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In the early 1960s, MIT professor Jay Forrester invented the Beer Game to teach people about all the parts of a supply chain, and how easily it can go wrong. The game board consists of the four sectors of a linear beer distribution chain—retailer, wholesaler, distributor, and factory. Players initially work with a consistent quantity of beer, but soon must contend with a variable quantity and figure out how to forecast demand. The catch is that players can’t communicate with each other. Orders and shipments going back is the only signal they get. Educational chaos ensues. 

Real-life supply chain chaos recently played out when the container ship Ever Given was grounded in the Suez Canal, blocking one of the world’s most heavily trafficked trade routes, and showing how only a week of supply-chain disruption has global consequences. But there’s a crucial difference between the siloed, go-it-alone players in the Beer Game and ship captains backed up in the Suez. The most advanced companies, the ones most able to pivot during adversity, employed artificial intelligence (AI) and machine learning (ML) in their supply chain logistics.

Using Tableau on Amazon Web Services (AWS), it’s easy to activate an AI- or ML-powered recommendation engine or model. Tableau brings the brains, while AWS handles the data to give companies the ability to accurately see into the future during chaotic times.

So what does that mean to a company whose ship captain is staring at the stern of stuck-in-the-mud Ever Given and struggling to get widgets (or beer) to a destination on time? They have a dashboard on which they can run as many scenarios as they need around a decrease in suppliers, changes in delivery times and pricing, and any other variable they want. This dashboard, based on the machine-learning AWS model, lets these companies respond with confidence to all those potentialities. Calculations that would’ve taken months in the recent past, if they were possible at all, are produced in seconds.

Never has AI and ML played such an important role as it does now, simply because they enable companies to ask questions and trust the answers.

Think of a supply chain crisis like a fire in your home. You smell the smoke and know there’s a disaster taking place somewhere in the house, but you don’t know what’s on fire, where it’s located, or what caused it. Is it lethal? How long will it last? And where’s the cat? Now, backed with loads of data processed with AI, all of these questions about risk and potential outcomes can be accurately predicted.

In a supply chain “fire,” you can unpack the series of events that preceded an incident. The combined power of machine learning, AWS data hosting, and Tableau’s intuitive analytics interfaces gives users an unprecedented ability to handle adverse circumstances. 

Building a highly visible, resilient, and data-based supply chain is easier said than done. Many companies have been running Tableau on legacy, on-premises data centers and are hesitant to rock the boat with a move to the cloud. But that move will open up a whole new set of tools—tools their competition is definitely looking at.

At Slalom, we take into account not just the technology platform side of data and analytics, but also your people, the processes you put in place, and the workflow that produces insights.

Through our Modern Cloud Analytics (MCA) practice with Tableau and AWS, Slalom helps clients migrate their Tableau Servers workloads to AWS. MCA combines the resources, technical expertise, and data knowledge of the three companies to assist in your end-to-end analytics journey. This trilateral company approach, encompassing strategy, migration, and operations, ensures you securely deploy and scale Tableau on the cloud with no compromise in data integrity, governance, or security.

So, whether you’re trying to get beer from the brewery to convenience stores, or taking a detour around the Horn of Africa, Slalom’s multidimensional data and analytics Navigator methodology can put the power of AI/ML behind your Tableau workflows on AWS.