Enterprises of all sizes have embraced the ability to make smarter decisions based on the analysis of clean, timely data. These days, data analytics is commonly used across many organizational silos—from marketing and sales to finance, and even the areas of risk and fraud.
Using analytics on data-driven systems throughout the entire value chain has indeed become standard best practice. But if you ask the average knowledge worker about the benefits of data analytics, you’ll probably get a response along the lines of its ability to analyze, acquire and retain customers as a function of marketing. And with all the buzz about enhanced marketing effectiveness, it’s easy to lose sight of the cost savings and process improvement associated with Operational Analytics.
Consider this: If an enterprise generates $10 in additional revenue via a new customer (assuming a 60 % profit margin) the company profits $6.00. However, if an organization realizes a $10 cost savings through operational analytics, they gain $10 in pure profit. A simple yet powerful example of the value of Operational Analytics, which contributes to both the top and bottom lines of an enterprise.
Infrastructure, in a technological and digital sense, created by Supply Chain Management (SCM) and Enterprise Resource Management (ERM) software, can be credited for ramping up the focus on Operational Analytics. Improvements in data science such as process mining (a method of analyzing business processes based on actual event logs,) has certainly added to the boost as well. In fact, process mining can be used to simulate the sequence of real-time events (complete with a time stamp) and alter the chain of events if need be, thus creating a stronger model of operational efficiency.
Over the past two decades, these technologies have allowed businesses to not only integrate and automate business processes more sharply, but also increase velocity and enhance customer relations.
Deploying analytics to enhance operational efficiencies can lead to the discovery and elimination of unnecessary costs. In fact, operational data analysis is often the key to identifying process bottlenecks and resolving them programmatically. Instead of repeatedly allotting time to troubleshoot operations, decision makers are now free to spend their time addressing strategic and tactical approaches to business management.
In fact, it’s fair to say that if SCM software is the first step in enhancing data-driven decision making, the next steps are Business Intelligence and Data Visualization. Analytics most certainly serves as the engine that drives the data-driven decision making triad.
Consider the case of a Montana-based LTL (“less than a truckload”) shipper, who wanted to improve its scheduled delivery rates by analyzing traffic and weather data. “Severe weather conditions are an everyday part of life in the northwestern region,” stated the owner. “My carriers are often at great risk, as they strive to provide on-time, day-definite delivery service.”
Utilizing operational analytics that provided instantaneous reports on a specific region’s traffic and weather patterns, helped this operation to avert severe weather and road conditions and dramatically decreased crash rates. For this small business owner, the results were deemed “priceless.”
Consider also the case of a mining company that utilizes thousands of dollars in trucks and extractors and has amassed a treasure-trove of data—including volumes of information regarding fuel usage, truck load allowances and repair history.
By plugging that data into an analytics framework, they’ve been able to optimize the entire operation, simply by pinpointing patterns and ascertaining risk assessments, such as equipment failure and the associated costs.
Oftentimes, in a mining operation, it’s not a sole component or KPI that predicts failure, but rather a compilation of load size and daily equipment wear and tear; thus coming up with a multitude of leading indicators to base predictions upon proves useful. For example, small items such as metal parts and the amount of stress they can endure before they become insolvent has provided this enterprise with several thousand dollars in productivity gains.
If that sounds excessive, consider the cost of having an excavator malfunction in a field and being out of commission for a week; or perhaps the cost of losing a hauling truck for the same duration. Both setbacks to a week’s work could translate to thousands of dollars in lost productivity.
With the use of predictive analytics, both disasters are now easily averted. “Taking our data from a wide variety of sources and ‘crunching it,’ if you will — has truly improved our entire enterprise,” states the Operations Manager. “Being able to make detailed predictions regarding key, specific pieces of equipment in real-time has proved essential to optimizing our operations and preventing disaster. It has certainly been worth the investment.”
What are your thoughts? Do you believe that more agile Operational Analytics can free up your time for higher value-add activities? From our experience and the case studies above, we’ve certainly seen evidence that data analytics is the key to correcting issues upfront, and enabling smarter, more fluid operational processes—we certainly welcome your thoughts on all aspects of this discussion.
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