Machine Learning Can Increase Your Profits
By Tim Askins, InterKnowlogist, Lead Software Engineer
Artificial Intelligence and Machine Learning are in the news all the time. But what do you think of when you hear those buzzwords? Maybe you eagerly anticipate the relaxation and safety of self-driving cars, the power of a smart automated personal assistant, or the pleasures of a Westworld amusement park. Perhaps instead you fear mass loss of jobs to automation, a struggle to remain useful to society, or the inevitable disaster when those Westworld robots become truly sentient (spoiler alert…). As you await one or both of those futures, it’s likely you aren’t aware of the amazing things Machine Learning can do for your own business, right now. Regardless of your industry, chances are you can increase your profits and improve your customer satisfaction using technology already available. What’s more, you don’t have to displace workers to reap the benefits.
Machine Learning is all about making predictions -finding patterns in past data to anticipate behavior in the future.
Artificial Intelligence is a blanket term that covers a wide range of capabilities. While autonomous cars and Go-champion bots make the sexiest news stories, it’s the subset of AI called Machine Learning that can most directly affect your earnings. Machine Learning is all about making predictions – finding patterns in past data to anticipate behavior in the future. You might be wondering how this has anything to do with your business. “What would we even try to predict?” you might ask. So many of our business processes are taken for granted that it’s often hard to see how additional information could improve them. An example will make things more clear.
Predictive Maintenance allows businesses to use Machine Learning to anticipate failures…
At InterKnowlogy, we find a particular application of Machine Learning especially useful. Predictive Maintenance allows businesses to use Machine Learning to anticipate failures in their products before they actually occur, such that preventative maintenance can avoid the failures altogether. Imagine you are in the business of installing and maintaining elevators for commercial buildings. You win contracts and justify your rates by promoting the superior reliability of your elevators compared to your competitors. Minimizing the downtime of your elevators is key to your sales. To this end, you’re already using predictions, even if you’re not thinking of it in those terms. You’ve established regular maintenance schedules, you keep an inventory of parts with long lead times, and you adjust staffing based upon anticipated usage in a given area – all based on predictions. You’ve gotten a sense of how long the elevators can go before adjustments are needed, of how frequently various parts break down, and of how usage affects the time until a part failure. Businesses have been making these types of predictions forever. But these are only rough, macro-level predictions. Failures still occur, and although regular maintenance may reduce their frequency, and smart management of part inventories and proper staffing may minimize repair time, downtime is still all too common.
…excise flaws with a scalpel rather than a sword.
Machine Learning allows you to make micro-level predictions, to excise flaws with a scalpel rather than a sword. Predictions become so precise, so accurate, that it becomes efficient to replace parts before they break, in machines that to human eyes appear to be working fine. If you were told that a particular part in a specific elevator would fail within the next three days with a 99% certainty, for example, you may choose to replace the part proactively. Although there was a 1% chance the replacement was unnecessary, avoiding an almost certain downtime event may be well worth the cost.
…indicators of imminent failure are typically elaborate interactions … too complex to be discerned easily by human inspection…
How are such accurate predictions possible? Two factors are responsible. First, machines now routinely report immense amounts of data over the internet, often referred to as IoT (“Internet of Things”). This typically consists of very low-level performance measurements, such as the temperature and vibration of motors, the speed with which doors open or close, or even the sound being produced by mechanical systems. These data samples are reported frequently and continuously, resulting in a tsunami of data that would overwhelm any human analysis but enables computational modeling. Second, the indicators of imminent failure are typically elaborate interactions between numerous elements within the collected data, patterns too complex to be discerned easily by human inspection, but discoverable by Machine Learning algorithms.
…Azure allows us to design elegant … Machine Learning solutions.
Imagine the complexity of the infrastructure necessary to gather and store massive amounts of data, to find hidden predictive patterns in that data, and to exploit those patterns to predict failures in new incoming data. This can sound a bit overwhelming! At InterKnowlogy, however, our extensive experience with Microsoft’s cloud framework (Azure) allows us to design elegant custom Machine Learning solutions. Our customers don’t need to concern themselves with the details of the architecture, and can instead focus on the simple predictive summaries we present in clear graphical dashboards. A quick glance reveals any anticipated problems that need to be addressed, along with the urgency of such predictions.
A particularly nice aspect of Predictive Maintenance is that the benefits are not gained at the cost of lost jobs or reduced wages. Technology is not replacing workers in these scenarios, but rather enabling them to be more efficient and accurate. This is truly a win-win scenario; the only losers are your competitors who fail to leverage the benefits of Machine Learning.
Predictive Maintenance is just one of many Machine Learning scenarios, and you don’t necessarily have to have access to IoT data to take advantage of what Machine Learning has to offer. Other applications of Machine Learning include detecting unusual or suspicious activity, classifying instances of data into known categories, and making recommendations. We’ll help you discover the unique ways in which predictive solutions can optimize your profits and customer satisfaction. To find out how Machine Learning can help your business, contact us at firstname.lastname@example.org.