What are the pros and cons of using AI for forecasting?

When it comes to using AI for forecasting, one of the primary advantages is the flexibility and scalability it offers in deploying various types of forecasting models. AI techniques, such as machine learning algorithms and neural networks, can handle a wide range of data types and complexities, allowing organizations to tailor their forecasting approach to specific business needs. Whether it's time series forecasting, demand forecasting, or predictive analytics for inventory management, AI enables the development of customized models that can capture intricate patterns and relationships in the data.

Retail and CPG companies have to deal with both quantitative and qualitative signals to improve their forecasting accuracy. Many times they need to blend complex data for which a data platform and the ability to run flexible algorithms become critical. For more read, https://cloud.google.com/customers/pluto7

I agree on most of the above points regarding qualitative and quantitative forecasting. In my experience, I've found that just relying on formulas or algorithms to generate my forecasts I am still off and have to make judgement calls on. While I use models daily, I'd say I still have human interaction with any and all forecasts I've created. When I put together a cash forecast for receivables, or a 13 week cash flow for management, I still adjust that data to some extent based on external factors like rates, quarterly adjustments, etc. within our industry or what may be changing on the banking side. Happy to share more details and connect more at www.guernseyconsulting.com.

Combining the power of AI and statistical forecasting overlayed with a qualitative model relying on judgments from seasoned leaders in the business is a winning combination. Leveraging AI for forecasting improves accuracy by relying on the relevant historical data and trends – helps to steer clear from overly optimistic or very pessimistic models and helps reduce human bias. The judgment leverages the unique insights and experience leaders can provide, helping the forecasts be more accurate, especially in dealing with complex situations.

The advantages of using AI for forecasting include hi-tech data analysis and high accuracy. AI systems use complex algorithms to analyze large sets of data and can learn from past trends to predict future outcomes. They are able to adjust to new information and improve their forecasts over time which contributes to high accuracy. On the downside, AI for forecasting can be quite expensive. It requires significant investment in technology and infrastructure. Moreover, AI system might over-rely on data, resulting in poor forecasts if the data is not accurate or if it doesn't adequately represent the situation. Plus, it requires a specific skill set to be fully utilized and understood. ??

Entre los pros de la IA tenemos los siguientes: 1. Puede procesar grandes cantidades de datos y analizar patrones y tendencias de manera más rápida y precisa. 2. Puede identificar relaciones complejas entre diferentes variables y factores que pueden afectar la demanda o los resultados financieros, para tomar mejores decisiones. 3. Puede automatizar procesos manuales y repetitivos, lo que puede ahorrar tiempo y recursos.

AI-based forecasting introduces new evaluation criteria that go beyond traditional metrics like accuracy and error rates. While AI models often excel in capturing complex patterns and making accurate predictions, their performance must also be assessed in terms of interpretability, robustness, and scalability. Interpretable AI models are essential for understanding the drivers behind forecast results and gaining insights into underlying business dynamics. Moreover, robustness to changes in data distribution and scalability to large datasets are critical for deploying AI forecasts in real-world settings.

The accuracy measures being tied to the business context is critical. Here is how a customer of our mentioned about it. “Pluto7 helped us optimize our warehouse management, enabling machine learning and AI to forecast demand. Our team was able to gain further interactive insights into demand patterns, forecast roll-ups and drill downs. As a result, we can accurately predict the package material needed and the warehousing costs.” – Mohammed Balwa, CEO, FulFill Plus. Read more here. https://pluto7.com/success-stories/pluto7-along-with-google-cloud-platform-helped-the-customer-to-be-able-to-forecast-80-accuracy-in-the-packaging-of-boxes/

Pros of using AI for forecasting include its high accuracy as it can analyze large volumes of data and identify complex patterns that humans might miss. It's also reliable and adaptable, able to respond to changing trends in real time. Moreover, it reduces the burden of manual analysis and potentially speeds up decision-making. However, AI also has its drawbacks. It can be complex and requires significant computational power and data to function effectively. Misinterpretation of data may lead to inaccurate forecasts, and it may not perform well with insufficient data. Furthermore, relying heavily on AI might make a company vulnerable to technical glitches and cyber-attacks. ??

One of our customer Revv Cars faced a similar challenge in determining the right node. Revv cars have limited machine learning mechanisms with no integration with external demand signals in place for Demand Forecasting to understand future demand on demand and other relevant factors. They calculate the total number of monthly hours a particular vehicle is available for booking. Out of these total hours, certain hours need to be deducted for service & maintenance or other services. The prime objective is to minimize the idle time of vehicles and predict total booking hours at city level. Read the solution here. https://pluto7.com/success-stories/revv-cars-achieved-an-average-forecast-accuracy-ranging-between-65-to-85/

Comparing AI-based forecasting methods with traditional approaches poses its own set of challenges. While AI models may outperform classical statistical techniques in certain contexts, such as handling nonlinear relationships or high-dimensional data, their superiority is not universal. Factors such as data availability, computational resources, and domain expertise influence the choice between AI and traditional methods. Additionally, the interpretability of AI models can sometimes be a double-edged sword, as simpler models may offer more transparent insights into forecast drivers and assumptions.

Pros of using AI for forecasting include higher accuracy, adaptability, and efficiency. AI can process large data sets and identify patterns humans might miss, improving forecast accuracy. It also adapts quickly to changes in data, unlike traditional models with fixed parameters. However, there are cons too. AI models can be complex and require skilled personnel to manage. They are also often more expensive than traditional methods, and there is the risk of overfitting, where the AI becomes too attuned to the training data and performs poorly with new data. Lastly, AI can lack the explanatory power of traditional statistical models; they can predict outcomes but can't always explain why those outcomes will happen.

In a nutshell, while AI presents promising opportunities for forecasting, it needs to be used judiciously considering the resources available and the specific requirements of the task at hand. ????????

AI can quickly gather and organize data for comparison purposes, like creating a base for forecasting methods. It's essential to review and deepen the analysis yourself to ensure accuracy and relevance to your specific needs.

It can analyze large amounts of data quickly and identify patterns that humans might miss. It's often used in finance, weather prediction, sales forecasting, and many other fields where predicting trends is crucial. The accuracy of the forecasts depends on the quality of the data /, so reliance on genAI models is risky and should be reviewed.

La IA ofrece la capacidad de mejorar la precisión, automatizar tareas y adaptarse a los cambios, pero también plantea desafíos relacionados con la calidad de los datos, la transparencia y los riesgos éticos.

Leveraging AI for forecasting offers several potential benefits, including improved accuracy, automation, and adaptability to dynamic environments. AI techniques can analyze vast amounts of data, identify complex patterns, and generate forecasts with higher precision than traditional methods in many cases. Moreover, AI-powered forecasting systems can be integrated with other supply chain processes, such as inventory management and production planning, to optimize decision-making and resource allocation. However, the adoption of AI for forecasting also comes with challenges, such as data privacy concerns, model interpretability issues, and ethical considerations.

There is a lot of imagination that goes on with AI. AI fundamentally depends on data platform and data foundation that is reliable. Mature customers think holistically and plan for it. https://pluto7.com/data-foundation-for-ai/