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Why understanding customer data remain a critical need for 2021 and beyond

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(Image credit: Shutterstock / Gorodenkoff)

We are dealing with an incredible set of “firsts.” Covid-19 has been a massive catalyst to the biggest transformations and the acceleration of online purchasing, automation investments and dynamic intelligent planning driven by the expansion in fulfilment models.

Online purchasing: Online shopping and e-commerce have accelerated: According to Planet Retail, 2020 has been an inflection point for ecommerce, which is now projected to account for 28 percent of chain retail sales globally. This is forcing retailers to quickly adapt and automate their stores, fulfilment centers and logistics workflows.

Automation acceleration: We have seen companies increase their spend on automation including robotics and the use of mobile computing and wearable technologies. This is to augment the human workers’ productivity and to better equip them to operate in a partially automated environment. Consider a robot working in a collaborative manner with a human worker, driving increased investments in the “co-bot” category. These technologies will continue to be embraced in 2021.

End-to-end supply chain optimization is being driven by the need to deliver directly to consumers in increasingly shorter timeframes. Carriers are shipping at peak volumes daily. Retailers, logistics companies and service providers are all facing the same seasonal highs associated with holiday periods, driving the critical need to automate workflows to satisfy customer demands while gaining efficiency and productivity.

Dynamic intelligent planning: As operational fulfilment models expand in all forms – micro-fulfilment centers, picking and shipping from retail stores, distribution centers being used for ship to store as well as shipping directly to customers, the need for greater visibility and predictive planning has never been greater. Given the dynamic nature of these fulfilment models, the ability to more effectively plan has become critical in the retail, transportation and logistics (T&L), manufacturing and healthcare sectors. From labor, inventory and fulfilment planning, intelligence has a role in answering questions such as: how do I allocate the right labor in the right places based on what's going to be required? For T&L, it is a question of intelligent planning around capital equipment, such as understanding what vehicles, trailers and other capital assets are needed and where and when to fulfil these requirements.

The top challenges faced by customers

As Covid-19 is expected to continue to impact operations across all vertical industries in the near term, the ability to integrate new technology solutions to keep enterprises running and profitable while optimizing workflows will be critical. Globally adapting operations to keep employees and customers safe and maintain adequate staffing levels are top 2020 and 2021 priorities. 

Depending on the industry, the pandemic has created different operating environments which will continue to drive customer demands:

In healthcare, ensuring the accurate labelling, tracking and tracing of laboratory test results, assets, patient flows in and out of hospitals and ambulatory units is driving the need for real-time visibility. Another requirement is medical-grade plastics on mobile devices suited for frequent disinfectant wipe down to keep clinicians and patients safe.

In logistics, there is increased demand from more people buying online. Given the rapid change in fulfilment models, adapting operations to build more efficient supply chains is driving technology investment and the need for more employees. For example, according to Forbes, Amazon US has hired more than 100,000 workers to cope with this demand increase. Technology such as wearable devices, collaboration solutions, Android™ mobile computers for easier and faster on-the-job training and cobots (collaborative robots) for supplementing human efforts: these all help manage and enhance labor utilization and reduce costs. Today, a typical warehouse operator can walk many miles picking goods from different warehouse locations to the pack and ship station. Using an autonomous mobile robot (AMR) significantly reduces this as the goods are brought to or from the operator within a picking workflow. This optimizes labor planning and workflow efficiencies.

In retail, category consumption is evolving with increased sales of cleaning supplies, for example, while select other categories have declined in volume. The challenge for retailers is the ability to forecast and manage their supply chains, without knowing precisely what will be in demand over time in a rapidly changing environment. Another need is equipping and enabling store associates with the required technology to manage more complex tasks, such as reacting to online orders. We are seeing an increased demand in mobile computing devices as new digital shopper experiences continue to grow. Companies that act quickly and innovate in their delivery models to help meet and exceed consumer expectations will establish a strong advantage. Even if retailers have modern data analytics and machine learning capabilities, they are often operating on historical data. Embracing new, real-time data sources to create more relevant predictive models can help retailers take the best next action in their operations.

There is a growing need for data analytics planning and predictive modelling. Customers are asking for help to make sense of their data because they need to rapidly adapt their operations and fulfilment models while being able to forecast demand in real time. Combining customer and inventory data with other external sources to readjust the predictive model is what innovative customers are doing right now. In the new normal, it’s about being more proactive rather than reactive.

Enterprises must explore new ways to optimize operations, improve productivity and increase profitability. Understanding the meaning in all of their data to help them achieve this is critical. We see an increasing need for mobility and visibility across operations that augment data analytics platforms, predictive modelling solutions and decision automation. Many customers today are starting to integrate intelligent automation solutions into their retail, warehouse and supply chain environments.

