. S., de Oliveira, J. F., & de Mattos Neto, P. S. (2019). An intelligent hybridization of ARIMA with ma-chine learning models for time series forecasting. Knowledge-Based Systems, 175, 72-86.
Kahkonen, A. K. (2014). Conducting a case study in supply management. Operations and Supply Chain Management: An International Journal, 4(1), 31-41.
Karia, N., Wong, C. Y., Asaari, M. H. A. H., & Lai, K. H. (2015). The effects of resource bundling on third-party logistics providers' performance. International journal of engineering business management, 7, 9.
Kim, M., Jeong, J., & Bae, S. (2019, April). Demand forecasting based on machine learning for mass customization in smart manufacturing. In Proceedings of the 2019 International Conference on Data Mining and Machine Learning (pp. 6-11).
Klinker, F. (2011). Exponential moving average versus moving exponential average. Mathematische Semesterberichte, 58, 97-107.
Kmiecik, M. (2021). Implementation of forecasting tool in the logistics company – case study, Scientific Papers of Silesi-an University of Technology, (152)
Kmiecik, M. (2022). Automation of warehouse resource planning process by using a cloud demand forecasting tool. Sci-entific Papers of Silesian University of Technology. Organization & Management, 166.
Kmiecik, M. (2022a). Conception of logistics coordination in the distribution networks. Logistics Research, 15(1).
Kmiecik, M. (2022b). Logistics coordination based on inventory management and transportation planning by third-party logistics (3PL). Sustainability, 14(13), 8134.
Kmiecik, M. (2023). Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribu-tion network. Production Engineering Archives, 29(1), 69-82.
Kmiecik, M., & Wolny, M. (2022). Forecasting needs of the operational activity of a logistics operator. LogForum, 18(2).
Kourentzes, N., Rostami-Tabar, B., & Barrow, D. K. (2017). Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?. Journal of Business Research, 78, 1-9.
Kramarz, M., & Kmiecik, M. (2022). Quality of Forecasts as the Factor Determining the Coordination of Logistics Pro-cesses by Logistic Operator. Sustainability, 14(2), 1013.
Krasnov, S., Zotova, E., Sergeev, S., Krasnov, A., & Draganov, M. (2019, October). Stochastic algorithms in multimodal 3PL segment for the digital environment. In IOP Conference Series: Materials Science and Engineering (Vol. 618, No. 1, p. 012069). IOP Publishing.
Liu, X., Qian, C., & Wang, S. (2020). When do 3PLs initiate low-carbon supply chain integration?. International Journal of Operations & Production Management, 40(9), 1367-1395.
Liu, Y., & Wang, S. (2011). Research on collaborative management in supply chain crisis. Procedia Environmental Sci-ences, 10, 141-146.
Mahmood, S., & Kess, P. (2016). An overview of demand management through demand supply chain in fashion indus-try. International Journal of Management Science and Business Administration, 2(12), 7-19.
Mentzer, J. T., & Moon, M. A. (2004). Sales forecasting management: a demand management approach. Sage Publica-tions.
Merminod, N., Large, R. O., & Paché, G. (2019, July). Procurement of advanced logistics services: proposition of a rea-soned action model of individual buying behavior. In Supply Chain Forum: An International Journal (Vol. 20, No. 3, pp. 169-184). Taylor & Francis.
Mir, A., Lazaar, S., & Balambo, M. A. (2021). The logistics service provider as an integrator of supply chain. Evidences from an emerging market. Revue Européenne d’Économie et Management des Services, 2021(12), 69-91.
Mohanty, M., & Shankar, R. (2020). DEA-ADALINE: an approach to improve the relative efficiency of 3PLs provid-ers. Benchmarking: An International Journal, 27(1), 166-191.
Morlidge, S., & Player, S. (2010). Future ready: How to master business forecasting. John Wiley & Sons.
Mortensen, O., & Lemoine, O. W. (2008). Integration between manufacturers and third party logistics provid-ers?. International Journal of Operations & Production Management, 28(4), 331-359.
