Part 01 of this series unpacked the value of analytics and artificial intelligence (AI) in transforming your business and five of the nine critical drivers that can shape this success. Here, in part 02, we look at the final four critical drivers that will help embed resilience, intelligence, and sustainability into your business and unpack four reasons why no business can afford to be without AI and analytics in today’s manufacturing.
Driver 06: Cross-functional, collaborative, and agile teams
A collaborative culture is far more likely to foster innovation and drive AI and analytics initiatives throughout the organization. You can benefit immensely from teams made up of highly committed business representatives, analytics translators, user-experience design experts, data engineers, and data scientists who are encouraged to collaborate often and to create agile spaces that allow for inspiration and innovation.
Driver 07: Prioritize decision-making processes
It’s worth your digital while to prioritize and map AI-assisted decisions that drive the most value using real-time data insights. This means using the data and the insights effectively and taking advantage of their speed and accuracy to refine how you approach decision-making across multiple points in the business. This includes operations, worker safety, permit to work, hazardous environments, emergencies, and facility optimization.
Driver 08: Establish clear decision-making rights and accountability
There is immense value in knowing the Who in the organization. This is the person, or the people, who are empowered to make analytics-based decisions on a day-to-day basis, and it includes the holding business unit leaders who are accountable for ensuring that their team members have the tools they need to achieve their KPIs, goals, and mandates. Visibility and transparency into the Who allows for decisions to be made effectively at the working team level and ensures a clear process for escalation in the organization.
Driver 09: Empower the front lines to make analytics-driven decisions
Finally, it is essential to realize that each of these components is interconnected and relevant to the other. Getting management on board is only one part of the battle. If you want your analytics working for you and want your insights to turn into outcomes, you must enable your frontline employees to leverage analytics efficiently to empower their decision-making. If you put this intelligence at every point in the business, you are creating a fabric of transparent insights relevant to everyone and every part of your business.
This is why you need to pay attention to analytics and AI
There are four reasons why your manufacturing organization can benefit from analytics and AI:
- Improved dynamic risk management using AI-driven predictive and prescriptive analytics with real-time data collection that delivers dynamic safety routines. Leveraging AI and machine learning to identify and monitor predictive and prescriptive metrics, you can create a historical and current picture of your organization to enhance decision-making and drive continuous process improvement.
- Actionable insights that ensure management and facilities are adaptable to changing conditions and enhanced worker and asset safety and performance. Using these insights and the data, you can gain personalized and visual business reports that offer insights into every role in the company and engage and empower all workers within the company.
- Accessible benchmarking across business units, facilities, and regions to allow for streamlined optimization and ongoing audits that offer up comprehensive insights into company-wide performance. Alongside benchmarking, auditing and reporting, AI and analytics offer intense visibility into workflows so that critical assets are always operational and workers are always allocated appropriately.
- Emergency preparedness that spans incident management, incident investigation, risk assessment, root cause analysis and effective corrective actions, and accurate documentation. Leveraging insights, data, and analytics, you can converge big data in real-time with a dynamic view of emergency operations, and you can use best practices, lessons learned, and insights to transform emergency preparedness.