Depending on the sports you follow, the long game could mean different things. Lisabeth Saunders Medlock (@LisabethMedlock), clinical psychologist, explains, “It means throwing the football down the field, AKA the passing game, or driving the golf ball toward the green.”[1] In the business world, however, the long game has a different meaning. Medlock explains, “The Urban Dictionary says the long game is having a long term plan, long term goals, or doing things now that set you up for the future.” Every business needs to learn how to play the long game. Nigel Duckworth (@nigelduckworth), a marketing strategist at One Network Enterprises, notes it can be difficult for manufacturers to concentrate on the long game with a swirl of new technologies constantly vying for attention. He explains, “Besieged by bright shiny objects of Industry 4.0, manufacturers should focus on long term goals instead of being swept up by the abundance of new technologies, such as cloud, big data, IoT, mobile, location, additive manufacturing, edge computing, miniaturization, augmented and virtual reality, artificial intelligence (AI), automation, and robotics.”[2] Medlock puts it this way, “With too many choices and too much information, everything seems so darn important. It becomes difficult to set priorities or to allot our time and energy. We get caught up in the short game.”

The short and long Industry 4.0 games

Neither Medlock nor Duckworth are arguing the short game should be ignored in favor of the long game. They are simply worried the short game is drawing so much attention the long game is being ignored. Katie Vazeos, a marketing strategist with Fronetics, reports a Deloitte study released earlier this year identified the top three priorities for manufacturing companies in 2019.[3] These priorities are a mixture of short- and long-term objectives. They are:

• Attracting and retaining talent. This priority is a short-term concern with long game implications. “Not since the end of the Great Depression has the manufacturing industry seen a talent shortage like we’re experiencing now,” Vazeos writes. “Job openings have been growing at double-digit rates since mid-2017, and are nearing the historical peak recorded in 2001.”
• Seeking new markets for products and services. This priority also involves both short- and long-term goals. Vazeos explains, “Manufacturing companies are trying to think outside the box when it comes to expanding markets. Sixty-four percent of the manufacturers surveyed are looking for increased market share in their existing markets. How do they expect this to happen? Through innovation.”
• Going Digital. Most analysts agree organizations born in the industrial age need to transform into digital enterprises if they are going to succeed in the digital age. According to Vazeos, “Manufacturers have spent the past few years making digital advancements, from the factory floor and operational functions to content and digital marketing. This year will solidify the digital movement in manufacturing.”

All the priorities identified by Deloitte are being driven by Industry 4.0. Duckworth explains, “The next seismic shift in manufacturing and development is the Industrial Revolution 4.0 (2010-?), which is driven by a multitude of technologies, but in general, is characterized by a marriage between cyber-physical systems. New technologies like artificial intelligence (AI), robots, and the Internet of Things (IoT) interact to create smart factories, devices, and self-regulating systems. According to Deloitte’s Forces of Change, ‘Industry 4.0 … marries advanced manufacturing techniques with the Internet of Things to create a digital manufacturing enterprise that is not only interconnected, but communicates, analyzes, and uses information to drive further intelligent action back in the physical world.’ Additive manufacturing, robotics, and automation all have a direct impact on the manufacturing process and are a key force behind Industry 4.0. The benefits of these technologies in manufacturing are undeniable.”

Cognitive technologies and Industry 4.0

The lifeblood of the digital age is data. So much data now courses through modern manufacturing processes it can only be analyzed using cognitive technologies (i.e., artificial intelligence). Antony Bourne (@AntonyBourne), president of IFS Industries, believes cognitive technologies will be essential for manufacturing success in the years ahead.[4] He’s so convinced of his position, he makes three predictions about the future of AI in manufacturing:

Prediction #1: “50% of all manufacturing companies will be using AI in some form by the end of 2021. … AI realism will spread in the coming years because of its ability to interpret the massive amounts of data that IoT devices provide, allowing more accurate forecasting to take place and better predictive maintenance routines. A big stumbling block for AI has always been the term ‘AI’ itself. It misleads many manufacturers, suggesting a large end-to-end system. AI is already used to support enterprise-wide business decisions in what-if planning scenarios. In reality, ‘AI’ is more often a collection of targeted technologies, from natural language processing to vision identification to chatbots to analytics to automation — each with its own strengths and applications. What they all share is the intelligence factor: a high degree of accuracy and an incredibly fast ability to learn from their mistakes.”

Prediction #2: “25% of manufacturing planners will be talking to their systems by the end of 2020. This percentage was based on two factors: the rate of adoption of digital transformation in general within business and the advantages that robots give (no lunch breaks, lights-out manufacturing, etc.). AI solutions are smarter and more eloquent than most of us realize.”

Prediction #3: Pick-and-place robots will put away 25% of manufactured goods by the end of 2020. Robots on the production line have been essential for decades. But what kind of savings will AI-enabled robots deliver in the warehouse? With the adoption of digital transformation and the advantages that robots offer, they’re likely to trim costs and give companies a competitive edge.”

Obviously, those are short-term predictions with long game implications. Michael Schuldenfrei (@MSchuldenfrei), a Technology Fellow at OptimalPlus, asserts, “The biggest [Industry 4.0] challenge [is] extracting value from manufacturing big data.”[5] He notes, “Industry 4.0 big data comes from many and diverse sources.” He lists a few of those sources:

• Product and/or machine design data such as threshold specifications
• Machine-operation data from control systems
• Product- and process-quality data
• Records of manual operations carried out by staff
• Manufacturing execution systems
• Information on manufacturing and operational costs
• Fault-detection and other system-monitoring deployments
• Logistics information including third-party logistics
• Customer information on product usage, feedback, and more

Schuldenfrei notes, “Some of these data sources are structured (such as sensor signals), some are semi-structured (such as records of manual operations), and some are completely unstructured (such as image files). In all cases, however, most of the data is either unused or used only for very specific, tactical purposes.” A good cognitive platform can deal with all those data sources and generate actionable insights that can be beneficial across the enterprise.

Concluding thoughts

“Ultimately,” Duckworth concludes, “Industry 4.0 is about a constellation of technologies that perform optimally when interconnected and intelligently coordinated across the parties and across the cyber-physical divide. It is this communication and collaboration that drive much of the business value that Industry 4.0 offers. When better connected, manufacturers are better positioned to leverage the power of Industry 4.0, within the factory through 3D printing, robotics, and IoT, and they’re better positioned to align objectives and resources across the value chain to deliver the most value to the end customer at the lowest cost.” In other words, they are better positioned to play the long game.

Footnotes
[1] Lisabeth Saunders Medlock, “We All Lose If We Don’t Learn To Play The Long Game,” Life the Blog, 3 October 2016.
[2] Nigel Duckworth, “Industry 4.0: Why Manufacturers Need to Keep Their Eye on the Long Game,” Manufacturing.net, 6 June 2019.
[3] Katie Vazeos, “The Top Three Priorities for Manufacturing Companies in 2019,” Supply Chain Brain, 24 January 2019.
[4] Antony Bourne, “What’s Ahead for Manufacturing AI,” IndustryWeek, 10 May 2019.
[5] Michael Schuldenfrei, “Big Data Challenges of Industry 4.0,” Datanami, 25 April 2019.