Musarat, M. A., Alaloul, W. S., Rabbani, M. B. A., Ali, M., Altaf, M., Fediuk, R., ... & Farooq, W. (2021). Kabul river flow prediction using automated ARIMA forecasting: A machine learning approach. Sustainability, 13(19), 10720
Nettsträter, A., Geißen, T., Witthaut, M., Ebel, D., & Schoneboom, J. (2015). Logistics software systems and functions: an overview of ERP, WMS, TMS and SCM systems. Cloud computing for logistics, 1-11.
Nguyen, X. H. (2020). Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river. Advances in Water Resources, 142, 103656.
Noh, J., Park, H. J., Kim, J. S., & Hwang, S. J. (2020). Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management. Mathematics, 8(4), 565.
Patil, H., & Divekar, B. R. (2014). Inventory management challenges for B2C e-commerce retailers. Procedia Economics and Finance, 11, 561-571.
Peels, R., Udenio, M., Fransoo, J. C., Wolfs, M., & Hendrikx, T. (2009). Responding to the Lehman Wave: Sales forecast-ing and supply management during the credit crisis.
Pinna, R., Carrus, P. P., & Pettinao, D. (2010). Supply Chain Coordination and IT: the role of third party logistics provid-ers. In Management of the Interconnected World: ItAIS: The Italian Association for Information Systems (pp. 299-306). Physica-Verlag HD.
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Pujiastuti, L., Wahyudi, M., da Conceição, B. J., & Riandari, F. (2023). Robust mathematical model for supply chain op-timization: A comprehensive study. Journal of Intelligent Decision Support System (IDSS), 6(2), 57-65.
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Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 29, 105340.
Bousqaoui, H., Slimani, I., & Achchab, S. (2021). Comparative analysis of short-term demand predicting models using ARIMA and deep learning. International Journal of Electrical and Computer Engineering, 11(4), 3319.
Brandt, P. T., & Williams, J. T. (2001). A linear Poisson autoregressive model: The Poisson AR (p) model. Political Anal-ysis, 9(2), 164-184.
Burnham, K. P., & Anderson, D. R. (2021). Multimodel inference: understanding AIC and BIC in model selection. 2004. Sociological Methods and Research, 261-304.
Caniato, F., Kalchschmidt, M., & Ronchi, S. (2011). Integrating quantitative and qualitative forecasting approaches: or-ganizational learning in an action research case. Journal of the Operational Research Society, 62, 413-424.
Carbone, A. (2009, September). Detrending moving average algorithm: a brief review. In 2009 IEEE Toronto Internation-al Conference Science and Technology for Humanity (TIC-STH) (pp. 691-696). IEEE.
Chalise, S. (2021). Estimation of Global Solar Radiation Potential using Hybrid Models: A Case Study of Nepal (Doctoral dissertation, Pulchowk Campus).
Christoffersen, P. F., & Diebold, F. X. (2000). How relevant is volatility forecasting for financial risk manage-ment?. Review of Economics and Statistics, 82(1), 12-22.
Choi, H., Suh, S. I., Kim, S. H., Han, E. J., & Ki, S. J. (2021). Assessing the performance of deep learning algorithms for short-term surface water quality prediction. Sustainability, 13(19), 10690.
Cox, A., Chicksand, D., & Ireland, P. (2005). Overcoming demand management problems: the scope for improving reac-tive and proactive supply management in the UK health service. Journal of public procurement, 5(1), 1-22.
Croxton, K. L., Lambert, D. M., García-Dastugue, S. J., & Rogers, D. S. (2002). The demand management process. The In-ternational Journal of logistics management, 13(2), 51-66.
Currie, C. S., Dokka, T., Harvey, J., & Strauss, A. K. (2018). Future research directions in demand management. Journal of Revenue and Pricing Management, 17, 459-462.
Darko, E. O., & Vlachos, I. (2022). Creating valuable relationships with third-party logistics (3PL) providers: a multiple-case study. Logistics, 6(2), 38
da Veiga, C. P., da Veiga, C. R. P., Puchalski, W., dos Santos Coelho, L., & Tortato, U. (2016). Demand forecasting based on natural computing approaches applied to the foodstuff retail segment. Journal of Retailing and Consumer Services, 31, 174-181.
