How can you use operations research to predict intermodal demand?

OR models can be used to optimize intermodal transportation networks and operations based on predicted demand. For example, mathematical optimization models such as linear programming, integer programming, or network optimization can be utilized to determine the most cost-effective routes, modes, and schedules for transporting goods or passengers to meet forecasted demand while satisfying various constraints such as capacity limitations, service levels, and regulatory requirements.

Operations Research should be utilized to drive optimization to help reduce the landed cost of goods. It would also help identify modal inefficiencies and find alternative solutions to transport and packaging.

Utilizando parámetros de Investigación de operaciones (teoría de restricciones, maximizar función objetivo, minimizar costos, teoría de rutas, etc) se puede desarrollar un modelo probabilístico que relacione la creciente demanda de transporte con el costo de los diferentes sistemas de transporte (marítimo, carretero, aéreo, etc). Mediante interacciones sucesivas el modelo buscará atender la demanda de transporte utilizando los sistemas transporte que tienen menor costo y tiempo. El método intermodal sería una de las alternativas de transporte que se elegirá después de agotar las alternativas de menor costo. Es necesario estimar la tasa de crecimiento de demanda de transporte y tasa de crecimiento de oferta de sistemas de transporte

Obter o cenário ideal é o que devemos estar obstinado em buscar, associar a teoria a prática é desafiador, principalmente considerando um Operador Logístico Multidepositante, temos flutua??es dos dados devido a sazonalidade de cada segmento, entender este fluxo, com a sazonalidade nos permite adapta??o rápida no modelo de estrutura e atendimento evitando montar estrutura e desmobilizar estrutura, contudo, através de desenvolvimento de fornecedores adequados principalmente para a busca de equalizar a estrutura entre Veículos Próprios para as rotas fixas e contingenciamento de parceiros, Empresas de Transporte de Cargas parceiras (ETC) e possibilidade de contrata??o direta via Transportador Aut?nomo de Carga (TAC).

One way to approach intermodal demand prediction is to use network models that represent the physical and operational characteristics of the transportation system. Network models can capture the flows of goods, the costs and revenues of each mode and route, the capacity constraints, and the service levels

An example I've seen is a logistics company using simulation models to predict intermodal demand for its transportation services. By simulating various scenarios and factors influencing demand, such as market fluctuations, seasonal trends, and capacity constraints, the company was able to assess the impact of different variables on demand and optimize its operations accordingly. This approach allowed them to make informed decisions, allocate resources effectively, and improve overall efficiency in meeting customer demand.

Another way to predict intermodal demand is to use simulation models that mimic the behaviour and interactions of the agents involved in the transportation system. Simulation models can account for the uncertainty, variability, and complexity of the intermodal environment.

Simulation models are key to digitally visualise a complex transportation system. It will show very clearly where bottlenecks are, and the time and cost effects of these bottlenecks.

Os modelos de simula??o s?o essenciais para termos previs?o de quais problemas iremos enfrentar no dimensionamento de recursos, prevendo possíveis erros operacionais e atrasos de atendimento. Assim podemos alocar melhor os recursos gerando menor custo, menor impacto ambiental e consequentemente melhor nível de servi?o por estarmos preparados para determinadas situa??es.

You can predict intermodal demands using operations research by collecting historical data, analyzing trends, and using predictive modeling techniques like regression and simulation. Then, optimize resource allocation based on these predictions for efficient transportation management.

Uma coisa muito útil e que ando utilizando de forma constante, e criar os cenários simulando algumas variáveis, de modo a stressar os cenários possíveis, tentando mitigar riscos e prever possíveis desvios, sendo muito mais assertivo na excuss?o dos processos posteriormente.

Data-driven models leverage historical data to identify patterns, trends, and correlations related to intermodal demand. Machine learning algorithms, such as regression or time series analysis, can be applied to make predictions based on past observations. Analyze historical demand data, considering various influencing factors. Train models to predict future intermodal demand based on identified patterns. Offers a predictive approach based on empirical data. As more data becomes available, the models can be continuously refined and improved, making them adaptable to changing market conditions.

As it is defined data driven model can contribute a lot in this whole process. In demand and supply trends are best worked out for a particular season or particular timeline depending on geographic requirements. Based on transportation hurdles additional clearing supports at locations might be planned for smooth movement to avoid time delays.

