The year 2020 brought about a collage of business lessons, none more potent than the importance of listening.
Let’s take a moment to appreciate what good listening is. Julian Treasure put it well in his Ted Talk, 5 ways to listen better:
“Listening means making meaning from sound,” but importantly, Treasure emphasized, “but we are not very good at it. We spend 60% of our time listening, but retaining just 25% of what we hear. An important technique we leverage is pattern recognition to extract that meaning.”
Sound is everywhere—even technology is talking to us through data and data feeds. With enormous volumes of data inundating us, we can’t rely on people to listen, capture all the information, and make the best decisions. There’s just too much and humans can’t process it. Fortunately, artificial intelligence (AI) and machine learning (ML) tools are available to help us identify the data to listen to and empower us to act on that data insight.
How can we apply this concept to service supply chain optimization?
Listening to your customers, subject matter experts, and other qualitative inputs have long been a best practice. With the abundance of raw data available to us, now more than ever, it is imperative to extract knowledge using AI/ML to listen to your data as well. This leads to understanding and improved decision making.
It’s not easy. There is a constant tone of machine chatter that can easily morph into useless noise without the ability to focus and hone in on what is most important.
Last year showed us how fragile supply chains could be. The stakes are incredibly high, demanding you make the best decisions quickly and decisively. We must get it right to ensure hospitals have life-saving equipment, that disaster relief organizations have mission-critical equipment, that high-tech OEM’s can satisfy skyrocketing demand, etc.
An optimized service supply chain will maximize your ability to delight your customer by ensuring asset availability. The value of an optimized service supply chain extends far beyond inventory planning and parts management.
With advanced data science, the future of service parts management is moving closer toward semi-autonomous planning (the combination of AI/ML and human capital).
Organizations with this strategic perspective of Service Parts Management are unleashing the power of AND: reduce inventory AND increase equipment availability; reduce aircraft on ground AND increase readiness; reduce carrying costs AND increase cashflow; AND ultimately delivering maximum customer satisfaction.
Servigistics has developed impressive industry-first innovations with AI, ML, big data, and IoT, thanks to an anchor in advanced data science. These innovations were profiled in the recent Service Parts Management State of the Art Benchmark Report from Blumberg Advisory Group.
“Servigistics’ introduction of advanced data science is well documented since 2006. An essential underpinning of the Servigistics offering is the application of advanced data science,” said Blumberg. “Servigistics’ forecasting, optimization, and analytics modules take advantage of artificial intelligence (AI), machine learning (ML), and big data analytics.” With respect to IoT, Blumberg continues, “Servigistics has also capitalized on PTC’s investment in IoT (ThingWorx) by creating a connected extension that harvests data directly from assets to improve forecasting and positioning inventory.”
With the right tools, you realize that solving complex service supply chain challenges does not require a Ph.D. in data science or any Ph.D. at all. With Servigistics, service parts planners and management listen closely to their service supply chain data, customers, service network, and equipment data. Listening well and taking decisive action ensures the best outcome for everyone.
Our innovations in AI, machine learning, big data, and IoT will optimize your service supply chain and unleash the full potential of your service business.