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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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... 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. ...
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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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... 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. ...
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... Dengan demikian, semakin kecil nilai MAPE maka semakin baik hasil peramalannya. Berikut ini meruapakn persamaan dari MAPE (Vandeput, 2021): ...
Penentuan Nilai Alpha Pada Double Exponential Smoothing Brown Dengan Evolutionary Algorithm & Excel Solver (Studi Kasus: Peramalan Penerimaan Pajak Pemerintah Pusat Indonesia)
Article
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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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Full-text available
  • Jan 2024
  • MAUSAM
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Inventory Optimization: Models and Simulations
Book
Full-text available
  • Aug 2020
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Kernel Density Estimation
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  • Jan 2001
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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Article
  • Aug 2020
  • INT J FORECASTING
Recurrent Neural Networks (RNNs) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as exponential smoothing (ETS) and the autoregressive integrated moving average (ARIMA) gain their popularity not only from their high accuracy, but also because they are suitable for non-expert users in that they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN architectures for forecasting, and we develop guidelines and best practices for their use. For example, we conclude that RNNs are capable of modelling seasonality directly if the series in the dataset possess homogeneous seasonal patterns; otherwise, we recommend a deseasonalisation step. Comparisons against ETS and ARIMA demonstrate that (semi-) automatic RNN models are not silver bullets, but they are nevertheless competitive alternatives in many situations.
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Conference Paper
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