Demand forecasting

Demand forecasting refers to the process of predicting the quantity of goods and services that will be demanded by consumers at a future point in time. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods. Qualitative methods are based on expert opinion and information gathered from the field. This method is mostly used in situations when there is minimal data available for analysis such as when a business or product has recently been introduced to the market. Quantitative methods, however, use available data, and analytical tools in order to produce predictions. Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirement

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Production management deals with producing goods sustainably at the right time, quantity, and quality with the minimum cost. This course equips students with practical skills and tools for manufacturing companies' demand management, supply management, and advanced supply chain analytics.

Continuous Improvement encompasses the ongoing effort to capture, create, and deliver value to internal and external customers. This course equips students with practical skills and tools to improve products/services/processes with the support of technology and empowering people.

This course teaches students the power of building and implementing marketing strategies in order to help businesses to commercialize successfully their technological innovations.
It offers a large overview of modern marketing and is not suited for students with advances marketing knowledge.

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Forecasting

Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the

Demand forecast for pharmaceutical company

Daniele Messina

2009

Generating daily activity schedules using machine learning Master Thesis Sergej Gasparovich June 19, 2020 Prof. M. Bierlaire, J. Pougala, T. Hillel Transport and Mobility Laboratory, EPFL Y. Liu Visual Intelligence

Sergej Gasparovich

The study of how people schedule their daily activities is of interest in the context of transport demand forecasting using activity based-models, where activity schedules are generated in order to estimate the trip demand they produce. Machine learning has achieved efficient results in a number of areas due to its strong data processing capacity and can be useful for demand forecast models. This paper demonstrates for the first time how Generative Adversarial Networks (GAN) can be applied in this context. We create a GAN model adapted for the specificity of the task, capable of working with discrete data and generating feasible daily activity patterns based on data of Swiss residents from the MTMC 2015 data set. We also show how we can condition our model to generate patterns for distinct days of the week. This work can be used as a framework for the activity generation step for activity-based modelling, in order to provide accurate and diverse activity schedules predictions using machine learning.

2020

Interpretable Social Anchors for Human Trajectory Forecasting in Crowds

Alexandre Massoud Alahi, Parth Ashit Kothari, Brian Alan Sifringer

Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multimodal distributions. In recent years, neural network-based methods have been shown to outperform hand-crafted methods on distance-based metrics. However, these data-driven methods still suffer from one crucial limitation: lack of interpretability. To overcome this limitation, we leverage the power of discrete choice models to learn interpretable rule-based intents, and subsequently utilise the expressibility of neural networks to model scene-specific residual. Extensive experimentation on the interaction-centric benchmark TrajNet++ demonstrates the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.

2021