https://ift.tt/eA8V8J Globally, out-of-stocks cost retailers an estimated $1T in lost sales . An estimated 20% of these losses are due to ...
Globally, out-of-stocks cost retailers an estimated $1T in lost sales. An estimated 20% of these losses are due to phantom inventory, the misreporting of product units actually on-hand. Despite technical advances in inventory management software and processes, the truth is that most retailers still struggle to report accurate unit counts without employees manually performing a visual inspection. .
For product manufacturers, quality problems erode between 15 and 20% of annual revenues. Manual checks come with their own set of risks, including worker fatigue, distraction, specialized training and general human error. To quote a US Department of Energy review of the relevant literature on visual inspections, “inspection error is a fact of life.”
A solution driving use cases addressing retail’s out-of-stocks or manufacturing’s cost of quality concern is computer vision. Why? Computer vision applications are ideal for solving these and other problems because it’s on 24/7, more accurate, and can immediately scale to thousands of devices with up to 99% detection rates, minimizing product defects to the absolute minimum. Computer vision uses the power of massive data sets, machine learning and an image library to compare and identify 2D images or 3D objects against a known standard. If that image or object does not match the standard, informed or predictive action can be taken. Computer vision can answer simple questions like, “are all my screws in the bin the same type and size, or is my retail stock shelf full and organized.”
What kind of problems does computer vision solve
Computer vision by itself does not improve manufacturing quality or a retailer’s store shelf-stocking levels, but it closes the time that a defect or stock out is detected and corrective action is taken. Use cases that benefit from computer vision are:
Manufacturing
- Quality assurance and inspection: final paint finish on a new car, judging if circuit boards are assembled correctly, or are screws machined within tolerance
- Positioning and guidance: weld location for automotive assembly, or pick and pack for warehouse shipment
- Predictive maintenance: measuring wobble or shaft diameters in rotating equipment
Retail
- Self check-out: speeding customer check out and decreasing shrinkage
- Inventory management: incorrectly placed products and gaps on shelves
- Store lay-out improvement: assess customer traffic flow and optimal merchandising
- Virtual mirrors and recommendation engines: assess product styles without trying them on
Computer vision from a data perspective
When implementing computer vision to tackle some of your toughest use cases, here are three guiding thoughts on how to handle your data:
Consider new data sources
- Typical sensors (weight, temperature, pressure, viscosity, speed and torque) produce structured or semi-structured data. For example, computer vision produces unstructured data originating from a .mp4 video feed or .jpeg still pictures. Does your current data warehouse handle this type of data format?
Address mountains of real-time data
- The volume of data created by computer vision is considerable, stemming from both the streaming data but also the thousands to tens of thousands of images that build the machine learning library. Does your current technology stack have the ETL capabilities to handle the data at the speed that your business runs? Is it stuck with batch processing? Is it able to scale to your needs five years from now?
Leverage the computer vision ecosystem
- Leveraging a strong ecosystem that enables image classification, object detection & text recognition, object-tracking and image segmentation, organizations are able to implement computer vision algorithms and apps with relative ease. Is your current technology open source? Do you have ecosystem partners lined up to automate image labeling?
Databricks unlocks the potential of computer vision
At Databricks, we are in a unique position to assist enterprises with their computer vision journey. Built with the goal of enabling all enterprises to leverage data and artificial intelligence (AI), Databricks has native capabilities for the handling of the complex, unstructured image and video data consumed in this space. Leveraging an extensible collection of the most popular computer vision libraries, Databricks focuses on scaling AI model training, management and deployment to ensure organizations are able to quickly recognize value from their work. And by tapping into the capacity of the major cloud providers, we allow organizations to cost-effectively take advantage of the specialized hardware (e.g., GPUs, edge devices, etc.) and workflows required by many computer vision models.
With this in mind, we are launching a series of blogs intended to share our insight on computer vision from a data-driven perspective, how a data platform may be used to tackle a wide range of computer vision challenges or end up being a challenge in itself, and how ecosystem partners can speed return on investment.
Attend Computer Vision Webiar with LabelBox
Our goal is to enable organizations to successfully deliver computer vision capabilities that map to widely recognized needs in the retail and manufacturing industries. Want to get started building computer vision solutions at scale? Join our upcoming workshop to get hands on understanding on December 9, 2021 at 9:00am PST as we kick off this series with an engaging webinar with our partner LabelBox. See you there.
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