The digital supply chain may make traditional demand forecasting a thing of the past.
In the latest step to enhance this digital supply chain, SAP and Accenture announced they plan to "coinnovate" on SAP S/4HANA-based extended planning applications for manufacturers.
The applications integrate demand-driven MRP (material requirements planning) capabilities into S/4HANA's MRP functions, according to SAP. This can help businesses create MRP simulations to support analysis and forecasting for issues like capacity planning and evaluating the effects of demand changes or supply disruptions.
"This resonates with companies from almost all industries because they want to avoid forecasting and want to get into a high degree of automation," said Hans Thalbauer, SAP's senior vice president of digital supply chain and internet of things. "This is called demand-driven MRP, and it's like the kanban approach, which has been used in manufacturing for decades, but is now taking this approach through the distribution centers to the customers. Digital signals go through the supply chain and are driving the process of the demand signals and the replenishment process."
This approach allows companies to become more efficient and introduce automation in the supply chain, which means they can move away from traditional forecasting that's not always as accurate as they need, Thalbauer said. This can be extremely valuable for industries that need to handle very short-term demand, such as those that promise next-day delivery.
The demand-driven MRP functionality is currently available as part of the SAP Integrated Business Planning component of S/4HANA.
The functionality is relatively simple to implement technically, Thalbauer said, but requires companies to change the way they think about supply chain and demand forecasting.
Hans Thalbauersenior vice president of digital supply chain and IoT at SAP
"Instead of just focusing on orders and the forecast, they now have to focus on inventory bumpers and creating the flow of the products," he said. "So, that's the difference and the mindset that needs to change."
Thalbauer said demand-driven MRP is being adopted first mainly in consumer products and high-tech industries, but the benefits can be applied to industries across the board.
"Companies are starting to introduce this DDMRP process first only for the low-volatility products, but they're finding out that it's applicable for all types of products," he said. "It's not only high volume [and] low volatility that can use this process; it can very much be used by all kinds of product segments and demand segments. That's the breakthrough in the environment where you're able to run the supply chain differently."
Accenture's part in the coinnovation process will be providing expert advice to customers on best practices, business case analysis and deployment support, according to the company.
SAP Analytics Cloud helps with March Madness challenge
Predictions about U.S. college basketball are all the rage in March, when people from all walks of life enter NCAA basketball March Madness bracket pools.
Sometimes real money is involved, and sometimes it's just bragging rights, but it seems everyone becomes part of the hoops cognoscenti. Some bracket players claim to possess inside basketball knowledge, and some just make their picks based on team colors.
SAP looks at March Madness as a way to showcase the value of predictive analytics. The company has run the SAP DataGenius Challenge for the NCAA men's basketball tournament through its SAP Analytics Cloud analytics-as-a-service offering.
"The SAP DataGenius team looks at interesting topics -- sports and otherwise -- to take predictive and machine learning capabilities and try to uncover some insight in the data," according to Nic Smith, SAP’s global vice president of product marketing for cloud analytics. "It also shows some use cases for predictive analytics that are a bit outside the norm, but relate it back to business."
Smith said SAP Analytics Cloud is available as a service and has machine learning and smart discovery capabilities, but it's designed to let anyone be able to use it.
"You don't need to be a data scientist or be able to code in R to use this," Smith said. "You're getting machine learning insights provided to you based on data that you may not have known about, so we wanted to see what it could do once we put it up against a bunch of basketball data."
For the SAP DataGenius Challenge, data was fed into SAP Analytics Cloud from a wide variety of sources, including data from the last five tournaments, NCAA statistics and Kaggle, a data science community platform, Smith explained. Then, it used smart preparation tools to clean the data and provide algorithms to join the data sets together. Once the data is prepared, users can discover, analyze and build out the story they want, which can be a winning basketball bracket or a relevant business opportunity.
This analysis involves data that indicates influencing factors in wins, such as time of ball possession, but Smith said the Analytics Cloud can also account for anomalies that happen in a game, like a coaching decision or a referee's call.
"This injection of an aspect of luck [enables] what-if scenarios that allow you to choose the path you want to take based on certain variables," Smith said.
The 2018 NCAA men's basketball tournament has been rife with upsets, making any predictions risky, but Smith said the SAP DataGenius Challenge has a good record over the years it has run.
"We're usually in around 70% to 80% correct selections," Smith said. "This year, there were a lot of closely matched teams, and we saw that in the data. So, we're a little bit below the standard 70% mark. But, last year, we selected the winning team, so you never know how it's going to go."
If you missed out on the NCAA March Madness SAP DataGenius Challenge, the next opportunity is just around the corner, as Smith said a similar challenge is planned for the 2018 FIFA World Cup, which will be held this summer in Russia.