The top 3 technologies that will influence or disrupt our industries

The top three technologies that will influence or disrupt our industry in 2021 are computer and machine vision, intelligent automation including robotics and AI, and prescriptive analytics.

All three of these technology areas relate and depend on one another to realize value. Companies can visualize their operations better through both real-time data (e.g., computer vision) as well as source system data (e.g., ERP systems). They can turn that enhanced visibility into intelligence to drive automated decisions to the front line, enabling them to take better actions resulting from the combined value of these technologies.

Computer and machine vision

Developments in computer vision and machine vision solutions are delivering more precise depictions of a company’s physical environment, regardless of industry. Computer vision systems enable solutions to visually interpret and understand the world in a broader, more dynamic way. Computer vision can provide recognition that rivals human capabilities allowing for better inventory visibility and streamlined check-out at the point of sale for example. Machine vision is a subset of computer vision, using visual techniques to focus on inspection analysis and anomaly detection.

In retail, examples include shelf-edge monitoring cameras and robots that roam the aisles in a retail store to monitor inventory and merchandising compliance. Computer vision can also help retailers ensure there are no issues with items being scanned and no theft is occurring in self-checkout lanes while also leveraging product recognition to speed up the check-out process. This transforms the overall value for retailers who can be more confident about what’s happening while shoppers can complete their purchases faster than going through staffed checkout lanes.

In the supply chain, these technologies can increase efficiency, including trailer and container capacity, workflow optimization and time savings. Utilizing AMRs in the warehouse helps solve labor shortages, meet customer demand, and reduce errors. Computer and machine vision can be deployed in a flexible, intelligent way to augment or even swap out physical labor in selected parts of an operation and workflow process so workers can focus on more strategic activities. Systems with computer and machine vision will continue to drive automation in 2021, with added capabilities to capture, process, interpret and direct action.

Intelligent automation including robotics and AI

Intelligent automation has been facilitated by virtual assistants from the consumer space such as Alexa and Siri. Application of this type of machine learning technology in enterprises is starting to improve workflows, deliveries and customer experiences. Additionally, artificial intelligence can improve our ability to recommend the best next action. For example, we see the use of artificial intelligence in manufacturing and logistics increasing in 2021. Artificial intelligence and robotics are driving intelligent automation as part of the Internet of Things (IoT) and Industry 4.0 trend.

In warehousing and supply chain, physical automation, radio frequency identification (RFID) and temperature sensing technologies – combined with the growth of robotics, including cobots that interface and co-work with humans – will help fulfilment centers improve ecommerce operations. RFID technology can enhance delivery processing and handling efficiencies for transportation, logistics and postal service providers. Integrating temperature intelligence solutions can indicate if vaccines, medications, and biologics have been exposed to potentially damaging conditions that can impact their efficacy.

In retail, automation can be a combination of physical mobile automation with fixed infrastructure such as RFID readers along with shelf edge cameras and computer vision. This will provide perpetual visibility inside retail store environments. A key benefit is the optimization of labor costs, ensuring retailers have the right items in store to drive their revenue and meet evolving customer demands.

By using a decision automation capability such as prescriptive analytics together with various data sources such as data coming from shelf-edge cameras, incoming orders, availability of in-store and nearby store inventory, labor resources including whereabouts of associates, their roles and availability, inventory location, and at any given time the in-store customer load, we can automate labor and inventory planning as well as the workflow itself. This prescriptive analytics capability can be qualified as digital decision automation as it can ingest and analyze all this data to decide what tasks need to be done in real time. It’s all about augmenting humans with technology. 

Prescriptive Analytics

The addition of artificial intelligence (AI) and machine learning (ML) in analytics solutions creates platforms that interpret data automatically and distribute improvement opportunities to the appropriate stakeholder with easy-to-understand actionable steps.  Prescriptive analytics platforms can recognize patterns and identify when abnormal behaviors occur to direct actions, avoiding errors, inefficiencies and improving operational outcomes.

For example, a retailer that had forecasted 3,000 units of bleach as part of its inventory planning sees its sales of bleach double in two days due to a Covid-19 outbreak in the area. The analytics system identifies this surge as it relates to the outbreak and then adapts its planning model to adjust inventory at other stores that show the same outbreak characteristics in the future. Without this capability, this spike in demand would not result in learning that helps to self-heal the demand needed in various geographies based on these changing dynamics.

In 2021, actionable intelligence gathered and distributed across operations by prescriptive analytics platforms will be doing more than eliciting swift preventive actions or resolutions. It will help build trust across the workforce while empowering individuals to confidently propose and collaboratively execute positive changes across the entire business with simple actions that deliver results.

Tom Bianculli, Chief Technology Officer, Zebra Technologies