When reading all the latest business journals and even Supply Chain Management Now, you likely have heard about the wonders of artificial intelligence (AI). Like an infomercial, experts quoted in articles tout the cutting-edge solutions and business-transforming properties of AI. However, in a June 7 article in Harvard Business Review, Nick Harrison and Deborah O’Neill warn against adopting AI if you haven’t yet mastered analytics.
“Companies that rush into sophisticated [AI] before reaching a critical mass of automated processes and structured analytics can end up paralyzed,” Harrison and O’Neill write. “They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters and open-source toolkits without programmers to write code for them.”
However, companies that have strong basic analytics can utilize and maximize AI. The authors use the example of one telecommunications company that, enabled by machine learning, can now predict 75 times more accurately when customers are about to cancel their service.
Harrison and O’Neill share some steps companies need to take prior to AI implementation. First, managers need to examine if the company’s current automated processes are concentrated in problem areas that cost the company money and hinder operations. “Companies need to automate repetitive processes involving substantial amounts of data — especially in areas where intelligence from analytics or speed would be an advantage,” they write. “Without automating such data feeds first, companies will discover their new AI systems are reaching the wrong conclusions because they are analyzing out-of-date data.”
Next, managers need to cultivate structured analytics and centralized data processes in order to support a single and standardized system. The authors propose that, with this information, managers can better understand their customers and what drives their decisions. Then, professionals can more closely collaborate with suppliers to manufacture and offer products customers want.
Once a company is ready, integrating structured analytics with AI empowers companies to accurately predict, describe and recommend customer behavior. “Artificial intelligence systems make a huge difference when unstructured data such as social media, call center notes, images or open-ended surveys are also required to reach a judgment,” Harrison and O’Neill write. They praise robust AI systems for their ability to provide accurate forecasts.
We are only beginning to know what benefits AI and its related technologies bring to enhance and improve business. “But one thing is already clear: [companies] must invest time and money to be prepared with sufficiently automated and structured data analytics in order to take full advantage of the new technologies,” the authors advise.
Supply chain implications
To fully exploit the promise of analytics and AI, companies and professionals need to understand the benefits of automation, which is defined by the APICS Dictionary as “The substitution of machine work for human physical and mental work, or the use of machines for work not otherwise able to be accomplished, entailing a less continuous interaction with humans than previous equipment used for similar tasks.”
APICS offers a variety of resources to help professionals understand and use the latest resources available. For example, consider attending APICS 2017, which will take place October 15-17 in San Antonio. Among the more than 65 educational sessions is “Forecasting Center of Excellence — Taking an Analytical Approach in Demand Planning.” This informative session will be presented by Fazlur Rahman from The Kraft Heinz Company. You can find out more about the content that will be offered at APICS 2017 and reserve your spot today at apics.org/conference.
“Companies that rush into sophisticated [AI] before reaching a critical mass of automated processes and structured analytics can end up paralyzed,” Harrison and O’Neill write. “They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters and open-source toolkits without programmers to write code for them.”
However, companies that have strong basic analytics can utilize and maximize AI. The authors use the example of one telecommunications company that, enabled by machine learning, can now predict 75 times more accurately when customers are about to cancel their service.
Harrison and O’Neill share some steps companies need to take prior to AI implementation. First, managers need to examine if the company’s current automated processes are concentrated in problem areas that cost the company money and hinder operations. “Companies need to automate repetitive processes involving substantial amounts of data — especially in areas where intelligence from analytics or speed would be an advantage,” they write. “Without automating such data feeds first, companies will discover their new AI systems are reaching the wrong conclusions because they are analyzing out-of-date data.”
Next, managers need to cultivate structured analytics and centralized data processes in order to support a single and standardized system. The authors propose that, with this information, managers can better understand their customers and what drives their decisions. Then, professionals can more closely collaborate with suppliers to manufacture and offer products customers want.
Once a company is ready, integrating structured analytics with AI empowers companies to accurately predict, describe and recommend customer behavior. “Artificial intelligence systems make a huge difference when unstructured data such as social media, call center notes, images or open-ended surveys are also required to reach a judgment,” Harrison and O’Neill write. They praise robust AI systems for their ability to provide accurate forecasts.
We are only beginning to know what benefits AI and its related technologies bring to enhance and improve business. “But one thing is already clear: [companies] must invest time and money to be prepared with sufficiently automated and structured data analytics in order to take full advantage of the new technologies,” the authors advise.
Supply chain implications
To fully exploit the promise of analytics and AI, companies and professionals need to understand the benefits of automation, which is defined by the APICS Dictionary as “The substitution of machine work for human physical and mental work, or the use of machines for work not otherwise able to be accomplished, entailing a less continuous interaction with humans than previous equipment used for similar tasks.”
APICS offers a variety of resources to help professionals understand and use the latest resources available. For example, consider attending APICS 2017, which will take place October 15-17 in San Antonio. Among the more than 65 educational sessions is “Forecasting Center of Excellence — Taking an Analytical Approach in Demand Planning.” This informative session will be presented by Fazlur Rahman from The Kraft Heinz Company. You can find out more about the content that will be offered at APICS 2017 and reserve your spot today at apics.org/conference.
