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Data Science for Supply Chain Forecasting

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Nicolas Vandeput at SupChains
Nicolas Vandeput
  • SupChains
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Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting--from the basics all the way to leading-edge models--will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.
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... The precise procedure for estimating the trend, seasonal factor, and cycle terms is described by O'Connell et al. (1993). Vandeput (2021) presented the basic idea of simple exponential smoothing based on assumptions that the future will be more or less the same as the past. Among the existing forecasting methods that use exponential smoothing, the most well-known are Holt's linear method (Holt, 1957), Multiplicative Holt-Winters' method (Winters, 1960), and Brown's model (Brown, 1959). ...
... Exponential smoothing is one of the most successful classical forecasting methods. Exponential smoothing is a method of smoothing a time series which has a number of previous hours when forecasting, a computational procedure that includes the processing of all observations, taking into account the aging of information as it moves away from the forecast period (Vandeput, 2021). The principle of operation of this method is a parameter in that the previous values are taken into account with decreasing exponential weights. ...
Time series forecasting of price of the agricultural products using data science
Article
  • Sep 2024
Purpose. The purpose of our article is to research and forecast prices for agricultural products using the example of potato prices based on the most effective models using data science techniques. Methodology / approach. Various forecasting models are explored, starting from baseline models like decomposition and exponential smoothing models to more advanced techniques such as ARIMA, SARIMA, as well as deep learning models including neural network. The data is split into training and testing sets, and models are validated using cross-validation techniques and optimised through hyperparameter tuning. Model performance is evaluated using metrics such as MAE, MSE, RMSE, and MAPE. The selected model is then used to generate future price forecasts, with uncertainty quantified through confidence intervals. Results. The study successfully applied advanced data science techniques to forecast potato prices, leveraging a range of effective models. By analysing historical price data and using various forecasting methods, the research identified the most accurate models for predicting future price trends. The results demonstrate that the selected models can provide reliable forecasts. In particular, the results showed that the model could achieve good forecast results when applied to real problems and, thus, can be effectively used for forecasting tasks especially considering seasonality. In addition, it should be noted that the model has a higher prediction accuracy at the time intervals closest to the original data. The obtained results support using both models simultaneously for forecasting, which can compensate for the shortcomings of each of them. The models can be used separately, to more accurately predict the values for the required period, or a combination of them is also possible. Originality / scientific novelty. The study’s originality lies in development of methods for effectively accounting for seasonality in agricultural price data, such as using seasonal decomposition techniques or more advanced techniques that combine statistical and data science approaches. The novelty implies the implementation of real-time data processing and forecasting system allows for the timely prediction of price changes, enabling stakeholders to make more informed decisions. Practical value / implications. Forecasting potato prices holds significant practical value for various stakeholders. For farmers, accurate forecasts enable informed decisions on the optimal times to plant, harvest, and sell their crops, thereby optimising their profits. In the supply chain, distributors and retailers can use these forecasts to manage inventory more effectively and plan contracts, reducing waste and avoiding shortages. Policymakers benefit from forecasts by anticipating market fluctuations and stabilising prices, which supports both consumers and producers. For consumers, stable pricing ensures better budgeting and helps avoid sudden price spikes, making essential foods more affordable. Overall, accurate price forecasting enhances market efficiency by reducing uncertainty and aiding investors in managing risk.
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... Gradient Boosting allows flexibility in choosing different loss functions depending on the problem, such as mean squared error for regression and deviance (logarithmic loss) for classification. The choice of the loss function influences the algorithm's behavior (Vandeput 2021). Gradient Boosting introduces a learning rate hyperparameter that controls the contribution of each tree to the final prediction. ...
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... If the comment is of positive sentiment, image-caption similarity is utilized to retrieve and rank products. If negative, the engine stops, thus not giving unwanted suggestions to the user [6]. ...
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Article
  • May 2025
In the era of social commerce, recommendation systems must transcend traditional paradigms by incorporating multimodal data to personalize content more effectively. This study proposes a sentiment-guided, multimodal recommendation framework that integrates text and image-based features to enhance product recommendation strategies, particularly in influencer marketing and fashion retail. The proposed architecture utilizes Gated Recurrent Units (GRUs) for analyzing user-generated textual comments to capture emotional tone and sentiment directionality. Concurrently, a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) pipeline processes product images, extracting semantic features and generating descriptive captions that encapsulate visual attributes such as color, category, and brand. By fusing textual sentiment with visual embeddings, the system produces contextually relevant and emotionally resonant recommendations. The GRU-based sentiment classifier achieves a classification accuracy of 89%, while the CNN-LSTM image captioning module attains a BLEU-1 score of 0.68 and 85% accuracy in visual attribute prediction. The real-time filtering mechanism dynamically modulates content delivery based on sentiment polarity—suppressing recommendations during negative user sentiment to reduce cognitive load and enhance user trust. Designed with scalability in mind, the system supports real-time interaction through streaming APIs and can be deployed on cloud platforms or edge devices. It enables automated visual tagging, reduces manual annotation overhead, and improves content discovery by aligning recommendations with both emotional and aesthetic user preferences. This architecture thus transforms passive browsing into an adaptive, emotionally intelligent interaction—offering a significant leap forward in personalized marketing and user engagement in visually rich environments.
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... Nicolas Vandeput (2021) demonstruje, jak przy minimalnym wysiłku, stosując narzędzia open source, można stworzyć prognozy zbliżone do najbardziej nowoczesnych rozwiązań rynkowych. Otwiera to drogę małym i średnim przedsiębiorstwom do korzystania ze swoich danych w celach prognozowania i optymalizacji łańcuchów dostaw. ...
