5 steps to create agile consumer goods supply chains

International conflicts, labor shortages, natural disasters, and now the challenge of wildly fluctuating trade tariffs around the world. Unfortunately, there are many factors that can disrupt a consumer packaged goods (CPG) supply chain.

But what if CPG companies could navigate supply chain disruptions so well, customers never had to worry about products being out of stock? With the power of artificial intelligence (AI) and other emerging technologies, it’s possible to turn this dream into a reality.

Businesses are investing in new technologies to improve ecosystem collaboration, manage distributed networks, and increase flexibility. In fact, 88% of CPG supply chain executives agree generative AI will provide a competitive advantage, according to HFS research in partnership with Genpact.

With this in mind, here are five steps CPG companies can take toward more adaptive and profitable supply chains.

Many CPG companies rely on legacy supply chain management systems that don’t support cloud technologies, data analysis, and interoperability. Upgrading these platforms can be very costly and take several years. However, it’s necessary for a foundation of high quality, real-time data for AI to provide valuable and trustworthy insights.

However, companies can’t wait to work with the latest applications to progress in their AI journey.

One global consumer goods company wanted to enhance its tech stack to gain better supply chain visibility and improve decision-making. While broader modernization efforts were underway, it introduced AI to go from limited insight to predicting risks up to 24 months in advance, reducing the time spent on operational planning. Once the platforms are modernized, the benefits will be even greater.

In parallel, CPG organizations should work to connect siloed data platforms. With a seamless flow of information from every supplier, manufacturer, logistic provider, warehouse, retailer, and consumer, companies can make informed decisions. This is especially important when tariffs and other costs impacting the supply chain can change overnight.

Here’s how to speed up these integration efforts:

• Conduct a data readiness assessment: Determine where to focus your efforts by assessing the availability, completeness, and quality of key data objects
• Create policies for data governance: Develop standard protocols for how data will be governed in different regions and markets to ensure best practices are followed consistently
• Automate data cleansing efforts: Save time and reduce errors with automations that check if policies are being followed and alert stakeholders when issues arise
• Develop a data-lake: Consolidate all the data your supply chain needs to run effectively so it’s ready for use by different supply chain applications

With this approach, companies come away with two major advantages. The first is a foundation of accurate data that supports advanced analytics for smarter supply chain decisions. When one multinational CPG company moved from multiple ERP systems to a global supply chain visibility solution, it saw a 30% increase in productivity and $20 million in cost savings thanks to inventory reduction and improved expiry management.

The second advantage of bridging data gaps is the ability to scale: when supply chains are working well, they can support other areas of the business to create more opportunities for growth.

When assessing AI use cases, it’s important to get support from operational, technological, and AI experts to consider factors like value to the business, feasibility, cost, and ROI. Some of the typical use cases in supply chain include demand forecasting, inventory optimization, supply chain visibility, and trade and promotion optimization.

One major company with 225 production centers worldwide, including third-party sites, chose to focus its AI efforts on decreasing inventory levels, reducing losses due to obsolescence, and improving supply chain visibility. They improved forecast accuracy by 50% and reduced inventory and obsolescence by 25% and 30% respectively — all in a single year.

Smaller companies are often “AI-first” — they can build systems that incorporate gen AI from the ground up, and scale more easily as a result. But large businesses are a different story. Well-established CPG enterprises have a wider range of operations with more complexity, so leaders need to be strategic about how and where they start their AI journey with a focus on speed to value.

It’s critical for companies to create a safe environment for employees. If companies focus on “productivity” as the main driver for AI initiatives, employees may feel that their job might be at risk. If employees feel threatened by AI initiatives, this hinders the adoption of new technology.

Instead, CPG companies should focus on growth, performance, or service improvements and the possibility to deliver sophisticated and personalized services as key reasons to adopt AI in supply chain.

Although AI is a powerful tool, it’s important to consider the human at the center. That’s why upskilling employees to work with AI is so important. Training employees will not only empower them to build and maintain AI applications, but also to provide functional specifications for new AI use cases, use the technology, and validate outputs to finetune the models.

The steps outlined so far require considerable time, resources, and preparation to be successful, but they don’t need to be overwhelming. Start by thinking about what’s feasible based on your organization’s maturity, operating model, and overall change readiness. Even slight improvements can provide benefits that will fund more implementations of gen AI and other technologies.

It’s also important to remember that you don’t have to rely solely on internal resources. A transformation partner with both AI expertise and a strong understanding of CPG supply chains can accelerate your progress and help you achieve agile supply chains much faster.

Supply chains that can adapt seamlessly to demand and supply disruption aren’t a distant goal — they’re a natural progression to remain competitive in the CPG industry. AI will become increasingly valuable for building sophisticated supply chains and satisfying consumers’ needs. Committing to digital transformation initiatives, experimenting with AI, and continuously learning will help CPG companies reap the benefits of new technologies and achieve competitive advantage.

A version of this article originally appeared on Supply & Demand Chain Executive authored by Ronald Liono, Kirk Niehaus, and Antonio Lopez from Genpact