This article taps into discussing problems met by retailers and ways in which they can be solved using robotic automation. Firstly, the industry’s problems will be outlined, follow by exploring the existing solutions.
Brick-and-mortar shopping is regaining traction after the digitalization of everything during the pandemic, and, with it, retailers’ preoccupation with operational efficiency and increase in customer satisfaction. Online retail might seem to have the upper hand but what often fails to be mentioned is the value that consumers put on in-store purchases with 54% of consumers declaring that they’d rather check the product online and buy it in-store.
Convenience and the ability to browse, feel, and touch the products define a satisfactory shopping experience for more than half of the shoppers. What happens when the retailer doesn’t have what they want? Due to inconvenience caused by price and product quality (including in-store presence and display), as many as 97% of consumers abandon their purchase journey.
A store’s success depends on how retail operations work towards customer satisfaction, and convenience while alleviating staff’s frustration with repetitive, monotonous tasks.
Although composed of a plethora of activities, from a customer’s point of view, retail store operations are equivalent to what they see: prices, promotions, products on shelves, queues, and checkout waiting time. The mechanism of retail operations concludes in-store, in front of the shoppers, and what they see is what defines their shopping experience perception.
Retailers see in-store inventory and price management in need of optimization and improvement to deliver the convenience customers are asking for. Both factors bring their own issues to the table, and solutions for them are not shy to appear, but do they provide substantial enough results to justify the investment?
To enhance the drive of additional business, retailers practice competitive pricing, sales, and promotions. The functionality of these methods relies on the accuracy of the information that gets to the customers, in this case, the shoppers that read the shelf displayed labels.
Taking into consideration that for consumer-packaged goods (and especially retail) prices tend to change frequently, labels displayed in-store need to adapt as fast as the modifications happen. Failure to present accurate pricing and promotions inconveniences customers’ buying journey. In correlation, shopping experiences poisoned by the inconvenience of inaccurate labeling lead to loss of business, which is never desired.
Retailers have the responsibility to monitor if products are precisely named, marked with the correct price or if the right promotions are applied. Each label must be verified against the store’s price and promotions database. Such processes are defined by repetition and monotony, heavily relying on staff, an excessive amount of time to be finalized and continuous implementation.
Building a favorable environment for customers to navigate the store drives sales growth by delivering a comfortable, convenient shopping experience. By manipulating a store’s space through display planning, technology, lights, and color, retailers attract buyers’ attention while facilitating their journey.
Visual merchandising responsibilities have products as central elements. In-store organization and shelf display strategy are reliant on trends (either a shift in or new discoveries), brands’ product placement preferences, and shopping patterns. To keep the store environment compliant with product placement strategies, retailers require good understanding of the store’s condition and updated information about the on-shelf availability of products.
Stock and inventory management goes beyond a retail store’s backroom and extends in front of the customers, on shelves. It is a multi-level process defined by a long communication chain that is prone to errors and delays. More than ordering and restocking, issues of the supply operations target trends and patterns, data collection, demand forecasting (dependent on store data), on shelf stock condition monitoring (misplacement, clutter, missing).
Having overviewed the price and inventory issues retailers confront with, let’s explore existing robotic automation solutions available to retailers for their correction.
Solutions that solve problems of the supply chain are starting to get to the heart of the retailer. Even though they approach shelf gap detection and in-store inventory data, monitoring on-shelf availability needs a more robust solution that tackles these issues holistically.
The retail automation market is extensive. Aside from manual audit (which will not be approached in this article since it becomes ineffective and limited in the context of retail automation’s growth), three solutions seem to be under retailers’ consideration more often than the others.
Technologies such as Electronic Shelf Labels, to maintain price accuracy, and retail shelf-mounted cameras, to track product availability, are currently being deployed to physical stores to boost operational efficiency. Although popular, they represent an incomplete solution for the formerly mentioned challenges.
As follows, ESLs and retail shelf cameras, autonomous and semi-autonomous solutions are analyzed, presenting their benefits and shortcomings.