Wykorzystanie nowych narzędzi analitycznych w zarządzaniu łańcuchami dostaw – studium przedsiębiorstwa LOKAD
Article
  • Jan 2023
Streszczenie: Zarządzanie łańcuchami dostaw w erze współczesnej wymaga stosowa- nia zaawansowanych narzędzi i metodologii, aby sprostać dynamicznym wyzwaniom rynkowym. Celem artykułu jest przedstawienie dwóch modeli referencyjnych w zarzą- dzaniu łańcuchami dostaw: SCOR (Supply Chain Operations Reference) oraz GSCF (Global Supply Chain Forum). Artykuł skupia się na tym, jak wykorzystanie narzędzi big data i data science może wzmocnić te modele, umożliwiając lepsze monitorowanie, optymalizację procesów i reakcję na zmiany rynkowe. Zastosowanie tych metod w rze- czywistych środowiskach biznesowych zostało przedstawione na przykładzie implemen- tacji technologii analizy danych w firmie LOKAD. Wyniki rozważań pokazują, że anali- za danych w czasie rzeczywistym pozwala na precyzyjne prognozowanie popytu, optymalizację zapasów i identyfikację ryzyka. Na poziomie operacyjnym i taktycznym narzędzie big data może być wykorzystywane do optymalizacji tras pojazdów, zarządza- nia flotą, poprawy obsługi klienta i rekomendacji produktów. Na poziomie strategicz- nym big data wspiera projektowanie produktów, planowanie sieci i strategię biznesową. Słowa kluczowe: big data, data science, zarządzanie łańcuchami dostaw, SCOR, GSCF, Supply Chain Scientist. Kod JEL: C8
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... In the preface to his book "Principes de la théorie des richesses" (1863), Cournot describes the genesis of his life as an author and confides that his volume "Recherches sur les principes mathématiques de la théorie des richesses" was a pivotal moment in his life, for it was from this point that he decided to move from "being a critic, editor, or translator" to "being an author". 7 Vandeput, N. (2021). Data Science for Supply Chain Forecasting. ...
Market demand analysis and forecasting through applied econometrics: Empirical research in the public transit sector in the Toronto City Area (1969-2019)
Article
Full-text available
  • Oct 2023
Findings. (i) We shall provide a price-demand sensitivity model (fare in C$) for ridership demand inside the Toronto city area from 1969 to 2019 as well as the derivation of short- and long-term elasticities. We shall also provide, through modelling and forecasting, insight into future demand trends considering pre- and post-pandemic scenarios. It is clear that one of the most remarkable points to emerge from the transit data in recent times is that the effects of the pandemic crisis have slowed ridership demand by almost half (225 million riders in 2020) compared to the previous trend (525.5 million riders in 2019) in the City of Toronto (TTC operating service area only), causing a structural break in the time series data, also observed in the TTC's latest report (only 197.8 million riders in 2021). This is not an isolated case for the city of Toronto, as Montreal (only 200 million users in 2020 compared to 426 million in 2019) and most of the major metropolitan cities in the world that were affected also experienced the same slowdown in demand due to reduced activity during the pandemic. (ii) Using our estimated market demand model for transit in the city of Toronto, we will attempt to estimate the optimal price and the price at which consumers are no longer willing to pay. And since this study concerns a public transit company in the form of a natural monopoly commonly referred to as a state monopoly, we shall draw on the seminal work and contributions of Frank Ramsey and Marcel Boiteux (Ramsey-Boiteux pricing rule) to understand the optimal pricing behaviour of this type of network infrastructure. The aim of this research will therefore also be to understand, beyond the elements of microeconomic optimization, that the nature of the company is at the center of its interests, which has a considerable influence on certain aspects that determine its business model and in particular its pricing behaviour (Baumol, 1959). Keywords: market demand, sensitivity analysis, time series modeling and forecasting, pricing behaviour. JEL Classification: C22; C63; C87; D12; D22; D42
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... This paper utilizes Key Performance Indicators (KPIs) for numerical prediction, referencing the algorithm presented in [60]. Formulas (17) to (24) are employed as indicators for model predictions. ...
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... where n is the number of data points, x i is the measured data i, and  x i is its corresponding estimation. For more information on the RMSE, the interested reader is referred to [49]. ...
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Full-text available
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The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.
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... An essential element in the supply chain is logistics. This activity consists of the process of planning and executing transport, the efficient storage of goods and the correct distribution of the products between customers and suppliers [7,8]. In this case, transport is the central problem for the design of the chain; this is where routes, loads, transport typology, operators, travel times, transport costs and time windows have to be defined. ...
A Routing Model for the Distribution of Perishable Food in a Green Cold Chain
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Full-text available
  • Jan 2024
In this research, we develop an extension of the stochastic routing model with a fixed capacity for the distribution of perishable products with a time window. We use theoretical probability distributions to model the life of transported products and travel times in the network. Our main objective is to maximize the probability of delivering products within the established deadline with a certain level of customer service. Our project is justified from the perspective of reducing the pollution caused by greenhouse gases generated in the process. To optimize the proposed model, we use a Generic Random Search Algorithm. Finally, we apply the idea to a real problem of designing strategies for the optimal management of perishable food distribution routes that involve a time window, the objective being to maximize the probability of meeting the time limit assigned to the route problem by reducing, in this way, the pollution generated by refrigerated transport.
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Interpreting the COVID-19 infection geospatial data of Indian provinces for Alpha, Delta, and Omicron variants using ARIMA and Machine Learning
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Inventory Optimization: Models and Simulations
Book
Full-text available
  • Aug 2020
Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple--yet powerful--framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the do-it-yourself examples and Python programs included in each chapter.
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  • INT J FORECASTING
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