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Revolutionising warehouse management: Continuous full stock checks with DexoryView

In warehouse management, the challenges are as dynamic as the inventory itself. Traditional methods of occasional stock checks have proven to be time-consuming, prone to errors, and often result in operational inefficiencies. If you've ever found yourself grappling with misplaced items, tedious inventory counts, or the headache of lost goods, DexoryView's groundbreaking feature — automated full daily stock checks — offers the transformative solution your warehouse needs.

The challenge: Traditional stock checks and operational inefficiencies

Conducting periodic stock checks has long been the norm in warehousing. However, this approach poses several challenges:

  1. Inaccuracy and time-consumption: Traditional methods rely on manual counts, leaving room for human error and consuming valuable time and money.

  2. Misplaced items: Locating misplaced pallets, boxes, or items can be a daunting task, leading to delays in production and shipment.

  3. Lost goods: Searching for lost goods can be a logistical nightmare, impacting picking, replenishment, and overall warehouse efficiency.

The solution: Continuous full stock checks

Meet DexoryView's feature — Full daily stock checks. Here's how this autonomous solution addresses the challenges faced by warehouses:

1. Real-time data collection with autonomous robots

2. Precise goods allocation

3. Instant identification of misplaced items

4. Error notification via DexoryView

The benefits: Efficiency, productivity and precision

By adopting DexoryView's continuous full stock checks, warehouses can improve their operations and easily meet contractual stock checking requirements:

1. Efficiency over occasional checks

2. Instant identification of misplaced items

3. Optimised goods tracking

4. Ad hoc location checks

Client success stories: Dexory transforms Maersk's logistics with continuous full-stock checks

Maersk, an integrated container logistics and supply chain services company, faced challenges in optimising warehousing processes to meet the evolving needs of the industry. Seeking innovation and efficiency, Maersk collaborated with Dexory, integrating an AI platform and autonomous robots into its warehouse operations in Kettering and Tamworth, UK. The results were transformative:

  1. Reduced WMS errors by 4% in one week: Dexory's real-time insights and automated solutions immediately addressed and minimised errors in Maersk's warehouse management system, enhancing overall accuracy.

  2. Saved hundreds of hours each year: The implementation of Dexory's solution significantly accelerated the process of locating items, resulting in a daily time savings of 2 hours per day on average.

  3. Improved customer experience and reputation: By streamlining logistics operations and ensuring accuracy, Maersk experienced an enhancement in customer satisfaction and an improved industry reputation.

  4. 40+ hours per week saved by automating cycle counts: Dexory's automation capabilities led to substantial time savings, allowing Maersk's staff to focus on more strategic and critical tasks.

  5. Full inventory checks daily vs. once every 2 weeks: The solution transformed Maersk's approach to inventory management, enabling daily checks instead of the previous bi-weekly schedule.

  6. Minimised 8 hours per week spent looking for misplaced/missing goods: Dexory's real-time tracking and identification capabilities reduced the time spent searching for misplaced or missing goods, contributing to operational efficiency.

Read the full case study to learn more about how Dexory's real-time, automated insights solution transformed Maersk's logistics operations.

DexoryView — Transforming warehouse management

DexoryView's full stock checks feature offers a seamless blend of automation, data analytics, and operational excellence.

Ready to revolutionise your warehouse operations? Contact us at to learn more about DexoryView's transformative impact on pick-face fulfilment.

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This company has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 849938