Predict Inventory Damages in Real Time With Machine Learning
September 2, 2021
Machine learning stands at the forefront of revolutionizing inventory management. By harnessing the capabilities of advanced ML models, businesses can now predict inventory damages with unparalleled accuracy and address them in real-time. The result? A proactive approach to handling product damages, optimized replacement processes, and a significant reduction in associated costs. Dive deep into the transformative power of machine learning in reshaping the way industries handle inventory challenges.
If you run a company or distributor shipping items in bulk, you know how damaged inventory can eat into your bottom line. You’ve invested heavily in putting sensors in your logistics and collecting data, but now you’re wondering: what does it take to put all that data to work solving this problem?
Product damage causes huge revenue losses every year across the storage and transportation industry. Replacing broken items is costly, to begin with, but the inefficiency of the replacement process makes these costs exponentially greater.
Let’s say you’re sending a shipment of wine glasses to a warehouse, and the truck driver hits a bump. Just like that, $7,000 worth of inventory is damaged…and you have no idea it happened. Two days later, when the shipment reaches the warehouse, you finally learn about the broken glasses. At that point, you have to call in to get a new shipment, which takes two more days to arrive at the warehouse. As the inefficiencies pile on, a few broken glasses add up to a major headache.
That’s why you’ve decided to invest in data science. Using the physical shipment data and real-time analytics you’re already collecting, your goal is to deploy ML that predicts damages the moment they occur, then uses AI to automate the replacement process — saving you time and money.
Your first step is investing in research to train models on the huge amounts of historical data that you have. Your data science team is developing a suite of models that are based on the route, and believe they will need frequent retraining as they learn about seasonal or weather impacts and other factors. However, there are some challenges when it comes to getting that research into production:
- Your COO is asking to see what the impact is to the bottom line in real-time, how you know if the model is having a positive business impact, and how the operations group can benefit
- You are concerned about deployment overhead:
- Will you need to pull your engineering teams from other projects if there is a lot of constant work to deploy the models and to build the right analytics capabilities
- What kind of infrastructure will you need to run these models
Figure 1. Move the slider back in time to see the location of packages, including damaged, delayed, and replacement packages. You can click on a package to see more details.
Speed up the inventory replacement process using machine learning
To optimize your ML investments, you need to work with deployment technology that’s flexible, easy to use and scale, and can give you the visibility to answer business questions in real-time.
One such software solution is Wallaroo, an innovative platform that shortens the time between deploying an ML model and reaping the profits. With everything you need to swiftly launch, test, and iterate your ML models, Wallaroo streamlines bulky processes (like inventory replacement) so you can leverage your AI/ML quickly, simply, and at a far lower cost.
Here are a few more advantages:
- Simplified deployments: Push models into production with minimal effort and stick with the training frameworks your team already knows. This saves your data scientists hours of re-engineering models and frees them to focus on refining their models to drive even more revenue.
- Live ML models in seconds: With the capacity to process data in microseconds (that’s one-millionth of a second)—Wallaroo is the fastest platform on the market for production AI. You can analyze data 100X faster, react to market changes in real-time, and deploy new and improved models within seconds, not weeks or months.
- Built-in monitoring and audit: Easily understand the business value of your ML and iterate quickly using powerful metrics on real-time model performance; along with recommendations to increase revenue and detailed analytics to streamline model retraining and compliance.
- Low cost and low maintenance: With a single platform for data ingestion, analytics, and deploying ML models, you can save up to 80% on infrastructure and maintenance overhead. Plus, with the ability to run multiple models on a single server, your resources are used more efficiently for lower computing costs.
As an added benefit, Wallaroo can be deployed directly in your environment, whether that’s in the cloud, on-premise, or at the edge, and a dedicated team will help you integrate it with your existing systems. This lets your company leverage the benefits of machine learning sooner, without interrupting your data scientists and engineers so they can concentrate on what they do best.
Real-time inventory damage prediction that’s easy to use
Wallaroo essentially acts as a runway between your ML models and business value—speeding up the time it takes you to reach it. Using the wine glasses example, here’s how you could leverage Wallaroo to predict damages in real-time and reduce your costs:
Imagine all your wine glasses are shipped in trucks equipped with built-in accelerometers, along with other sensors. These sensors continuously stream data back to the cloud, allowing Wallaroo to deploy your machine learning solution to determine the probability of damages and delays in real-time.
Wallaroo would also power additional visualizations, such as an interactive map that allowed you to see all your shipments, all delayed shipments, or all damaged shipments in real-time–unlike most solutions, which process information in batches, limiting your ability to respond quickly to issues that affect your bottom line. This tool can be a crucial component in automating the replacement process. Whether you want to order a new shipment of wine glasses, alert the warehouse that a new shipment is coming, or send out emails to stakeholders informing them about the damaged glasses and the fact that a fresh batch is on the way, that functionality can be automated with AI, freeing you to focus on other matters.
Leapfrog traditional inefficiencies and reduce costs
Developing and implementing a solution like this isn’t easy–it typically requires months of labor and operational overhead. By plugging a platform like Wallaroo into your existing data ecosystem, you can leapfrog all those steps, relieving yourself from managing low-level engineering costs and challenges.
With the flexibility of open-source tools and the power of advanced AI/ML processing, Wallaroo enables your team to launch a solution that’s three times faster than the DIY version, with 80% lower compute costs and 50% lower operational overhead—giving you the best possible return on your investment.
