How AI can help companies beat inflation and avoid shrinkflation

The domino effect of COVID-19 and the war in Ukraine has disrupted supply chains and increased costs, leaving manufacturers with three paths to survive: increase prices directly, reformulate the product with cheaper materials or downsize products in waves. 

As customers are more sensitive to increases in price or reductions in quality, many companies opted to take out some of the product without changing the price. Manufacturers refer to this practice as “cost reduction.” But consumers call it shrinkflation.

The shrinkflation backlash

Shrinkflation isn’t new, and for a long while it was considered a standard, if not necessarily ethical, way of doing business, especially during recessions. It was done incrementally – in bits and pieces – which is why it rarely came under the consumer’s radar. But, for the last two years, a time when consumers’ financial concerns are at an all-time high, shrinkflation is not going unnoticed.  

According to a recent consumer survey by Gartner, “In the past year, 70% of consumers say they’ve noticed shrinkflation or skimpflation [the practice of using cheaper materials in a product] in at least one product category. 41% of consumers noted that household products suffered from shrinkflation, while 32% of consumers noted that ‘personal care’ products suffered from it.”

Here are a few recent examples of shrinkflation:

• Nestle was recently chastised for reducing its Cadbury Dairy Milk sharing bar from 200 g to 180 g. 
• Bags of Party Size Frito Scoops were reduced from 18 to 15.5 ounces this year. 
• PepsiCo is phasing out its 32-ounce Gatorade bottles in favor of 28-ounce containers. 

In addition to customers reacting strongly to shrinkflation, manufacturers themselves are understanding the ineffectiveness of this practice. “It’s a short-term tactic for a long-term problem. Shrinkflation doesn’t drastically impact transportation routes, packaging or other fixed overhead costs, it only helps with the quantities of raw materials, which means it doesn’t have the restorative impact on margins that companies are often hoping for,” said Ira Dubinsky, GTM director at Peak, a decision intelligence company.  

Incremental efficiencies across the supply chain can help

Aggregating data across the traditional supply chain is common, but customer operations – where demand is generated – is often siloed away in another department and typically works at a much more granular level. Linking fulfillment and demand data across the supply chain represents a huge opportunity for demand planners, who are already working at capacity. 

This is where artificial intelligence (AI) can help. Having location data, customer behavior trends and detailed retail stock movement numbers in one place can help businesses make incremental efficiencies across the supply chain. Cost savings are realized as businesses don’t miss out on sales due to inventory issues, avoid late delivery fines, optimize vehicle routes to lower fuel costs, and so on. 

Fostering decision intelligence with artificial intelligence 

Decision intelligence (DI) is the commercial application of AI to the decision-making process. Peak, an AI platform founded in 2015, builds DI applications. It competes with platforms like O9, C3 and DataRobot. 

Peak’s platform features a library of ready-to-go applications that fit a variety of use cases across sectors including CPG, retail and manufacturing. These apps allow users to rapidly apply AI to deliver on commercial objectives, while giving them the tools they need to expand the use of DI over time.

“Imagine if you knew what to manufacture, when to manufacture it, how much your materials would cost, the number of packs per pallet and the most efficient delivery route. We don’t have a crystal ball, but we do have the next best thing,” said Dubinsky. 

Peak’s stack of demand and supply applications is designed to take data from across the supply chain and uses AI to optimize decisions from buying and inventory, to logistics, to pricing.

By bringing both technical and commercial teams onto one platform and providing an interface for commercial teams to engage with a model, Peak addresses many of the challenges faced by businesses looking to deploy AI. It ensures models are outcome-focused, and speeds up the process end-to-end, increasing time to value.

Dubinsky cited an example of the sustainable grocery delivery company, the Modern Milkman, which needed to get a complete view of its supply chain and, subsequently, make more informed decisions about its stock, ordering and warehouse operations. 

By taking a connected, data-driven approach, the Modern Milkman is able to predict demand in a volatile environment, reduce food waste, eliminate unnecessary costs and ensure that the right products are in stock in its localized grocery hubs to meet customer demand. Making incremental efficiencies by optimizing performance across the supply chain keeps its customers coming back and puts the Modern Milkman in a great position to combat this inflation cycle. 

The right data can solve problems

As problems that drive shrinkflation aren’t going anywhere any time soon, Dubinsky advises companies to break down internal silos to bridge the divide between demand generation and demand fulfillment. “Marketing and supply chain folks really can be best friends. Linking data from these two departments can unlock some of the opportunities we talked about earlier where demand data enriches the demand forecast and helps make operations more efficient,” he said. 

Another key priority should be customer data. The future is about 1:1 relationships with consumers and a consumer-centric way of operating, Dubinsky says. Whether you sell directly to consumers and know who they are, or sell your manufactured products to retailers, use every marketing channel to drive signups and data collection, and then provide rich experiences to nurture that relationship. 

“The common theme here is that strategies need to be informed by a rich understanding of who your customers are, where they are, and what they’re buying. AI can do that with more data, smarter and faster,” said Dubinsky. “And that’s where the longer-term play comes in. Invest in technology that leverages data from across your organization to optimize inventory, minimize movements and manage volatility.”