Electronic Shelf Labels (ESL) are small units using e-Ink paper to display product data from price and name to stock availability or applied promotions. Being updated wirelessly and automatically from systems that centralize product information, ESLs serve retail operations concerning on-shelf price conformity.
To populate a supermarket-sized perimeter with smart labels, a retailer must invest in thousands of individual shelf-placed units. Abandoning old paper methods has increased the accuracy of the displayed prices, bringing some efficiency to time-consuming processes.
In-store prices are timely updated, and the perception is that they are more correct than ever. However, ESLs have replaced repetitive tasks with others of the same nature. Labels are not scanned by hand, but that doesn’t prevent malfunctions of the electronic price tags. The printing and individual placement of paper tags is changed with a tedious battery or device replacement process (since ESLs battery lifespan is no longer than one year or two). Electronic labels’ market growth is not indifferent to those hiccups since it is slowed down by difficult installation operation and expensive infrastructure costs. Additionally, as the store integration process of ESLs is highly dependent on the company they’ve been acquired from, so is the maintenance process.
To have smart labels under care requires not only supervising price display but monitoring the whole operation behind it. This translates to the staff being reliant on lengthy training sessions and the acquisition of technical knowledge necessary for the operation of ESLs. As seen by retailers, the only way in which the utilization of electronic shelf labels is justified (bottom-line the benefits delivered and ROI point of view) is when they bring to the table additional use cases. Hence, as a self-standing solution, it doesn’t seem to be fully convinced of its efficiency.
Even though providing a more accurate display of information, malfunctions at the level of individual tags still need to be noticed in time and solved individually, taking a step back towards manual audit. Still reliant on a long interdepartmental communication and on the store staff’s reaction to resolve detected issues, it stimulates misinformation risks and associated delays in action.
On-self availability refers to the products available to be purchased from a store, by customers at a given time. It is more complex than is believed as its status is affected by multiple factors along the supply chain, including out-of-stock situations, phantom inventory (product is out of stock but appears as existent in inventory systems), and product voids (items are approved but have nonexistent shelf tags and inventory). To monitor levels across the supply chain, retailers have slowly moved from manual audits to solutions based on artificial intelligence and computer vision. One of those solutions is retail cameras for on-shelf availability.
Cameras are mounted on either side of each retail store aisle, capturing the entire ensemble of shelves and products in front of them. With a low operational cost, retailers can attain real-time monitoring of their stores. Although promising the delivery of hourly data for the whole stock-keeping unit, retail camera manufacturers are solving only half of the problem. Passive monitoring of OSA does not refill shelves, solve labels’ accuracy issues or their replacement.
From an operational point of view, shelf cameras do an excellent job when it comes to product and stock detection, compliance with product placement strategies, and data collection (inventory updates, shopping patterns). However, with shelf gaps being only one side of the problem, the retail shelf cameras build to high expenses and reliance on long communication chains prone to human error.
To define their utility, the advantages and disadvantages of their functionality are underlined. On the one hand, shelf cameras avoid image blurriness by being stationary; on the other they are easily impaired by external elements that can block their view (abandoned shopping carts, boxes, and pallets, clutter). 400 cameras dispersed around a 30k sqm surface offers permanent access to shelf view across retail stores. However, maintenance processes and costs for hundreds of devices do not justify the need for total, constant surveillance of products. The surge in product demand remains unpredictable and concentrated under the peak traffic periods of the shopping day or promotional campaigns.
Retail OSA cameras require not only installation procedures that are lengthy and costly, but also dependency on the manufacturer across maintenance processes. From cables and mounting supports to network administration, and overall system setup and integration with the retail store, shelf cameras introduce supplementary tasks for which the staff needs to be rigorously trained. Video analysis needs to be performed both by cameras and designated staff for the relevant data to be filtered (i.e., distinguish people from other objects, clutter on shelves).
