In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Predictive Analytics – What is 2% Worth, we interviewed Edward Crowley, CEO at The Photizo Group, Inc. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.
Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?
A: The biggest challenge in any predictive analytics deployment is to have the understanding of the business processes, business models, and workflows required to build predictive analytics models that have a meaningful impact on the business. Clearly, these processes and models can be unique to each vertical. The bigger issue is, in my mind, that much of predictive analytics has been focused on marketing and customer facing applications; however, there are tremendous opportunities for business process and operational related predictive analytics solutions which bring immediate savings to your organization or your customer’s organization and which are often overlooked when the focus is on customer facing solutions.
Q: In your work with predictive analytics, what behavior do your models predict?
A: Today our models predict device failures for either A) very expensive capital equipment items which have significant costs associated with lost production capacity due to unexpected failures, or B) mass fleets (as high as several million units) of devices which benefit from predicting failure in order to reduce service costs, improve productivity, and early identification of mass failures.
Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?
A: By understanding when a printer or copier will actually be out of toner (versus when it is ‘low’ on toner), the amount of toner left in the cartridge when it is replaced can be reduced from up to 35% of total capacity to less than 10% of capacity. In this example, our model accurately predicts when toners will be empty, shipping the toner ‘just in time’ before the printer runs out versus shipping the toner when the printer initiates a toner low alert.
Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?
A: Our first model, a toner delivery optimization model, reduces ‘lost toner’ and excess shipping costs by over 50% per device. This translates into over $100 per year, per device savings – or from $5M to $50M for each of our OEM customers. The ROI for the model is immediate since we deliver this in a ‘as a Service’ model where the client just has to sign up; we have already developed the solution.
Q: What surprising discovery have you unearthed in your data?
A: Initially, we thought that the wasted toner from throwing away toner cartridges early was around 15% of the total cartridge capacity – but a validation phase of our project where we actually measured the amount of toner in a large volume of returned cartridges identified that the average waste is closer to 32% per cartridge.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.
A: There are, in my view, three key building blocks to PA – industry knowledge, PA technology, and a knowledgeable team which can turn technology into a solution. It’s not just about the software – it’s about the industry knowledge!