How does Amazon manage to flawlessly deliver millions of products across the globe every day? Or a restaurant chain accurately predict demand, plan inventory and distribute staff coverage across its footprint? The secret sauce is a blend of two potent mathematical techniques: forecasting and optimization. While each is powerful in its own right, their combined capabilities can streamline the achievement of business objectives.
State-of-the-art (SOTA) time-series ML forecasting models almost seem magical when they can predict a future value with high accuracy. Forecasting is used to predict stock prices, weather, retail demand, economic indicators like inflation, interest rates, and even climate change. On the other hand, the right optimization algorithm can find solutions to complex business constraints such as getting staff to needed locations, products to customers, and energy to homes on time and within cost.
Combining Forecasting and Optimization:
Forecasting is undoubtedly a valuable tool, but often the end solution requires more than just predictive insights. Forecasting does not translate insights into actions; optimization does not improve over time or consider the past and future. Real value is unlocked when they are used together.
However, most teams face challenges when trying to incorporate optimization solutions. Typically, forecasting is performed by data scientists on an ML platform – after which, business teams must export the data and piece together solutions using separate operations research tools. This is extremely time consuming and limits real-time updates to models.
To streamline this process, Abacus.ai offers an approach where you can combine both optimization and forecasting within the same platform. In fact, we are the only ML platform that provides a dedicated optimization use case. Our end-to-end MLOps system allows you to set up pipelines, train models, evaluate their performance, and maintain them over time—all in one place.
Moreover, you can integrate multiple use cases—such as predictive modeling, Large Language Models (LLMs), optimization, and intelligent agents—to solve complex, real-world business problems.
We’re not just an ML platform; we offer a comprehensive toolkit with the ultimate goal of empowering you to make data-driven business decisions. We can work effectively even with sparse data sets, chain multiple ML models together, and even import your existing models.
Autonomous Forecasting and Planning
Combining ML and optimization enables the creation of robust, autonomous forecasting and planning services within a matter of hours. The diagram above brings this to life – here, a restaurant chain needs to forecast demand and optimize staff across its restaurant footprint. Data scientists can merge multiple data sets, create a predictive forecasting model considering historical sales, weather and other factors, and then optimize specific staff types to specific restaurants to maximize profit.
Abacus has worked with clients across retail, distribution, oil and gas, energy and financial services to implement chained optimization and ML models. Further examples include:
- Oil and Gas Sector: Imagine being able to predict fluctuations in energy demand and then adjust your production and supply chains accordingly. This level of foresight enables you to determine the optimal drilling rate based on current oil prices and asset utilization, thereby maximizing profitability.
- Energy Grid Management: By understanding future energy demand, You can manage energy generation and placing bids in the energy market.
- Inventory Management: For businesses with multiple SKUs, machine learning models can forecast demand for each SKU and optimize warehouse shifts and production schedules accordingly.
If you’re interested in taking your forecasting and optimization to the next level, contact us at email@example.com for a free Proof of Concept (POC) and/or a consultation.