Abstract
Stockouts present significant challenges for Fast-Moving Consumer Goods (FMCG) companies, adversely affecting profitability and customer satisfaction. This research investigates key drivers causing Case Fill Rate (CFR) to fall below target levels and identifies the best model for predicting future CFR for the sponsor company. By utilizing feature importance techniques including Shapley additive explanations (SHAP) value plots, we conclude demand forecast error is the most critical driver influencing CFR. Machine learning classification and regression techniques were deployed to predict shipment cut quantity. To improve longer-term forecasts, a combination of models should be incorporated, along with extended historical data, promotions data, and consideration of exogenous variables. In conclusion, companies should prioritize forecasting accuracy and optimize inventory policy to improve CFR in the long run.
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References
Alzubaidi, Z.Y.: A comparative study on statistical and machine learning forecasting methods for an FMCG company. Rochester Institute of Technology, Scholar Works 96 (2020)
Bhandalkar, S.: FMCG Market Expected to Reach $15,361.8 Billion by 2025, Allied Market Research. https://www.alliedmarketresearch.com/press-release/fmcg-market.html. Last accessed 1 Dec 2022
Blattberg, R.C., Neslin, S.A.: Chapter 12 sales promotion models. In: Eliashberg, J., Lilien, G.L. (eds.) Marketing, Handbooks in Operations Research and Management Science, vol. 5, pp. 553–609. Elsevier (1993)
Calhoun, S.: On-Time, In-Full (OTIF): A Key Supply Chain Metric. https://www.veryableops.com/blog/on-time-in-full-otif. Last accessed 1 Dec 2022
Carvalho, H., Naghshineh, B., Govindan, K., Cruz-Machado, V.: The resilience of on-time delivery to capacity and material shortages: an empirical investigation in the automotive supply chain. Comput. Ind. Eng. 171, 108375 (2022)
Carbonneau, R., Laframboise, K., Vahidov, R.: Application of machine learning techniques for supply chain demand forecasting. Eur. J. Operat. Res. 184(3), 1140–1154 (2008)
Chase, C.W.: Machine learning is changing demand forecasting. The J. Bus. Forecast. 35(4), 43–45 (2016)
Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D.: Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Manag. Sci. 46(3), 436–443 (2000)
Dickson, B.: Machine learning: What’s the difference between supervised and unsupervised? TheNextWeb.Com [blog]. Advanced Technologies & Aerospace Collection. https://www.proquest.com/blogs-podcasts-websites/machine-learning-what-s-difference-between/docview/2407960774/se-2?accountid=12492. Last accessed 1 Dec 2022
Drew Editorial Team. 10 Key Performance Indicators for production management. http://blog.wearedrew.co/en/10-key-performance-indicators-for-production-management. Last accessed 1 Dec 2022
EKN Research: Plugging Out-of-Stock Gaps in Consumer Goods, RIS News, https://risnews.com/ekn-research-plugging-out-stock-gaps-consumer-goods. Last accessed 1 Dec 2022
Gruen, T.W.: A Comprehensive Guide to Retail Out-of-Stock Reduction in the Fast-Moving Consumer Goods Industry. https://www.nacds.org/pdfs/membership/out_of_stock.pdf. Last accessed 1 Dec 2022
Gundogdu, B., Maloney, J.: Comparison and financial assessment of demand forecasting methodologies for seasonal CPGs. In: Supply Chain Management Capstone Projects, Massachusetts Institute of Technology (2019)
Henry, J.: Data Analytics and Machine Learning Fundamentals Live Lessons Video Training, 1st edn. Addison-Wesley Professional (2019)
Inderfurth, K.: Safety stock optimization in multi-stage inventory systems. Int. J. Product. Econ. 24(1), 103–113 (1991)
Infosys BPM, Big Data Analytics in CPG: Insights Into Its Benefits. https://www.infosysbpm.com/blogs/retail-cpg-logistics/why-big-data-and-analytics-is-a-must-for-profitable-growth-in-cpg.html. Last accessed 1 Dec 2022
ITC Infotech, Inventory Management and Optimization for an FMCG Manufacturing Company. https://www.anylogic.com/resources/case-studies/inventory-management-and-optimization-for-an-fmcg-manufacturing-company/. Last accessed 1 Dec 2022
Lohman, C., Fortuin, L., Wouters, M.: Designing a performance measurement system: a case study. Eur. J. Operat. Res. 156(2), 267–286 (2004)
Manyika, J., Chui, M., Brown, B.: Big data: The next frontier for innovation, competition, and productivity | McKinsey. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation. Last accessed 1 Dec 2022
Nielsen IQ, Can the FMCG industry afford to lose billions from empty shelves? https://nielseniq.com/global/en/insights/education/2022/can-the-fmcg-industry-afford-to-lose-billions-from-empty-shelves/. Last accessed 1 Dec 2022
Nigam, A.: Product promotion effectiveness: root causes of stock-outs by. In: Supply Chain Management Capstone Projects. Massachusetts Institute of Technology (2016)
Raman, A., Kim, B.: Quantifying the impact of inventory holding cost and reactive capacity on an apparel manufacturer’s profitability. Product. Operat. Manag. 11(3), 358–373 (2002)
Zoellner, F., Schaefers, T.: Do price promotions help or hurt premium-product brands? the impact of different price-promotion types on sales and brand perception. J. Advert. Res. 55(3), 270–283 (2015)
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Siddiqui, K.I., Lee, M.M.Y., Koch, T., Dugundji, E. (2023). Case Fill Rate Prediction. In: Terzi, S., Madani, K., Gusikhin, O., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2023. Communications in Computer and Information Science, vol 1886. Springer, Cham. https://doi.org/10.1007/978-3-031-49339-3_18
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