Nearly every manufacturer today relies on a blend of technology and systems to improve outcomes of decisions. As the trend toward greater use of technology continues, a new way of thinking about how to manage manufacturing environments is emerging, one that focuses entirely on making decisions with better outcomes. Gartner calls it decision intelligence. Although obscure until very recently, this emerging field is spreading across all industries. As machine learning and artificial intelligence grow smarter, organizations investigate how these technologies can optimize their business decisions and practices.
And as obvious as it seems in theory, manufacturers are just now leaning into really using it.
Decision complexity is increasing
We, as humans, rely on our own skills, experience, gut instinct, logic, and emotions to evaluate what action needs to be taken. The average person makes 35,000 decisions in a day, and none of these decisions can stand alone. In recent years, organizational decision complexity has heightened due to the increased pace and dynamics of production, high regulation for risk management, and expansion of human and machine collaboration. It’s no wonder that a 2019 Mckinsey survey found that a typical Fortune 500 company could waste more than 500,000 days of managers’ time a year on ineffective decision making. The need for rapid, data-driven decision making has led many organizations to look to decision intelligence and the platforms that enable it.
How, then, does this key strategy come into play for manufacturers? Manufacturers face a growing demand to take into account all decision makers, from customers to employees to suppliers to stakeholders. Gathering and utilizing meaningful data and insights across organizational boundaries generates a level of complexity that traditional decision making can’t address.
Embracing technologies that enable decision intelligence may (and almost certainly will) exponentially upgrade the decision making process, especially when it comes to improving manufacturing processes.
This category of solutions provides a framework for linking comprehensive data with decisions and their outcomes. Historically, decision intelligence developed with decision modeling as a foundation for engineering or reengineering the decision process, using decision theory and methods like decision mapping. Most significantly today, decision intelligence brings forward best practices for employing machine learning and artificial intelligence to improve the outcomes of business decisions.
Improvement does not happen overnight. A good framing of a decision intelligence program is a three-step maturity model. In early stage implementations, machines support human decisions. Some implementations can then move to machines augmenting the decision process. Still other implementations can extend machine involvement to automate decision making. While there are plenty of decisions that will always need a human touch, many manufacturing decision processes can move along the maturity model, moving from machines providing decision support, to decision augmentation, and finally to decision automation.
A maturity model for decision intelligence
1. Machine-supported decisions
2. Machine-augmented decisions
3. Automated decision making
Some of the largest companies in the world have adopted decision intelligence practices, with a predicted 33% of large companies employing decision intelligence this year. The process is a gradual one, in which organizations improve by adding capabilities incrementally. A manufacturing organization can follow the maturity model of decision intelligence. Unified data, from enhanced connectivity and data filtering, situates manufacturers to get started with data-driven, machine-supported decisions. As the technology proves its trust, augmentation and automation become options for leveling-up decision making processes.
To keep pace with advancements in manufacturing technology, decisions must be accurate, prompt, and responsive. Adopting decision intelligence and data-driven practices is a key step in maximizing efficiency and reducing downtime and labor. The ArchFX Platform provides manufacturers with graduated access to machine learning and AI technologies that can target weaknesses in manufacturing process decisions. With ArchFX, manufacturers can begin to use decision intelligence to improve their operations more efficiently than ever before.