Boost customer relationships, end-to-end visibility, and operating margins
Supply chain disruptions are here to stay. But advanced technologies can be strong allies that help you navigate these challenges, from tariff changes and spikes in raw material pricing and logistics costs to evolving consumer buying behaviors.
But for that to happen, you'll need to break out of the traditional forecasting methods and integrate AI and machine learning (ML) into your forecasting processes. So, how can you do this?
We outline seven practical ways that can guide you through transforming your demand forecasting strategies using AI/ML.
Read our whitepaper produced in collaboration with Industry Week
Download NowBy adopting these strategic guidelines, you'll be equipped to:
Identify potential disruptions and impacts
Make smarter, faster decisions with AI-driven analytics
Enable frictionless operations through streamlined exchange of insights across departments
Want to see how demand forecasting works in practice? There's more on the future of demand forecasting in the paper.
Penske makes spare parts planning simple
Penske Transportation Solutions is an industry leader in transportation and logistics. The company wanted to strengthen its customer commitment to 100% vehicle uptime by having its parts inventory in the right place, at the right price, at the right time. But with 700+ locations servicing nearly 430,000 trucks and many different makes and models, this was a tough inventory planning challenge for the company.
To solve the problem, it partnered with Genpact to customize a machine learning model to predict demand and maintenance repair and synchronize with parts inventory. Utilizing the power of artificial neural networks, Genpact created the ability to optimize spare parts inventory based on fleet age, vendor capabilities, and location capacity.
Today, the AI solution helps Penske Transportation Solutions deliver on its commitment to customers. So far, the organization has improved forecast accuracy by 15%, cut inventory levels by 10%, and delivered better insights on supplier performance. It also helped the company better manage part shortages and stock-out situations.
In our recent study with HFS, 53% of supply chain and procurement executives stated that they are shifting funds from other resources to fund gen AI initiatives. The number indicates that companies are willing to cautiously experiment with new technologies to enhance the capabilities of traditional AI systems, aiming for more predictive and autonomous supply chain operations.
Are you ready to raise your AI game, too?
