Demand forecasting is a common business practice for optimizing workflow in inventory, but it actually has use cases across all industries even if it isn’t immediately clear. Let’s walk through how demand forecasting can be used and explore its value.
What is demand forecasting?
Demand forecasting is a process that takes historical sales data and uses it to make estimations (or forecasts) about customer demand in the future. For enterprises, demand forecasting allows for estimating how many goods or services will sell and how much inventory needs to be ordered.
Demand forecasting lays the foundation for many other critical business assumptions such as turnover, profit margins, cash flow, capital expenditure, and capacity planning. Demand forecasting is often associated with managerial economics and supply chain management, but it applies to every company in every industry.
What are demand forecasting methods?
In order to forecast demand, we must have historical data on the market and past revenue, but the time span, the scope of the market, and other details can change the results. There are six common ways to calculate a demand forecast, but even these methods can be tweaked to meet the needs of a company.
- Passive – Passive demand forecasting is common in small businesses, because it is the simplest way to estimate future demand. In this method, only past demand performance is used to make predictions about future demand. This means it can be potentially inaccurate, but easier to calculate a result (ie. for the last 19 weeks, carrots sold at 13 cents a piece, therefore we can expect for them to sell at 13 cents this next week).
- Active – Active demand forecasting is typically used by companies that are growing and expanding. The active method of predicting demand takes into account aggressive growth plans such as marketing or product development and also the general competitive environment of the industry.
- Short-term – Short-term demand forecasting only predicts demand for three to 12 months in the future. This can give businesses an idea of what to expect within the next few quarters up to a year, but not longer. Seasonal demand is often calculated this way.
- Long-term – Long-term demand forecasting is used to predict demand for more than a year in the future, often up to three or four years out. Marketing and product strategies are often based on this type of demand forecast.
- External macro level – External demand forecasting is based upon the macroeconomics of the market and external environmental factors. These types of predictions drive internal business decisions, such as product portfolio evaluation and expansion and the development of new customer segments.
- Internal business-level – Internal business-level demand forecasting takes into account only internal metrics such as revenue, costs of goods sold, profit margins, cash flow, etc. This does not take external data into account, so it makes forecasts based only on current business processes.
Why is demand forecasting important?
Demand forecasting is a pivotal business process. Many strategic and operational tactics are based on this forecast, such as budgeting, financial planning, sales and marketing plans, and capacity planning. Because so many business decisions are contingent on demand forecasts, it is crucial to get an accurate prediction. Imagine if demand is predicted to grow, and the company is liberal with its yearly budgets as a result, but demand actually shrinks.
Demand forecast calculations rely on a large amount of data, and are custom to a company’s specific situation, often making them proprietary.
Many businesses rely on machine learning models to do the demand forecast calculation. This makes the forecast more accurate and reliable while saving human time that would otherwise be spent on manual calculations.
The great thing about using machine learning for demand forecasting is that once the model is built to calculate a specific formula for future demand, it can update predictions as time passes. That way, there is always a real-time prediction available that includes any new data.
How Algorithmia can help
Demand forecasting is best done using machine learning, and Algorithmia provides the best machine learning framework available. Our serverless microservices architecture can help your team get their ML models up and running as quickly as possible by navigating around common roadblocks.
The time and money invested in ML must demonstrate a business value to be worthwhile, and machine learning is a big investment. That’s why we want to help companies extract value from their ML investments sooner. Our framework can make that happen. Check out our product and see how it can help your organization make more accurate demand forecasts faster.
Algorithmia is fortunate to work with companies across many industries with varied use cases as they develop machine learning programs. We are delighted to showcase the great work one of our customers is doing and how the AI Layer is able to power their machine learning lifecycle.
Tevec is a Brazil-based company that hosts Tevec.AI, a supply chain recommendation platform that uses machine learning to forecast demand and suggest optimized replenishment/fulfillment order for logistics chains. Put simply, Tevec ensures retailers and goods transport companies deliver their products to the right place at the right time.
In founder Bento Ribeiro’s own words, the “Tevec Platform is a pioneer in the application of machine learning for the recognition of demand behavior patterns, automating the whole process of forecasting and calculation of ideal product restocking lots at points of sale and distribution centers, allowing sales planning control, service level, and regulatory stocks.”
Tevec runs forecasting and inventory-optimization models and customizes user permissions so they can adjust the parameters of their inventory routine, such as lead times, delivery dates, minimum inventory, and service levels. Users can fine-tune the algorithms and adapt for specific uses or priorities.
The challenge: serving and managing at scale
Initially, Tevec was embedding ML models directly into its platform, causing several issues:
- Updating: models and applications were on drastically different update cycles, with models changing many times between application updates
- Versioning: model iterating and ensuring all apps were calling the most appropriate model was difficult to track and prone to error
- Data integrations: manual integrations and multi-team involvement made customization difficult
- Model management: models were interacting with myriad endpoints such as ERP, PoS systems, and internal platforms, which was cumbersome to manage
Algorithmia provides the ability to not worry about infrastructure and guarantees that models we put in production will be versioned and production-quality.”
Luiz Andrade, CTO, Tevec
The solution: model hosting made simple with serverless microservices
Tevec decoupled model development from app development using the AI Layer so it can seamlessly integrate API endpoints, and users can maintain a callable library of every model version. Tevec’s architecture and data science teams now avoid costly and time-consuming DevOps tasks; that extra time can be spent on building valuable new models in Python, “the language of data science,” Andrade reasons. That said, with the AI Layer, Tevec can run models from any framework, programming language, or data connector—future-proofing Tevec’s ML program.
With Algorithmia in place, Tevec’s data scientists can test and iterate models with dependable product continuity, and can customize apps for customers without touching models, calling only the version needed for testing.
Algorithmia’s serverless architecture ensures the scalability Tevec needs to meet its customers demands without the costs of other autoscaling systems, and Tevec only pays for compute resources it actually uses.
Tevec continues to enjoy 100-percent year-on-year growth, and as it scales so will its ML architecture deployed on Algorithmia’s AI Layer. Tevec is planning additional products beyond perfect order forecasts and it is evaluating new frameworks for specific ML use cases—perfect for the tool-agnostic AI Layer. Tevec will continue to respond to customer demands as it increases the scale and volume of its service so goods and products always arrive on time at their destinations.
Algorithmia is the whole production system, and we really grabbed onto the concept of serverless microservices so we don’t have to wait for a whole chain of calls to receive a response.”
Luiz Andrade, CTO, Tevec
Read the full Tevec case study.