Having its focus only on-shelf availability and out-of-stock situations, shelf cameras are a solution that, although popular, still struggles to have its cost versus benefits validated by retailers. Complex integration processes and long adjustment timeframes for retailers and staff to operate the new system contribute to the impeded adoption growth of OSA cameras. Additionally, retailers must be ready for a considerable commitment upfront and ongoing, as they are indirectly forced into vendor lock (training, store integration, updates, and maintenance are dependent on the manufacturer of the solution). They need to be prepared for changes brought to the store’s infrastructure, already implemented software and solutions, and a different way of operation and specialized workforce demand.
ESLs and shelf cameras are solutions dependent on human intervention, relying on interventions subsequent to the reported data. Hence, development in retail automation has taken a giant leap toward solutions that aim to function without the need for human help.
Covering both price and inventory issues, autonomous robots have brought a new level of efficiency to in-store tasks as well as new problems and complexities that retailers have not been prepared for.
Some retailers have abandoned a gradual transition to the automation of retail operations and have jumped straight to autonomous aisle roaming robots. Autonomous robots scan stores for price tag inconsistencies, on-shelf inventory, and stock issues. In the last couple of years such robots have gained both in popularity, yet generated their own concerns. The solution has been put to question, its utilization causing trouble in retail stores and not delivering the expected results. Consumers have had trouble adapting to the self-roaming robot attacking, getting scarred by or being hurt by it
Although bringing efficiency to OSA and price tag detection-related tasks, in-store problems are still not solved in their integrity. Issues are detected and signaled but to actually replace a price label or replenish on-shelf stocks are actions still dependent on long, error-prone communication chains. ROI issues, and high acquisition and maintenance costs, together with registered malfunctions, make retailers question fully autonomous solutions.
Building bridges between solutions that are outdated, incomplete, and heavily reliant on staff intervention (ESLs and shelf cameras) and the farfetched, costly autonomous robots, semi-autonomous robots are collaborating with humans to deliver the best of both worlds.
With the aid of a human operator, a semi-autonomous retail robot completes activities linked to in-store inventory. Pushed through the store's aisles to identify price label inaccuracies and gather data on the OSA status, it generates real-time scanning results.
The workforce required to execute tedious retail operation tasks is reduced to one staff member who oversees guiding the robot along the aisles and reviewing the output results of scanning sessions. The same operator can take action to resolve or communicate in-store pricing mistakes or out-of-stock issues.
Most problems found during scanning can be resolved immediately, reducing the danger of misinformation or action delays by reducing the need for interdepartmental contact. Being semi-autonomous not only offers the advantage of being user-friendly (no specialized staff needed) but also more cost-effective than other solutions. The thing it needs most is to be brought from the charging station to the designated scanning start point.
ERIS completes the functionalities of shelf cameras through additional capabilities and use cases that treat OSA issues globally, not partially. By being able to detect and scan both paper labels and e-ink electronic tags, the semi-autonomous robot makes the task of monitoring labels, including ESLs malfunctions, easier. As the shelves are scanned, when price tag issues are detected, the operator can print the correct labels and replace them on the spot or send alerts about the electronic tags that need to be updated.
Encouraging the advantage of on-the-spot problem solving, ERIS gets rid of issues caused by long communication chains while being effective in resolving both price tag inconsistency and OSA issues. In perspective, the semi-autonomous robot eliminates risks and concerns brought by its fully autonomous competitors while being a more complete solution than electronic shelf labels and shelf cameras. Additionally, ERIS not only offers a better ROI, but it is also easy to integrate, maintain and get accustomed to solutions.
In-store pricing accuracy and on-shelf inventory issues are on top of retailers' minds when it comes to delivering convenience and satisfaction to customers. ESLs, shelf cameras and autonomous robots have been overviewed as some of the most popular solutions considered by retailers. However, they have their shortcomings when comparing the delivered results to the overall investment.
Solving both price inconsistencies and inventory issues, store robots have been created as a single unit roaming around in-store aisles. This eliminates the trouble of having to monitor hundreds of shelf places units, reduces maintenance time and minimizes the store changes brought through their integration. A smooth transition towards retail automation are semi-autonomous robots, that work with human staff to solve price and inventory issues as efficiently as possible while promoting on-the-spot problem-solving.
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