The Use of Automatic Intelligence making the stores smarter than ever

The Use of Automatic Intelligence

The Use of Automatic Intelligence

By Staff Writerécoute moi

Brands and retailers are already adopting AI-powered intelligent automation at a breath-taking pace and that process is about to accelerate. Over 80 percent of executives in both the retail and

consumer products industries expect their companies to be using intelligent automation by 2021, according to our new IBM Institute for Business Value study, developed in collaboration with the National Retail Federation. What’s more, 40 percent say their organizations are already engaged in some form of intelligent automation. Companies that aren’t experimenting with this capability risk falling behind and need to move quickly if they hope to remain competitive.

Why the surge in participation? Intelligent automation represents a major technological breakthrough that has the potential to not just improve, but to transform the way companies do business. In intelligent automation, artificial intelligence (AI) is infused into automation, enabling machines to learn and generate recommendations and to make autonomous decisions and self-remediate over time “Intelligent automation and AI”.

The Use of Automatic Intelligence

In the 1990s, the ecommerce revolution initiated a fundamental change in consumer shopping behavior, which has continued to gain momentum in the mobile and social media era. In the process, customer demands have reshaped the retail and consumer products industries. To meet these changes, retailers and brands have leveraged technologies over the past decade that enable them to stay close to local market trends, understand consumer preferences and shopping behaviors, design products, provide value-added services and engage consumers in contextual ways.

To understand how brands and retailers are using intelligent automation today and what they expect its future impact to be, we conducted a survey of 1,900 consumer products and retail executives who are leaders in the areas of supply chain and operations, and customer engagement in 23 countries. There was a deeper look into impacts within industries and across organizational functions to determine how retailers and brands can address the upcoming challenges and opportunities intelligent automation creates.

The Use of Automatic Intelligence

It was found that retail and brand executives have high expectations that intelligent automation can boost their organizations’ bottom lines. Survey respondents anticipate that these capabilities can help reduce operating costs by an average of up to 7 percent, while increasing annual revenue growth by up to 10 percent – four times the average revenue growth in consumer products for 2017 and two times the forecasted growth in retail for 2018.

Today, retail and consumer products organizations primarily use intelligent automation to perform discrete internal processes that rely on existing rich-data sets, such as demand forecasting and customer intelligence. But within the next three years, executives plan to incorporate intelligent automation into more complex processes that require broader sets of data, external collaboration and additional system integrations. And during that time, the projected penetration is expected to burgeon to more than 70 percent across organizational areas that span the value chain. Intelligent

automation is guided by AI tools that need minimal manual routine interventions. This operational shift augments and assists human capabilities, reduces human errors and builds efficiencies, while enabling digital operations and innovations. Four components make up intelligent automation: the first three are fueled by AI, the fourth by automation.

The Use of Automatic Intelligence

Consumer products executives project the highest rate of intelligent automation adoption over the next three years to be in manufacturing, and product design and development. These are areas in which intelligent automation can have potentially transformational impacts.

In manufacturing, ongoing maintenance of production line machinery and equipment can represent a major expense. On the other hand, any downtime can be even more costly. Brands can use predictive maintenance to address this challenge. Predictive maintenance employs advanced AI algorithms to identify potential machine malfunctions and automatically schedule the specific services needed.

In addition to maintaining equipment, brands must keep product quality high, despite ever-shorter time-to-market deadlines and increasingly complex products and processes. Regulations and standards add an extra layer of difficulty, as does pressure from customers for faultless products.

The Use of Automatic Intelligence

Using AI algorithms, machines equipped with intelligent automation can evaluate emerging production issues likely to cause quality problems. When they detect a potential issue, they can automatically notify manufacturing personnel, and may even autonomously execute corrective actions.

In designing and developing products, brands must consistently come up with new – and hopefully trendsetting – design concepts. AI search engine could inspire next trend in fashion design. To that end, brands can use intelligent automation capabilities to ingest vast pools of data related to product use, as well as contextual and global consumption information.

Many of the retail executives in survey, on the other hand, are exploring ways to apply Intelligent automation to cross-functional collaboration and interactions with customers. These activities require more complex processes that involve additional system Integrations. This focus is evident in the two areas that show the highest growth in intelligent automation adoption over the next three years: supply chain planning and in-store operations. Supply chain planning involves collaboration across multiple functions, such as materials, distribution and transportation planning. Previously, many of the processes tying these planning functions together were manual. Intelligent automation is ideally suited for this type of environment. AI-powered tools can absorb data from different planning functions, and digest and analyze it quickly. They can then produce calculations to help retailers make near real-time decisions when developing and balancing plans, determining trade-offs and gaining consensus. As they work through the process, retailers can use automation to execute repetitive tasks, direct workflows and execute resolutions to exceptions.

The Use of Automatic Intelligence

 Store operations and in-store services can also benefit greatly from intelligent automation. Every city or neighborhood is unique, with its own highly localized flow of people, places and events that shape consumer behavior and demand. A store in a college town requires different product assortment than a store in a resort area. Intelligent automation can learn from local data to determine products and services that serve the needs of the neighborhood. Based on local venue characteristics and available ingredients, it can automate assortment selection for a particular store.

AI technology also can apply what it learns to tailor in-store product and service offerings to the individual customer’s needs. Imagine, for example, that you walk into a sporting goods store looking for golfing gear. As you enter the store and opt-in for assistance, the store’s AI-powered app accesses data about your purchasing patterns, interests and preferences. It then automatically assigns you a sales associate who is a competitive golfer.

The Use of Automatic Intelligence

At the same time, the app provides your information to the sales associate, so she is equipped with pertinent knowledge at her fingertips. She greets you personally, strikes up relevant conversation while leading you to the golf section of the store, provides product-specific advice based on her golfing expertise and offers recommendations for the right gear. For instance, The Procter & Gamble Company, a multi-national consumer goods organization, is working toward deploying a next-generation demand planning solution to improve its near-term forecast accuracy globally. Its goal is to enhance productivity and equip planners to make better decisions in areas that are traditionally challenging, such as promotional lift predictions.