Datengetriebene Modelle zur Vorhersage der Nachfrage nach intermodalen Leistungen k?nnen den Operateuren helfen, die Kapazit?ten in genügender Menge bereitzustellen. Die Voraussetzungen sind aber die Integration von regionalen Nachfragen, von unterbrochenen Bahnlinien aufgrund von Baustellen oder Streiks, etc. und von Wetterdaten (Unwetter). Nur so k?nnen die Angebote von der Datenunterstützung profitieren. Wichtig sind auch Daten über Gewichte und Volumen der Ladung. Auch die Beschaffenheit der Einheiten sind wichtig. Sind es Trailer, Wechselbrücken oder Koffer oder sind es Container. Mit dem Zusammenfügen all dieser Daten kann eine datengetriebene Vorhersage funktionieren.

Beware of using historical data to predict the future! Historical data is exactly that 'historical' and that means it tells you what happened in the past...

Intermodal falls into four general categories: Truck, rail, sea, and air. Trucks can easily transport steel freight containers. They are usually the first mode of transportation that intermodal shipping relies on and uses to get the process underway.

Necessário o uso tecnologias de rastreamento com meio de comunica??o GPS, Satelital ou RFID s?o formas eficientes de monitorar a carga durante todo o transporte. Essas tecnologias permitem acompanhar todo o percurso da carga em tempo real, reduzindo o risco de perda ou danos.

Application of OR to Inter-modal transport: First by mapping transport mode in a domain or region and then collecting the details of cost of transportation per unit, capacity constraints, physical constraints, supply/originating quantities/capacities and demand quantities at major destination/distribution ppoints. Further carrying out network analysis through application software of the various algorithms. …see more

Como Operador Logístico, desenvolvemos internamente um software que consolida as informa??es dos processos que est?o chegando do Mercado Internacional pelo ETA (Estimated Time Arrival), para o modal Marítimo com muita flutua??o de setembro de 2023 até fevereiro de 2024 devido aos sucessivos problemas vividos na área (situa??o climática em SC, guerra com mudan?a de rota, indisponibilidade do porto de Itajaí e reforma de 50% dos ber?os de Navegantes com redirecionamento de rota). Para as demandas doméstica, temos a expectativa de venda do cliente que tem ficado em média acima do realizado, hoje, necessitamos de sistemas híbridos que permitam além de trazer dados e fatos, fazer a leitura da situa??o atual sem precedência.

Combining elements of network, simulation, and data-driven approaches, hybrid models offer a comprehensive way to predict intermodal demand. By integrating multiple perspectives and techniques, these models can provide more robust and accurate forecasts.

A analise de dados contribui de forma intensiva a redu??o de desperdícios. Considerando uma Transportadora, Armazém ou Operador Logístico, esta previsibilidade se dá no plano estratégico muito em fun??o da Macro Economia e Analise Histórica do Fluxo Operacional, onde até mesmo para atender as previs?es de S&OP acabam gerando desafios, como é previsto no SCOR (Supply Chain Operation Reference) que os dois pontos de grande diferen?a entre o processo industrial e a cadeia de suprimento se dá na Adapta??o e Flexibilidade, desta forma, obter capacidade de atender as flutua??es é de grande valia dado os desafios em cadeia mundial, seja climática ou situa??o de guerra que implica em toda analise pregressa.

Operations research can predict intermodal demand by utilizing quantitative modeling techniques such as time series analysis, regression analysis, simulation modeling, optimization, and machine learning. These methods analyze historical data, identify patterns, and forecast future demand trends based on factors like economic indicators, population growth, and transportation infrastructure development. By leveraging these techniques, organizations can develop accurate forecasts to optimize resource allocation, improve planning, and make informed decisions in intermodal transportation management.

Each of these approaches has its strengths and limitations, and the choice of method depends on the specific characteristics of the intermodal system, the available data, and the goals of the prediction. Often, a combination of these approaches may provide a more comprehensive understanding of intermodal demand and enhance the accuracy of predictions.

A analise de dados contribui de forma massiva a redu??o de danos e percas no processos. A empresa que possui esta previsibilidade realizada o plano estratégico muito em fun??o da Economia e linear histórico do Fluxo Operacional, onde até mesmo para atender as previs?es de S&OP acabam gerando desafios, como é previsto no SCOR (Supply Chain Operation Reference) que os dois pontos de grande diferen?a entre o processo industrial e a cadeia de suprimento se dá na flexibilidade, desta forma, obter capacidade de atender as flutua??es é de grande importancia dado os desafios em cadeia global, seja climática ou situa??o de conflitos,que podem dificultar a análise.

Make your business *fully* OKR and KPI lead. That way you will spot trends (incl. rise or fall in intermodal demand) very early. Hire great people! Better to have 3 very talented and well paid people than an "army" of low talented people. It will pay off.