Importance of Artificial Intelligence in Commodity Forecasting

Artificial Intelligence in Commodity Forecasting

Artificial intelligence (AI) has recently been employed for various supply chain applications, ranging from commodity forecasting analysis to supplier risk management. The price of commodities is currently predicted using the same methodology.

This can yield vital insights in the proper scenario. AI allows us to examine larger, more complex data sets over a longer period, improving forecast accuracy and accelerating decision-making in near real-time. While commodity producers and traders are significantly investing in this technology, commodity purchasers are trailing.

AI-commodity forecasting insights 

If your commodity forecasting prices are important to your business, you may wonder if adding artificial intelligence (AI) to your commodity forecasting insights is time. AI is used in various industries to help businesses make better decisions, so why not commodities?

The technical reason behind considering Artificial Intelligence in commodity forecasting

Because of the huge quantity of AI research being performed, the domain of artificial intelligence is fast growing. The world’s largest companies, businesses, academic institutes, and governments financing major AI research programs.

Natural language processing and machine learning are typically used in commodity forecasting to systematically break down organized and unstructured data and construct models that anticipate commodity prices with minimum human interaction.

Things that would not ordinarily be visible to the naked eye may therefore be brought to the forefront, allowing manufacturers to foresee production, traders to forecast price, and buyers to arrange more strategic procurement.

The advantages of NLP technology include fewer manual processes for users. It captures the data that a user would normally enter into a transaction capture system, reducing the chance of human mistakes. This software also decreases operational risk by collecting contractual commitments made by users throughout the day and storing them as a source of proof for compliance purposes.

But on the other hand, machine learning comprises algorithms that can train over a period to behave and act like people to enhance forecasting. With a “supervised learning” method, when these algorithms are exposed to new sources, professionals training the models may assure that they are always improving.

Thinks to consider before diving into Artificial Intelligence based forecasting

1. The amount of data you have:

AI needs data to function, so if you do not have a lot of data on commodity forecasting, it may not be able to help you as much as you need. Hence its vital to have lot of data to get the most accurate data. More the data more the training occurs via Artificial Intelligence algorithms.

2. The complexity of your data:

Commodity prices can be overly complex, with a lot of varied factors influencing them. If your data is complex, AI may be able to help you identify patterns that you would not be able to see on your own. Since Artificial Intelligence uses various algorithms, it can break down even complex information to simpler terms and forecast accurately.

3. Your budget:

AI can be expensive, so it may not be the right solution for you if you are on a tight budget. For the growth of business, it is vital to have AI in your business. But one must be mindful before investing in any tools. Deeper research on how to use data acquisition must be clearly analyzed before diving in.

4. Your business goals:

Can use AI for various purposes, so it is important to consider what you want to use it for. Artificial Intelligence provides greater results if the ultimate business goal is to get optimized and great insights into commodity price forecasting and market trends.

But if you are looking to use AI for other purposes, such as predicting future commodity prices, you may need to combine AI with other tools for better results. A clear-cut idea of business strategy formulation for insight generation, analyzing tons of data, metric tracking, performance tracking, predictions and many more, can be combined with machine learning and artificial intelligence algorithms.

If you are commodity prices are important to your business and you have the data and budget to support AI, it may be time to add AI to your commodity insights. However, it is important to consider all the factors before making your decision.

Artificial Intelligence Role in Commodity Management in the Future

With more volatility and unpredictability impacting commodities, and more data sources accessible to help decisions, one thing is certain: AI will play a significant part in commodity intelligence in the future. As a result, there are several organisations now offering AI solutions for commodities managers. We should know since we are one of them.

However, we also know that racing after the latest technology is not always the best option – acceptance, adaptability, and ROI all play a role – and hence conventional methods to commodity forecasting management continue to deliver a significant quantity of value.

AI can provide commodity managers with the insights they need to make better decisions and predict future commodity forecasting prices.

So far, AI has been used successfully in a variety of industries, including healthcare, retail, and finance. And there is no reason to believe that it will not be just as successful in commodity management. As more businesses adopt AI, the technology will become more sophisticated and able to provide even better insights.

Importance of AI to Predict Live Commodity Forecasting Prices

Artificial intelligence isn’t something new to explain, since it is now more and more common all around us through phones and other technology. the algorithm incorporates and produce results which are dependent on the input data supplies to predict real-time prices commodities

AI-Based – Live Commodity Price Forecasting

Traditional forecasting methods are based on a quantitative and qualitative assessment of demand- and supply-side factors and statistical models that are based on multivariate approaches or historical data on pricing.

These methods, on the contrary, are unable to accurately reflect all market variables, and can even be unsuitable when forecasting longer-term time horizons (weekly/monthly).

However AI/ML-based forecasting models are able to give more accurate projections of regions, and can provide more accurate exchanges over longer time frames.

The algorithms are also able to process huge amounts of historical data in order to discover hidden trends, which can help companies make better informed and more successful business decision-making. The benefits of AI-based commodity forecasting:

• Ability to manage extreme price volatility.
• Capability to incorporate different predictors from various sources.
• The ability to accurately forecast across several forecast time frames (daily, monthly, weekly).
• Model Interpretability or the ability to comprehend what is important to a given variable

More About PriceVision

PriceVision is an AI/ML based commodity price forecasting solution from ThouCentric Labs to ensure businesses have accurate and interactive forecasts. From agri to non-agri products.

How PricVision Helps to Predict Future Demand

PriceVision helps predict future demand for every product and get real-time insights to let retailers be more competitive. It enables smart and profitable business decisions by generating price forecasts on a daily, weekly, and monthly basis.

• Validate your analysis through charts and technical analysis.
• Save money by avoiding losses and missed opportunities.
• Forecasting prices for all geographies
• Easy to use data drivers technology
• ML-based commodity price prediction
• Broader horizons for commodity forecasting

If you are looking for help with commodity forecasting and commodity prediction it is time to consider AI. To get a quick business consultation, contact@pricevision.ai  W