Modern enterprise strategic decision-making requires the availability of data, actionable insights and instant access to contextual information with powerful algorithms.
Nigerian commodities trading firms are faced with unpredictable government regulations, low margins, unharnessed data, broken enterprise systems and disempowered teams.
The urgency to modernize will be critical to the profitability of these firms and digitalization is a step forward.
This is because digital transformation in the commodities market has been hastening at an extraordinary pace backed by increased regulation and the disruptive force of AI.
In the evolving world of AI, technology is bringing in more transparency in this opaque market where traders with proprietary information used to have an edge.
Now there is a tougher push to put a sharp focus on leveraging technology to extract value from data to recompense for thinner margins in commodities trading.
We have seen that profit management using predictive analytic tools is leading the way in helping commodity traders regain their information edge.
More so, successes in this space have been possible by AI-driven applications matched with empowered data science team.
What we are learning:
Gigantic amount of amorphous data hides business value; we need to be unlocked this for effective trading business outcomes in Nigeria.
There is need for an enterprise-wide application that enables firms to navigate different subsidiary needs, challenges; enabling aggregation demand and augmentation of margins.
Promoting manual efforts with the involvement of different people take time and reduces productivity; this brings in inconsistent results and errors.
AI-powered enterprises are shifting housing its data teams in HQ to democratize them across the enterprise, giving opportunities for live data crunching and enhancing decisions that drives innovation and profitability at scale.
This is empowering teams at the frontline with the capacity to drive up margins.
The availability of APIs from data aggregators like S & P Global Platts is enabling traders contextualize the market; improving the understanding of market dynamics so helping firms deliver on contractual terms.
We understand that the best solution should incorporate learning models; this can help in efficiently solving enterprise data problems.
There is an eternal streaming of data; having a feedback loop that takes this in, is critical in building a robust system that keeps learning and improving different models and outcomes.
Also, there is the opportunity to unlock business value from enterprise data by extracting intelligence from complex enterprise documents.
All the above are not exhaustive but raise the prospect for enabling hyper-competition, consumerism and profitability for Nigerian commodities trading firms.
Without gainsaying, AI has found commodities trading. It is helping engineers and data scientists in aspects of their roles.
Such as predictive maintenance on equipment, competitor pricing, demand sensing, and streamlining routine processes.
Intuitively, traders can carry out demand sensing. This is about the future consumption based on historical sale and external features, possibly enabled by API(s) and incorporation of data from diverse sources e.g. S & P Global Platts etc.
With this new capacity for trading firms, AI/ML is helping traders carry out disruption prediction: future supply chain disruption such as late
shipment, stock out & supply shortage.
We can finally adjust prediction and adjustment of supply chain parameters such as lead time, yield etc.
How can commodity traders take advantage of these breakthrough technologies?
The local commodity traders need to invest heavily in big data and technology if they are to arrest the relentless erosion of profit margins and recapture their information edge over rivals.
Contrarily, if they don’t deploy such infrastructure, there is the possibility of commodity trading firm losing significant market share or becoming a rustic pipe.
This is because the digital economy is a win-takes-it-all game.
While in the past it took months to arrive at corporate decision, that’s has changed tremendously.
AI-driven enterprise-wide applications have split the time into real-time, accurate oil product analytics. It is empowering teams to make better commercial decisions.
As a result, harnessing AI has provided commodities traders with a more accurate and timely view of market fluctuations, ultimately allowing companies to do much more in far less time.
With ML algorithms crawling through enterprise’s database, we can find patterns that are similar to the current problem the company is aiming to solve.
Also, we expect the revolution to proceed further. NLP algorithms are now capable of extracting business insights and emerging trends from texts, speech and sound, while computer vision algorithms determine the objects inside images and videos.
Supply chains aren’t exempted. A combination of RPA and AI, are enabling better, faster or more accurate analytics and forecasting.
They include but not only profit management using predictive tools, sophisticated transportation routing model, demand-planning algorithms that incorporate weather and other causal factors
Or document scanners capable of extracting and matching key contract and payment information from unstructured documents, which previously would have required human involvement.
For commodity traders the opportunity has come to look beyond trade and shipping schedules in order to anticipate the arrival of commodities.
Looking at your enterprise data and incorporating other diverse data sources is strengthening profitability.
AI combines historical delivery information with customer feedback, weather reports and logistics to give an accurate prediction of when products will get to your customers but also opportunity to grow margins.
This beautiful outcome is a more unified company that is able to make decisions faster and satisfied customers who cherish improved consumerism.
Finally, digitalized commodities trading are no longer a welcome competitive edge. It is a matter of enterprise survival. AI-driven enterprise platforms would reduce your business fragility.
While Statistics help us to manage large quantities of data; AI is helping traders to find and understand all possible relations between the variables and the prices.
Thereby heralding epochal times for intelligent firms and transforming a perennial low margin commodities business.
About the Author
Caesar Keluro, works for Nanocentric Technologies Limited
Featured Image: Openpr
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