Let me start by posing a question:
"Are you forecasting at the edge to anticipate what consumers want or need before they know it?"
Not just forecasting based on past demand behavior, but using real-time information as it is streaming in from connected devices on the Internet of Things (IoT). Or, are you transmitting sensor data to a cloud, or other data repository for later use?
Edge analytics is gaining attention as the IoT has become more widespread, streaming data from manufacturing machines, on-line purchases, mobile and other remote devices. The concept of “edge analytics,” also known as distributed analytics, essentially means analyzing data at the point where the data is collected.
Using analytic algorithms as data is generated, at the edge of the corporate network, companies can set constraints to determine what information is worth sending to the cloud, to a demand signal repository, or other data repositories for later use. Companies can process data continuously, on the move, in-memory with very high speed and low latency to sense demand, understand what's influencing demand, and act to anticipate future demand. Thus, enhancing the customer/consumer experience while ensuring supply efficiencies at the store and/or mobile devise purchase point.
The key benefit of edge analytics is the ability to analyze data as it is generated, which decreases latency in the decision-making process as the data is collected. For example, if sensor data from mobile devices point to product purchases of a particular product trending upward, business rules built into the algorithms interpreting the data at the network edge can automatically alert manufacturers to increase production of that product to meet demand. Using event stream processing combined with artificial intelligence and machine learning, that information can save time and lower costs compared with transmitting the data to a central location for processing and analysis, potentially enabling companies to minimize or eliminate back orders.
Rather than designing consolidated data systems where all the data is sent back to an enterprise data warehouse (or data lake) in a raw state, where it has to be cleaned and analyzed before being of any value, why not do everything at the edge of the system, including demand forecasting using advanced algorithms or machine learning. Understanding and filtering out the noise from the useful information, and shrinking down to the individual device (or node), retailers can anticipate future demand to eliminate on-shelf out-of-stocks and optimize store-level promotions. Combining nodes across the store (and/or mobile devices) can help identify increases in demand as a result of sales promotions, thus improving promotion effectiveness, and driving inventory policy.
Another key benefit of forecasting at the edge is scalability. Pushing analytics algorithms to sensors and network devices alleviates the processing strain on enterprise data and analytics systems. Instead of demand planning, we can accomplish real time demand execution across the supply chain. This offers the first step toward the autonomous supply chain.
Forecasting at the edge will allow companies to apply advanced time series analytics across devices to enhance the customer journey. This impacts not just how retailers market to the consumer, but also how they drive the ideal product assortment within each store, channel or location — including online. Also, the ability to make decisions as to where the optimal inventory should be held — locally versus regional distribution centers or customer warehouses closer to the customer ordering point. Providing the analytics and insights to make those decisions end-to-end across the supply chain. Companies can use these insights to improve the effectiveness of marketing campaigns, product assortment and merchandising decisions, distribution, and operations across all channels of business using “predictive analytics.”
Today, the technology exists to support forecasting at the edge. There are no longer any data or technology hurdles to overcome. Only our own inhibitions and the desire to take the leap toward achieving the end goal of an autonomous supply chain are stopping us.
The multitude of forces affecting the relationship between demand and supply are set to expand their influence. Smart leaders will take advantage of the flood of digital data to better understand those forces to make more accurate and predictive supply chain decisions.
So, ask yourself:
Are you stuck in a vicious cycle of planning demand, using 2-4 week old data, or are you conducting real time demand execution anticipating demand at the edge?
For more information regarding how the next generation demand management will change the way demand planning will be done in the future read this free white paper.