Responding to multiple, simultaneous disruptive forces has become a daily routine for most demand planners. To effectively forecast demand, they need to be able to predict the unpredictable while accounting for diverse and sometimes competing factors, including:
- Labor and materials shortages
- Global health crises
- Shifting cross-border restrictions
- Unprecedented weather impacts
- A deepening focus on sustainability
- Rising inflation
Innovators are looking to improve demand forecast accuracy by incorporating advanced capabilities for AI and data analytics, which also speed up demand planning. According to a McKinsey survey of dozens of supply chain executives, 90% expect to overhaul planning IT within the next five years, and 80% expect to or already use AI and machine learning in planning.
From our partners:
Google Cloud and SAP have partnered to help customers navigate these challenges and supply chain disruptions starting with the upstream demand planning process, focusing on improving forecast accuracy and speed through integrated, engineered solutions. The partnership is enabling demand planners who use SAP IBP for Supply Chain in conjunction with Google Cloud services to access a growing repository of third-party contextual data for their forecasting, as well use an AI-driven methodology that streamlines workflows and improves forecast accuracy. Let’s take a closer look at these capabilities.
Unify data from SAP software with unique Google data signals
When it comes to demand forecasting and planning, the more high-quality and relevant contextual data you use, the better, because it helps you understand the influencing factors of your product sales to sense trends and react to disruptions or capitalize on market opportunities more timely and accurately.
The expanded Google Cloud and SAP partnership helps customers who use SAP® Integrated Business Planning for Supply Chain (SAP IBP for Supply Chain) bring public and commercial data sets that Google Cloud offers into their own instances of SAP IBP and include them in their demand planning models in SAP IBP. So, in addition to sales history, promotions, stakeholder inputs and customer data that are typically in SAP IBP, a demand planner can incorporate their advertising performance, online search, consumer trends, community health data, and many more data signals from Google Cloud when working through demand scenarios.
More data enables more robust and accurate planning, so Google continues to build an ecosystem of data providers and grow the number of available data sets on Google Cloud. Some current providers include the U.S. Census Bureau, the National Oceanic and Atmospheric Administration, and Google Earth, and partnerships are underway with Crux, Climate Engine, Craft, and Dun & Bradstreet to help companies identify and mitigate risk and build resilient supply chains.
Augmenting demand planning with additional external causal factor data is a starting point to drive more accurate forecasting. For example, knowing what regional events may be happening, or the weather patterns that may impact sales of your products, allows you to react faster to these changes by making sure adequate supply is being provided. The result is a more accurate overall plan that reduces resource waste and out-of-stock events. Planners can respond with more accurate and granular daily predictions about sales, pricing, sourcing, production, inventory, logistics, marketing, advertising, and more based on the expanded data.
Get more accurate forecasts with Google AI inside
Extending the already expansive algorithm selection available in SAP IBP, the release of version 2205 allows SAP IBP customers to access Google Cloud’s supply chain forecasting engine, which is built on Vertex AI — Google Cloud’s AI-as-a-platform offering — from within SAP IBP as part of their forecasting process.
The benefit of using an AI-driven engine for demand forecasting is that it meaningfully improves forecast accuracy. Most demand forecasting today is done through a manually set, rules-based model versus an AI-driven model that is smarter and gets better at predicting demand as it works.
Take the fastest path from data to value with streamlined workflows
Vertex AI can include relevant contextual data sets for demand planning, and the results can be shown in SAP IBP for planners to incorporate when building their workflows.
In addition to more accurate forecasts, planners can work faster and more efficiently as they build potential scenarios, meaning they can do more simulations than they do now so that a wider range of disruptions can be modeled. Customers of SAP IBP don’t have to do any of the heavy lifting. They just have to share their data from SAP IBP with Google, then access the process workflow capabilities to set up automated workflows that use the combined data. Google makes the data available so that planners can use it as they’re setting up their workflows in Vertex AI.
Users of the Google Supply Chain twin and SAP IBP can combine the rich planning data from IBP with additional SAP data and other Google data sources to provide better supply chain visibility. The Google Supply Chain twin is a real-time digital representation of your supply chain based on sales history, open customer orders, past and future promotions, pricing and competitor insights, consumer history signals, external data signals and Google data.
Leverage Google data signals with SAP IBP for more accurate forecasts
It’s not difficult to access these new capabilities, and the benefits are more accurate near-term forecasts and more return on your investments in SAP IBP and Google Cloud. If you happen to be at the Gartner Supply Chain Symposium from June 6-8th in Orlando, Florida, stop by our booth to say hello. Or, get started now
By: Dinesh Vandayar (Partner Manager, SAP Strategy and Architecture)
Source: Google Cloud Blog
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