Using AI to Serve 'Right Here, Right Now' Consumers

Consumers have come to expect a new level of immediacy that requires brands to contend with a confluence of factors that create unprecedented challenges while simultaneously driving change for consumer goods organizations.

First, consumers are hyper-aware and demand transparency. In a recent study from Label Insight, 94% of consumers said it was important that brands be transparent about how their food is made, and 37% said they would switch to another brand that provided more detailed product information. This also extends beyond production to include sourcing, ingredients, freshness and other information.

At the same time, the industry is experiencing a sustained period of declining revenue and profitability. Recent industry analysis reveals that, from 2006 to 2011, average yearly revenue growth was 7.7% and average annual operating profit growth was 6.1%. By contrast, average revenue growth from 2012 to 2016 fell to 0.7% and average operating profit dropped to 1.3%.

These issues are exacerbated by the radically evolving nature of competition itself. Consumer product companies increasingly find themselves competing at the shelf with their retail customers’ private label offerings. New, nimble market entrants are challenging the old order, chipping away at category volume while capturing a disproportionate share of category growth.

Winning in this environment means identifying new opportunities for consumer transparency, immediacy, and growth that together also increase agility, accelerate execution and lower both cost and risk. This is where artificial intelligence and machine learning enter the picture.

AI and machine learning can be applied to automate repetitive tasks, monitor processes to spot exceptions or identify patterns, and even learn from data over time. The development of increasingly precise predictive capabilities can improve forecasting, eliminate waste, reduce cost and accelerate execution — all at a reach, scale, pace and level of accuracy well beyond that of any human cognitive capacity. This provides opportunities to automate and accelerate any number of business processes, at any point within a consumer-centric value network.

For example, SAP is working with beverage manufacturers that merchandise their products in coolers and vending machines to leverage machine learning to improve asset efficiency and merchandising effectiveness.

Through a combination of sensors and cameras placed inside individual coolers, companies can now monitor ambient temperature and electric power to help maintain product freshness; the location of the asset to ensure it's properly placed and quickly identify potential theft; the correct placement and facings of products inside the cooler to ensure merchandising compliance; and actual purchase demand as consumers buy the items.

Applying machine learning to the data gathered by the sensors and cameras has transformed what was once a reactive, time-lagged process to one that is entirely proactive and real-time.

Now, companies can monitor the health of their asset network globally and in real time while automating the process of generating work orders for necessary servicing or replacement in a store. Likewise, they can monitor consumer demand down to the individual cooler level, predict ideal re-order points and automatically generate replenishment orders to help ensure the cooler is always correctly and optimally stocked. 

SAP similarly is transforming supply and delivery networks by applying sensors to enable true, collaborative cold-chain logistics and distribution. The data generated by the sensors helps guarantee that manufacturing inputs and finished goods are stored, transported, and delivered all the way to the shelf — and in some cases, directly to consumers — within defined temperature tolerances.

This enables trust and transparency for all value network stakeholders — suppliers, manufacturers, distributors, retailers, and especially consumers — when it comes to establishing provenance and sourcing, predicting freshness and expiration cycles, and optimizing supply based on observed demand to minimize waste and spoilage.  

These examples demonstrate how AI and machine learning are being applied to automate, optimize and accelerate existing processes, all without the need for human intervention and in a way that translates to higher asset utilization, lower inventory carrying costs, and higher incremental sales volume.

As the technology becomes more widely adopted and drives substantial improvement to both the top and bottom line by enabling automation and accelerating collaboration across business functions, additional use cases will emerge that will highlight this maturity.

To address consumers’ need for immediacy, consumer product companies that investigate the numerous benefits of these technologies and invest in automation, collaboration and intelligence will find themselves best positioned to serve their “right here, right now” customers.