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What are the best practices for incorporating weather and economic trends into demand forecasting models?

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Demand forecasting is a crucial process for logistics management, as it helps to optimize inventory levels, reduce costs, and improve customer satisfaction. However, demand forecasting can also be challenging, especially when external factors such as weather and economic trends affect the demand patterns. How can you incorporate these factors into your demand forecasting models and ensure accuracy and reliability? Here are some best practices to follow.

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Masoud Salehi Borujeni

Lead Data Scientist at Apetito UK (Ph.D. in Electrical Engineering)

1 Use multiple data sources

One of the best ways to capture the impact of weather and economic trends on demand is to use multiple data sources that provide relevant and timely information. For example, you can use historical sales data, market research, customer feedback, competitor analysis, weather data, economic indicators, and social media trends to enrich your demand forecasting models. By using multiple data sources, you can reduce the risk of relying on a single source that may be inaccurate, outdated, or biased.

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Masoud Salehi Borujeni

Lead Data Scientist at Apetito UK (Ph.D. in Electrical Engineering)
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When incorporating weather data into a demand forecasting model, the initial consideration is selecting the appropriate variable. Air temperature is a common choice, although,for some cases, wind speed or precipitation may be relevant. Following this, one must determine the data resolution. While there exists a strong correlation between weather data fluctuations and demand for some businesses (e.g., even during a summer, the demand for food and beverage businesses relies on daily or hourly temperature/solar radiation), others simply follow the weather trend.

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Luís Fernando David

Engenheiro Produ??o | Engenheiro Seguran?a do Trabalho | Engenheiro Ambiental
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Incorporar tendências climáticas e econ?micas nos modelos de previs?o de demanda é crucial para melhorar a precis?o das previs?es e permitir uma gest?o mais eficiente da cadeia de suprimentos. Alguns exemplos de melhores práticas para incorporar esses fatores: Coleta de Dados Adequada, Integra??o dos Dados, Análise de Impacto Econ?mico, Segmenta??o de Mercado, Feedback Contínuo, Integra??o com Sistemas de Informa??o Geográfica (GIS), Colabora??o Interdepartamental, Planejamento de Contingência. Ao seguir essas melhores práticas, as organiza??es podem melhorar a solides e a eficácia de seus modelos de previs?o de demanda, adaptando-se melhor às mudan?as nas condi??es climáticas e econ?micas.

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2 Apply machine learning techniques

Another best practice for incorporating weather and economic trends into demand forecasting models is to apply machine learning techniques that can analyze complex and dynamic data patterns and generate accurate and adaptive forecasts. Machine learning techniques can use algorithms such as regression, classification, clustering, and neural networks to learn from the data and identify the relationships and dependencies between demand and various factors. Machine learning techniques can also handle uncertainty and variability in the data and adjust the forecasts accordingly.

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3 Segment your demand

A third best practice for incorporating weather and economic trends into demand forecasting models is to segment your demand according to different criteria that reflect the influence of these factors. For example, you can segment your demand by product type, customer group, geographic region, season, or channel. By segmenting your demand, you can tailor your demand forecasting models to each segment and account for the specific characteristics and preferences of each segment. This can help you to improve the accuracy and relevance of your forecasts and avoid overgeneralization.

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4 Validate and update your forecasts

A fourth best practice for incorporating weather and economic trends into demand forecasting models is to validate and update your forecasts regularly and compare them with the actual demand. This can help you to evaluate the performance and accuracy of your demand forecasting models and identify any gaps or errors. By validating and updating your forecasts, you can also incorporate new and updated data and information that may affect the demand patterns and adjust your forecasts accordingly. This can help you to maintain the reliability and validity of your forecasts and avoid surprises or deviations.

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5 Communicate and collaborate with stakeholders

A fifth best practice for incorporating weather and economic trends into demand forecasting models is to communicate and collaborate with stakeholders who are involved in or affected by the demand forecasting process. For example, you can communicate and collaborate with your suppliers, distributors, customers, marketing team, sales team, and finance team to share your demand forecasts and receive their feedback and input. By communicating and collaborating with stakeholders, you can ensure that your demand forecasts are aligned with the expectations and needs of each stakeholder and that they are aware of the assumptions and limitations of your demand forecasting models.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for incorporating weather and economic trends into demand forecasting models?

1

2

3

4

5

6

1 Use multiple data sources

2 Apply machine learning techniques

3 Segment your demand

4 Validate and update your forecasts

5 Communicate and collaborate with stakeholders

6 Here’s what else to consider

Logistics Management

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What are the best practices for incorporating weather and economic trends into demand forecasting models?

1

2

3

4

5

6

1 Use multiple data sources

2 Apply machine learning techniques

3 Segment your demand

4 Validate and update your forecasts

5 Communicate and collaborate with stakeholders

6 Here’s what else to consider

Logistics Management

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