In this blog post from IBM Marketing Manager, Mikhail Lakirovich, we hear about the travails of Mikhail's move to the Lincoln Park neighborhood of Chicago, Illinois and how easily avoidable this fiasco could have been. Mikhail then discusses the adoption of predictive maintenance & quality solutions and how they're already changing the way companies like the auto manufactures operate using specific examples.
Read Mikhail's blog here:
A few weekends ago I found myself finally returning to the city from a brief stint in the burbs – I was moving to my favorite part of Chicago – Lincoln Park. I did not have too many things on me, so I decided to rent a truck and bribe a few friends with pizza and beer to help me accomplish the task. The process was to be fairly simple: pick up the truck, load it with my possessions in Skokie, drive to Lincoln Park and unload in my new building.
As with all simple processes, there of course was a caveat; in order to avoid paying a few hundred dollars for an ‘official move’, I had to request the freight elevator in my Chicago building to be blocked for no longer than 2 hours. Given the amount of stuff I was moving, this seemed to be plenty of time. So, I booked the elevator from 10am-12pm with the intention to get the process over before the city and the roads got too busy.
Picking up the truck was not a problem. Neither was bringing the furniture down and loading it in the truck. To my surprise, the vehicle would not start. I immediately called the company and demanded for maintenance to come and fix the truck. An hour later (at exactly 10am), the maintenance guy arrived and after examining the vehicle and attempting to jump it, he declared that the battery was no good and would definitely not survive a trip to Lincoln Park. He told me that the truck would have to be towed and that I would be getting a replacement vehicle at no cost. That in itself was frustrating. But what bothered me even more was that now I was definitely out my $400 for the official move as there was no way I could get the move-in to my building done by noon.
The frustration of having a piece of equipment fail and witnessing a disruptive chain reaction that directly hit my wallet is something that plant managers, asset managers and all those who rely on functioning equipment to do business have to deal with on a daily basis. The key factor in my situation was that the problem was diagnosed AFTER it happened. If there was a way to prevent such problems to occur BEFORE they occur, many a dollar would be saved.
To minimize such domino effects, IBM has created a cross-industry, packaged software solution – IBM Predictive Maintenance and Quality – that can predict when and how an asset is going to fail.
The software solution includes data integration and analytics, as well as decision management technologies, so organizations can capture all of their data, garner insights and predictive outcomes from that information, and quickly and accurately make the right decisions at the right time.
The solution not only monitors asset sensors – it has the capability to leverage input from other sources such as environmental, facilities monitoring systems, and even text information, such as maintenance logs, but also tightly integrates enterprise asset management (EAM), such as IBM Maximo, and analytic systems capabilities to offer distinctive benefits across an organization’s enterprise. Additionally, this packaged solution includes data connectors, data schemas, predictive models, dashboards and reports - accelerating the time-to-value for organizations.
Organizations using IBM Predictive Maintenance and Quality will be able to:
Monitor, maintain and optimize assets for better availability, utilization and performance;
Gain better visibility into assets via real-time monitoring, mobile, decision management and predictive analytics capabilities;
Predict asset quality and identify poor quality parts earlier to optimize operations and supply chain processes.
Extend predictive analytics from the asset to its associated processes, such as quality and maintenance, inventory and resource schedules—and provide insights for both assets and processes.
Make critical decisions faster and more accurately. Real-time, interactive dashboards and reports allow you to quickly get different views and drill into information so when something changes; you know immediately and can take the recommended actions.
As an example, Honda wanted to better understand factors that have the greatest effect on battery performance and longevity. Since all-electric vehicles, EVs, rely solely on batteries for power and since the cost of an electric battery pack ranges from $10k to $20k per vehicle, organizations are heavily focused on battery performance and life. Honda R&D, a division of Honda Motor Company, can now gather and analyze near-real-time battery data from Fit EVs on the road in Japan and the United States. Analysis can identify which operating factors, such as road conditions, charging patterns and trip length have the greatest effect on battery life. Further analysis can help the automaker predict when batteries will need to be replaced in order to alert vehicle owners in advance. In future phases, Honda will be focusing on other car components, such as engines, and providing real-time information directly to the drivers.
Had this solution been available for me, I would have been made aware of it before I started to do the heavily lifting (literally) and would have avoided unwanted costs and time wasted. Had I had the ability to predict unwarranted asset failures, I would have had a smooth time space truckin’ to my new home.
Watch this webinar, which includes a panel from Forrester Research and IBM.