Faruk, D. Ö. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering ap-plications of artificial intelligence, 23(4), 586-594.
Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. In-ternational Journal of Engineering Business Management, 10, 1847979018808673.
Fuqua, D., & Hespeler, S. (2022). Commodity demand forecasting using modulated rank reduction for humanitarian logis-tics planning. Expert Systems with Applications, 206, 117753.
Gligor, D. (2014). The role of demand management in achieving supply chain agility. Supply Chain Management: An In-ternational Journal, 19(5/6), 577-591.
Guo, Z., Liu, H., Zhang, L., Zhang, Q., Zhu, H., & Xiong, H. (2022, August). Talent demand-supply joint prediction with dynamic heterogeneous graph enhanced meta-learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2957-2967).
Gupta, A., & Kumar, A. (2020, June). Mid term daily load forecasting using ARIMA, wavelet-ARIMA and machine learn-ing. In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-5). IEEE
Gürler, Ü., Alp, O., & Büyükkaramikli, N. Ç. (2014). Coordinated inventory replenishment and outsourced transportation operations. Transportation Research Part E: Logistics and Transportation Review, 70, 400-415.
Harvey, A. C. (1984). A unified view of statistical forecasting procedures. Journal of forecasting, 3(3), 245-275.
Hassanzadeh, Z., Mahdavi, I., Tajdin, A., & Fazlollahtabar, H. (2022). Collaboration analysis for a three-tier sustainable logistics network considering 3PL using BCVR technique. Environment, Development and Sustainability, 1-20.
Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10.
Huemer, L. (2012). Unchained from the chain: Supply management from a logistics service provider perspective. Journal of Business Research, 65(2), 258-264.
Hunter, J. S. (1986). The exponentially weighted moving average. Journal of quality technology, 18(4), 203-210.
Huo, B., Ye, Y., & Zhao, X. (2015). The impacts of trust and contracts on opportunism in the 3PL industry: The moderat-ing role of demand uncertainty. International Journal of Production Economics, 170, 160-170.
Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2016). Global suppy chain and operations management. A decision-oriented introduction to the creation of value, 2.
Jiang, L., Guo, Y., Su, J., Jian, J., & He, Y. (2019). Sub-coordination in a competing supply chain with a 3PL provid-er. IEEE Access, 7, 158148-158159.
Jin, M., Wang, H., Zhang, Q., & Zeng, Y. (2020). Supply chain optimization based on chain management and mass cus-tomization. Information Systems and e-Business Management, 18, 647-664.
Júnior, D. S. D. O. S., de Oliveira, J. F., & de Mattos Neto, P. S. (2019). An intelligent hybridization of ARIMA with ma-chine learning models for time series forecasting. Knowledge-Based Systems, 175, 72-86.
Kahkonen, A. K. (2014). Conducting a case study in supply management. Operations and Supply Chain Management: An International Journal, 4(1), 31-41.
Karia, N., Wong, C. Y., Asaari, M. H. A. H., & Lai, K. H. (2015). The effects of resource bundling on third-party logistics providers' performance. International journal of engineering business management, 7, 9.
Kim, M., Jeong, J., & Bae, S. (2019, April). Demand forecasting based on machine learning for mass customization in smart manufacturing. In Proceedings of the 2019 International Conference on Data Mining and Machine Learning (pp. 6-11).
Klinker, F. (2011). Exponential moving average versus moving exponential average. Mathematische Semesterberichte, 58, 97-107.
Kmiecik, M. (2021). Implementation of forecasting tool in the logistics company – case study, Scientific Papers of Silesi-an University of Technology, (152)
Kmiecik, M. (2022). Automation of warehouse resource planning process by using a cloud demand forecasting tool. Sci-entific Papers of Silesian University of Technology. Organization & Management, 166.
Kmiecik, M. (2022a). Conception of logistics coordination in the distribution networks. Logistics Research, 15(1).
Kmiecik, M. (2022b). Logistics coordination based on inventory management and transportation planning by third-party logistics (3PL). Sustainability, 14(13), 8134.
