Remember the military computer Joshua from the 1983 Matthew Broderick movie WarGames? Joshua learned how to “play a game” by competing against other computers, got confused about reality, and nearly started WWIII.
As depicted in that movie, Joshua isn’t all that different from Google’s DeepMind, which became a superhuman chess master in only four hours. DeepMind represents one of many astonishing feats of contemporary artificial intelligence (AI) lore. In the midst of this wave of AI hype, it’s little wonder a recent Gartner supply chain report ranked AI as the top theme to watch going into the upcoming Gartner Supply Chain Conference in Arizona.
Analytics always has been core to manufacturing and supply chain decision making. So now that we have AI, can we just magically codify our process and hand them over to AI computers to find success?
Magic vs monetization
AI can feel a bit magical if you don't understand its underlying principals. Demystifying AI from magic to monitization is a critical step before working to embed AI into your manufacturing operations.
To move from magic to monetization, you must embrace several principals:
Principle 1: Today's AI techniques do not leverage explicit coding.
Let's explore the implications for Google Deepmind's chess master example:
- AI understands the rules of the game. AI explicitly understands all legal chess moves and how the game is won.
- AI learns from experience. When Google DeepMind "learns" how to play chess in four hours, it learns by playing against other computers just like Joshua.
How do these points apply to the realities of your unique manufacturing operations? Principle 2 gives us the answer.
Principle 2: Focus on areas with clear rules, objectives, and large data sets to train the AI.
There are a multitude of AI concepts and analytic algorithms to explore. But targeting AI use cases that are better wired for AI success should guide your thinking when getting started.
Principle 3: AI manufacturing opportunities are massive but so are their data hurdles.
The wisdom of AI will always be limited by the knowledge captured in the training data sets. If you do not know your data or cannot envision the possibility of data patterns, then you may not be ready for AI monetization.
For deeper insight on Principal 3, McKinsey recently published, "Notes from the AI Frontier: Insights from Hundreds of Use Cases." Here is a summary of findings for Manufacturing Executives:
- "AI's potential impact is greatest in marketing and sales ($3.3-$6.0 Trillion) and supply-chain management and manufacturing ($3.6-$5.6 Trillion)" (pg 20 Exhibit 11).
- "Data requirements for deep learning are substantially greater than for other analytics, in terms of both volume and variety" (pg 12).
- "Limitations include the need for massive data sets, difficulties in explaining results, generalizing learning, and potential bias in data and algorithms" (pg 26).
How about a nice game of chess?
Keep these three principals top of mind as you survey your operations and envision the chess pieces at play for your organization.
A great way to explore your organization's possibilities is through networking with industry peers at events such as:
- IndustryWeek Manufacturing and Technology Show this week in Raleigh, NC. Look for SAS in multiple speaking sessions and booth 631.
- Gartner Supply Chain Conference on May 14th-17th in Phoenix, AZ. Look for SAS on stage Tuesday evening and in the Solution Showcase Booth 210.
Visit SAS at these events to hear how we help companies take steps to introduce AI into their manufacturing operations. And for further AI reading, download our Artificial Intelligence for Executives White Paper.