Discover AI’s role in predicting demand, optimizing inventory, and streamlining logistics.
Forecasting can result in huge dollars from a revenue perspective. Think of how powerful that is. If your business fails to adequately forecast expected sales for a particular period or product, there’s an overwhelming chance you’ll incur revenue loss and jeopardize customer loyalty. It’s a risk no one can afford to take.
Disruptions aren’t Fun
Disruptions can happen in a minute, or in a day— storms, traffic disruptions, closing of ports, and (lest we forget) global pandemics. Disruptions wreak havoc on your supply chain and jeopardize whether you can meet customer demand, and ultimately customer satisfaction.
It’s not only painful to your bottom line and your customers, but also painful to your employees if they are running around wading through massive Excel spreadsheets and trying to make million-dollar decisions at the last second.
So, being able to react in a timely fashion is critical. This year, more than ever, companies are doubling-down on efforts to create more intelligent order management with AI-infused forecasting analytics. By 2024, 60 percent of Forbes Global 2000 manufacturers will depend on AI for their supply chain logistics. And, as companies like Microsoft build AI copilots into the tools that planners use every day, the ability to do so is becoming a reality and not just a ‘what-if.’
The Difference Between Demand Planning and Forecasting
Demand planning and demand or sales forecasting are methods for planning future demand so you can meet it and still achieve the maximum profit possible. They must work together to maximize the number of revenue opportunities with a minimum of fuss.
Your company can’t have demand planning without good forecasting because demand planning relies on a forecast as the foundation for the plan. Make sense? The primary difference between the two is their place in the overall process — sales forecasting happens first, and demand planning comes after.
Sales forecasting uses historical data and upcoming trends (based on current data) to predict, or “forecast,” how many sales your company should expect, and the inventory needed to meet that demand when the time comes.
Demand planning takes sales forecasting one step further. It starts with the forecast but goes deeper and dives into the nitty-gritty of business operations to consider the logistics of meeting the hypothetical forecast and ensuring it matches reality. It’s the second stage, where business acts on those forecasts— with activities like making sure you have the right amount of inventory in the right place, or making sure that you can move that inventory to meet changes in demand when the time comes.
Is Your Planning Accurate?
Despite best efforts, the numbers planners generate can still be inaccurate. And the more ‘traditional’ the efforts, the greater the potential for error.
Traditional time-series forecasting methods, like the Holt-Winter’s account for a relatively narrow range of demand-influencing factors such as seasonality. In the real world, demand moves up and down in response to numerous market and macroeconomic forces. Such limitations cause traditional solutions to produce poor forecasts, which is reflected in the company’s performance.
According to McKinsey, a 10 to 20 percent improvement in supply chain forecasting accuracy is likely to produce a 5 percent reduction in inventory costs and a 2 to 3 percent increase in revenues. In a world where margins are increasingly narrow and critical, this percentage can be make or break.
AI-infused Predictive Analytics
Data science and predictive analytics combine machine learning models with statistical methods. It allows for not only estimating demand but also for understanding what drives sales and how customers are likely to behave under certain conditions. It does so by analyzing large datasets and identifying patterns (that may not be immediately apparent to humans) and use them to better predict future demand. These models continuously learn and adapt, improving their accuracy over time.
Predictive analytics can reduce the manual effort required to analyze more than one market variable at a time, by honing in on “fine-grain” demand variables like seasonality, location, competition, and economic factors. Then, forecasts have the potential to capture the patterns that influence demand closer to the level at which that demand must be met, especially during seasonal or promotional events.
With more accurate predictions, you can avoid overstock and stockouts. This, in turn, minimizes the costs associated with excess inventory and lost sales.
A Platform for the Future
Most organizations around the world are still struggling to move data between legacy data systems that don’t support real-time updates and integrations with data analysis platforms. Legacy data systems and warehouses are batch oriented, meaning they bring data in on a scheduled basis, and then they must do additional processing before that data is available. In a warehouse, data needs to be extracted and analyzed elsewhere, or you’re greatly limited in what can be analyzed.
When running historical demand forecasts using a data warehouse, the categories and depth of forecasts needed to be limited because optimal granular forecasts would take days or weeks. Data warehouses weren’t built for today’s data
There’s a litany of data out there, unstructured, semi structured, fully structured, everything in between. And you’ll need a platform to consolidate all that and transact that data in real time. Unstructured data is a problem for 95 percent of businesses and prevents them from making more informed decisions across their operations.
To meet that need, companies started implementing separate systems for tapping into unstructured and third-party data, such as weather forecasts. But these systems were expensive and required integration with the data warehouses, adding costs and complexity.
If everyone could all see the same information, all on one platform, decision-making would be light years faster, it would be cheaper and it would be simpler.
How do you leverage data to make inventory and sales forecasting more accurate, whether it’s your own sales data, or external data? It’s where data science, AI and powerful modern data estates come in. An Azure-based modern data estate built on a foundation like Databricks is an ideal place for doing so because it takes care of the entire data lifecycle from ingest to insight, so data experts can focus on the analysis that truly matters. In addition, Databricks provides a ‘marketplace’ functionality that makes additional third-party data available in a way that is secure and compliant.
In a recent Hitachi Solutions webinar, data experts from Hitachi Solutions and Databricks shared some interesting ways for using Azure Databricks to leverage your data quickly and cheaply with a single, integrated platform for internal and external data. Check it out.
Hitachi Solutions won the Databricks 2023 Disruptor Partner of the Year Award for our work with our Empower Data Platform this year. Empower is a single platform that rapidly centralizes data sources in a matter of days onto a cloud platform. Our research to make this possible for customers is the reason Hitachi Solutions won the Databricks award.
Empower leverages the Azure Databricks Platform for its strength in data science and machine learning applications and its lakehouse design to give end users richer information faster and at less cost. We can bring together the data you require to build your forecasts, generate those forecasts efficiently and expediently, and then make that data available to a wide range of analytics and operational applications. And it’s the coordination of the resources, the distribution of the work, and the persistence of data for analysis that Databricks helps you manage.
Interested in a Test Run?
We view data science and machine learning as business tools—more than an algorithm or piece of technology. Our team can provide a path for your better demand planning and inventory management ambitions while taking the mystery out of how it is accomplished. You bring the data, we bring the expertise, practice methodology, and technology skills to build your data-driven future.
Check out our Advanced Analytics Data Lab. In just four weeks, you’ll be leveraging the power of a collaborative workspace that enables your analysts and data scientists to ingest, mold, model, and visualize data solutions big and small.