Kmiecik, M. (2023). Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribu-tion network. Production Engineering Archives, 29(1), 69-82.
Kmiecik, M., & Wolny, M. (2022). Forecasting needs of the operational activity of a logistics operator. LogForum, 18(2).
Kourentzes, N., Rostami-Tabar, B., & Barrow, D. K. (2017). Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?. Journal of Business Research, 78, 1-9.
Kramarz, M., & Kmiecik, M. (2022). Quality of Forecasts as the Factor Determining the Coordination of Logistics Pro-cesses by Logistic Operator. Sustainability, 14(2), 1013.
Krasnov, S., Zotova, E., Sergeev, S., Krasnov, A., & Draganov, M. (2019, October). Stochastic algorithms in multimodal 3PL segment for the digital environment. In IOP Conference Series: Materials Science and Engineering (Vol. 618, No. 1, p. 012069). IOP Publishing.
Liu, X., Qian, C., & Wang, S. (2020). When do 3PLs initiate low-carbon supply chain integration?. International Journal of Operations & Production Management, 40(9), 1367-1395.
Liu, Y., & Wang, S. (2011). Research on collaborative management in supply chain crisis. Procedia Environmental Sci-ences, 10, 141-146.
Mahmood, S., & Kess, P. (2016). An overview of demand management through demand supply chain in fashion indus-try. International Journal of Management Science and Business Administration, 2(12), 7-19.
Mentzer, J. T., & Moon, M. A. (2004). Sales forecasting management: a demand management approach. Sage Publica-tions.
Merminod, N., Large, R. O., & Paché, G. (2019, July). Procurement of advanced logistics services: proposition of a rea-soned action model of individual buying behavior. In Supply Chain Forum: An International Journal (Vol. 20, No. 3, pp. 169-184). Taylor & Francis.
Mir, A., Lazaar, S., & Balambo, M. A. (2021). The logistics service provider as an integrator of supply chain. Evidences from an emerging market. Revue Européenne d’Économie et Management des Services, 2021(12), 69-91.
Mohanty, M., & Shankar, R. (2020). DEA-ADALINE: an approach to improve the relative efficiency of 3PLs provid-ers. Benchmarking: An International Journal, 27(1), 166-191.
Morlidge, S., & Player, S. (2010). Future ready: How to master business forecasting. John Wiley & Sons.
Mortensen, O., & Lemoine, O. W. (2008). Integration between manufacturers and third party logistics provid-ers?. International Journal of Operations & Production Management, 28(4), 331-359.
Musarat, M. A., Alaloul, W. S., Rabbani, M. B. A., Ali, M., Altaf, M., Fediuk, R., ... & Farooq, W. (2021). Kabul river flow prediction using automated ARIMA forecasting: A machine learning approach. Sustainability, 13(19), 10720
Nettsträter, A., Geißen, T., Witthaut, M., Ebel, D., & Schoneboom, J. (2015). Logistics software systems and functions: an overview of ERP, WMS, TMS and SCM systems. Cloud computing for logistics, 1-11.
Nguyen, X. H. (2020). Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river. Advances in Water Resources, 142, 103656.
Noh, J., Park, H. J., Kim, J. S., & Hwang, S. J. (2020). Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management. Mathematics, 8(4), 565.
Patil, H., & Divekar, B. R. (2014). Inventory management challenges for B2C e-commerce retailers. Procedia Economics and Finance, 11, 561-571.
Peels, R., Udenio, M., Fransoo, J. C., Wolfs, M., & Hendrikx, T. (2009). Responding to the Lehman Wave: Sales forecast-ing and supply management during the credit crisis.
Pinna, R., Carrus, P. P., & Pettinao, D. (2010). Supply Chain Coordination and IT: the role of third party logistics provid-ers. In Management of the Interconnected World: ItAIS: The Italian Association for Information Systems (pp. 299-306). Physica-Verlag HD.
Poll, R., Polyvyanyy, A., Rosemann, M., Röglinger, M., & Rupprecht, L. (2018). Process forecasting: Towards proactive business process management. In Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings 16 (pp. 496-512). Springer International Publishing.
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