Predictive analytics is the big “buzzword” in much of the industry today, and the imaging market is no exception. While still profitable, our margins are under pressure on almost all fronts. Customers increasingly see less differentiation in hardware, and as a result, competitors are more often using price as a weapon. Supplies still have strong margins, but they are also under pressure from remanufacturers and new, aggressive pricing models. A third, but no less critical, dynamic affecting margins is the increasing pressure on basic managed print service (MPS) engagements to compete on price, as more firms claim MPS capabilities.
The key opportunity in our market is to drive increased profitability, with increased efficiency and decreased costs. One of the key ways to reduce costs is to move from a reactive form of decision-making to predictive decision-making. A great example of this is service management. Today, we build a service organization based on historical data on failure rates for devices and by using our commitments for service-line agreements in the field. When a device fails, we dispatch a technician to fix it. The next day, we may dispatch the same technician to the same building to fix another machine. We react to device and supply failures.
Now, in the same scenario with predictive analytics, we would have been able to anticipate both device failures by analyzing usage data, environmental impact and metrics on failure occurrence. Instead of making two service calls, we could deploy the technician to proactively fix both devices, before failure. Furthermore, with consistent analysis of parts usage and installed base, we could forecast what parts will be needed and stock the service vans accordingly.
Predictive analytics uses multiple data sources (both structured and unstructured) to analyze individual device data in order to predict future events, such as device failures. It is already having a big impact on many industries, most notably automotive, energy production and jet propulsion. In these industries, firms are seeing downtime reductions of up to 45%, breakdowns being reduced by up to 75% and maintenance costs being reduced by as much as 25%. Notice these are all production and delivery cost savings. Usually when I ask firms how they are using predictive analytics, they reference someone working on customer retention, targeting or other revenue drivers, which are all good. However, we feel the greatest, and most immediate, impact is available from cost savings on manufacturing and fulfillment.
The key opportunity in our market is to drive increased profitability, with increased efficiency and decreased costs. One of the key ways to reduce costs is to move from a reactive form of decision-making to predictive decision-making. A great example of this is service management. Today, we build a service organization based on historical data on failure rates for devices and by using our commitments for service-line agreements in the field. When a device fails, we dispatch a technician to fix it. The next day, we may dispatch the same technician to the same building to fix another machine. We react to device and supply failures.
Now, in the same scenario with predictive analytics, we would have been able to anticipate both device failures by analyzing usage data, environmental impact and metrics on failure occurrence. Instead of making two service calls, we could deploy the technician to proactively fix both devices, before failure. Furthermore, with consistent analysis of parts usage and installed base, we could forecast what parts will be needed and stock the service vans accordingly.
Predictive analytics uses multiple data sources (both structured and unstructured) to analyze individual device data in order to predict future events, such as device failures. It is already having a big impact on many industries, most notably automotive, energy production and jet propulsion. In these industries, firms are seeing downtime reductions of up to 45%, breakdowns being reduced by up to 75% and maintenance costs being reduced by as much as 25%. Notice these are all production and delivery cost savings. Usually when I ask firms how they are using predictive analytics, they reference someone working on customer retention, targeting or other revenue drivers, which are all good. However, we feel the greatest, and most immediate, impact is available from cost savings on manufacturing and fulfillment.
It’s tough to cover the full breadth of predictive analytics in one article. So what I have given you here is a 30,000-foot view. The key things I hope you took away from this are:
1. Predictive analytics is about moving from a reactive mode to a proactive mode.
2. Predictive analytics presents strong potential to drive revenue in a profit margin-pressured industry.
3. Predictive analytics is more than a basic linear model, SPSS analysis or algorithm. It’s a combination of tools that take data and turn it into decision-making insights.
4. Tools are important, but it is not just about tools. It’s about the skills and staffing in order to use these tools to drive impactful outcomes.
Predictive analytics is powerful, with the potential to radically impact the industry, but it is difficult to implement correctly. Engaging in predictive analytics means more than just having a basic SPSS or SAS software license. We encourage those evaluating implementing predictive analytics solutions to think about the factors that contribute to a successful engagement, such as industry knowledge requirements, data management practices and team alignment. For more information on examples of using predictive analytics, see our free predictive analytics white paper "What is 5% Worth?"
I would love to hear more about how your predictive analytics efforts are coming along. Feel free to visit our LinkedIn group, Predictive Analytics for Manufacturers, or to drop me a line. In either case, I wish you success on this incredibly fun and exciting journey.
I would love to hear more about how your predictive analytics efforts are coming along. Feel free to visit our LinkedIn group, Predictive Analytics for Manufacturers, or to drop me a line. In either case, I wish you success on this incredibly fun and exciting journey.
Edward Crowley is the president and CEO of Photizo Group, a global consulting and market intelligence firm and an imaging partner of IBM predictive analytics. For more information on using predictive analytics, visit www.photizogroup.com/predictive-analytics.
