The client, a major global clothing company, faced issues with merchandise placement and management across their stores. Ensuring accurate in-store product positioning during operational hours turned out to be effort/ cost// manually-intensive. Moreover, it kept the staff from focusing on more important tasks such as personalization, marketing, recommendation-based activities, etc. thereby affecting their bottom line.
Computer vision analytics
Sensors
RFID analytics
Machine learning
IoT
Modern retail experiences are driven by customer satisfaction and operational efficiency. Focusing on merchandise planning and management – every aspect from product to price, range, space, and assortment is paramount to improve comfort, convenience, and purchase decisions to make sure shoppers keep visiting in-store locations. However, today’s retailers face numerous challenges, from macroeconomic hurdles, such as inflation and acute staff shortage, to technical setbacks, such as low inventory visibility and fragmented inventory management. The lack of digital capabilities, such as AI or automation, adds to the woes of store managers and staff since they are unable to modernize their legacy infrastructure and elevate core functionalities such as finance, logistics, order, customer management, etc. This hinders them from investing in next-level initiatives, including predictive insights-based hyper-personalization, omnichannel marketing, and reward/loyalty programs. The difficulties only grow further for large-scale clothing companies that frequently update their store layouts, promotions, and product lines, disrupting shopping experiences. Such issues negatively impact not just sales but customer satisfaction, retention, and base expansion as well.
A major global clothing company was facing a similar challenge. Frequent changes in store layout, promotional schemes, and product lines further added to their woes. Moreover, customers trying on clothes often left them in unexpected places, upending the carefully curated displays. Ensuring efficient merchandise positioning during operational hours required excessive manual intervention, increasing labor costs. The company needed a solution to effectively track and manage diverse merchandise in a dynamic retail environment, reducing manual efforts and improving operational efficiency.
Our experts at Bosch SDS identified the retailer’s need for a cutting-edge, self-learning system to address merchandise misplacement challenges after a thorough process analysis. Leveraging decades of strong innovation expertise and diverse industry exposure, our strategic solution approach was underpinned by the following measures:
We helped the client adopt a data-driven approach complemented by sensor data analysis through a real-time monitoring/alert system that significantly enhanced operations for the industry giant. Our computer vision techniques powered by RFID tagging and sensor data resulted in tangible improvements across the client’s core processes.
30% reduced manual intervention
Increased product availability
Improved inventory accuracy
Enhanced shopping experience
Streamlined store planning
Reduced customer frustration
Data-driven decision making
Improved brand reputation
Increased customer loyalty
Bosch stands out in the retail technology landscape due to its deep-rooted expertise, global reach, unwavering commitment to quality, data-driven strategies, along with a sustainability and customer-centric mindset. Through SDS’ intelligent merchandise management solution, the client was able to improve their bottom line as automated and streamlined operations translated to significant cost savings on staff wages, scheduling, and potential overtime. They also considerably reduced losses from misplaced or stolen items. The firm garnered higher customer satisfaction and loyalty through fewer product placement errors and quicker staff assistance that minimized customer ire and inconvenience. Finally, through real-time data-driven business decisions, the retailer elevated its brand reputation and competitive edge.
