Feature
Enterprise operations leaders are applying artificial intelligence (AI) to a variety of business cases across the organization.
Operations teams deploy AI to help them drive and manage the many mission-critical daily tasks that an organization must perform to create and deliver products and services. AI is allowing operations leaders to optimize business systems more efficiently by leveraging data, automation and business intelligence.
Here, we look at several examples of exactly how companies and their leaders are using AI to enhance their operations.
Supply Chain Management
Using AI in supply chain management can enhance decision-making and operational efficiency by enabling businesses to process large amounts of data in real-time, anticipate market trends, optimize logistics and perform routing and scheduling based on changing conditions.
Generative AI in particular is allowing companies to capture previously untapped benefits of AI in supply chains. GenAI overcomes the complexities of earlier AI implementations and promotes higher adoption by simplifying user interfaces, automating operations and generating actionable insights from large data sets, according to Boston Consulting Group.
Supply chain technology is continuing to grow in complexity and skilled supply chain professionals are in short supply, so companies need GenAI, which can enable human-machine collaboration, to compete successfully, the firm says.
“GenAI can embed advanced supply chain intelligence and complex tools into accessible workflows,” BCG says.
Forecasting, supply planning and other advanced analytics that “traditionally require specialized algorithms and expert knowledge can be put at users’ fingertips, democratizing usage.”
GenAI can connect disparate supply chain systems as well as enable autonomous orchestration, “coordinating activities and processes without manual intervention.”
“We see these benefits emerging as GenAI deployments advance through a stepwise evolution,” BCG says.
Demand Forecasting
The most efficient supply chain in the world will mean little if too much or too little product is being produced. There will either be waste or lost opportunities for the enterprise.
As such, machine learning (ML) and AI have been applied extensively in demand forecasting.
Companies that integrate AI into their demand forecasting can improve efficiency, achieve better accuracy and improve costs, according to the consulting firm A&M.
AI can analyze demand data much faster than humans and recognize patterns that humans might miss. AI can also ingest data and update forecasts in real-time, another benefit that is beyond the capability of humans.
However, there are challenges with using AI for demand forecasting.
The accuracy of the demand forecast is heavily dependent on the quality of the data used to train them, A&M says. Inaccurate or incomplete data can lead to inaccurate predictions, and the interpretation of output isn’t always easily understandable.
Related Article: 5 AI Case Studies in Logistics
Equipment Maintenance
Enterprises across different industries are using AI for the early detection of machine failure as part of Industry 4.0.
Industry 4.0 is based on intelligent machinery, which can provide alerts about potential failure before a machine goes down. Such alerts enable a manufacturer to make repairs during slow times and prevent machines from failing during peak periods.
For example, Volvo Trucks North America unveiled significant enhancements to its Blue Service Contract, a dealer-managed preventive maintenance solution based on AI models. The adaptive maintenance system adjusts service intervals dynamically based on factors, such as fuel consumption, idle time and oil samples.
Volvo expects this approach to be more cost-effective for truck owners than an approach based strictly on mileage or the calendar.
If a truck has seen unusually demanding conditions for a prolonged period of time, the adaptive maintenance AI model will recommend service sooner, reducing the likelihood of unplanned downtime. Other drivers may find they can wait on some services, enabling them to get multiple services at the same time.
Volvo Trucks’ integrated connectivity solutions monitor all systems on the truck, which is connected to the 24/7 Volvo Trucks Uptime Center. This allows fleet managers and their dealers to monitor and manage trucks in near real-time.
“We find that many fleets are over-maintaining their trucks, which can be costly,” said Magnus Gustafson, VP of connected services, Volvo Trucks North America.
“Applying AI to optimize maintenance intervals based on truck specs, operating conditions and actual use ensures our customers can maximize the uptime.”
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Though the concept has been around for a while, the use of AI for preventative maintenance is still in the development stages. Dataiku predicts that 52% of manufacturers will use generative AI in the next year.
Staffing Levels
Shift-based workplaces, such as health care locations, retail locations and contact centers, are using AI-based technology, or workforce automation software, to manage staffing levels.
For example, Cleveland Clinic used an AI-based solution to assist with its staffing. One feature provided a real-time display of patient census and forecast capacity at the organization’s facilities, which helped improve patient flow and resource planning.
The AI platform also improved the organization’s throughput by managing beds and allowing for more hospital transfers. It tracked operational data, such as transfer volumes and transfer time to bed assignment.
Real-time data provided more accurate volume prediction than previous staffing models, and a “staffing matrix” helped ensure staffing is based on both demand and capacity.
In health care, the ability to forecast staffing with an up-to-date view is critical operationally.
“Knowing our staffing availability days ahead of time leads to fewer last-minute changes, earlier scheduling and less manual and operational management burden,” says Meg Duffy, senior director of staffing and university outreach in nursing operations, Cleveland Clinic.
Related Article: Where’s the Generative AI ROI? Start With the Supply Chain
