Part 2 in the 3-part “From Old to New with the Digital Supply Chain” series
I was thinking recently about the old adage attributed to Peter Drucker: “If you can’t measure it, you can’t improve it.”
Some certainly regard this as a bit reductionist. What about serendipity and all the intangibles common in any business made up of actual living people? Fair enough. But I think it’s safe to say that when it comes to achieving the goal of total visibility for global supply chains operating in the digital economy, Drucker’s insight stands up.
In today’s era of Big Data, however, the challenge isn’t so much finding metrics and KPIs to track. To the contrary, with the Internet of Things (IoT), sensors everywhere are sending out data almost all the time. There are sensors in the shoes I wear, the car I drive, and the watch I use. In industrial settings, it’s even more pronounced. Pumps in chemical refineries, HVAC units in factories, anything that moves through inventory – all of these have sensors that feed out data.
No, in today’s environment of data overload, the overriding challenge companies face is quickly and effectively analyzing the data they pull in.
From Big Data to intelligent analysis
What’s needed, then, is an industrial-strength data platform that can take device data in, process it, analyze it, and feed business decisions back into processes through apps and automated actions.
But, of course, IoT data is not the only data source of concern. As I mentioned in the first blog in this series, companies today also want data from social media, points of sale, and even third-party sources that track weather, economic conditions, market trends, and more. If your goal is a more customer-centric supply chain, you need to pull in all of this data.
The challenge, however, doesn’t stop there. While incoming data from all sorts of sources may be useful on its own, true value is achieved only when this data is mixed with data from internal business systems – in real time. Thus, your data platform needs the ability to hold live and historical data together (either in memory or in data lakes) where it can all be analyzed in the moment. This is what helps increase responsiveness.
From responsive actions to predictive analytics
Not that responsiveness is the end all and be all, though. With real-time data, you can go one step further with predictive analytics.
Here’s the challenge: While management by exception is well and fine, many supply chain managers today find themselves in a sea of red – where alerts are thrown for any and all out-of-bounds conditions.
- “The machine has failed”
- “The delivery is late”
- “The quality on line 1 is below acceptable”
- “The products in the freezer have melted”
If any of these alerts sound familiar, you know all too well the hassles of endless firefighting. But with a solid foundation of data forming a single version of truth for your supply chain needs, intelligent technologies can now be applied to lend a helping hand.
Machine learning, for example, can be applied to automatically analyze large volumes of supply chain data, recognize patterns, and yield insights that help you predict what will actually happen – all using real-time insights into the assets in the field, trucks on the road, weather forecasts, or whatever. Think of the possibilities:
- If I fix that machine before it breaks down, I can reduce the cost of maintenance and minimize disruption to the customer
- If I see that a delivery is late, I can reroute another delivery to maintain satisfaction levels
- If I sense that products coming off the line are trending in the wrong direction, I can make adjustments before things get worse
- If I am told that a freezer temperature is trending up, I can adjust or repair it before the products turn to mush
This is what total supply chain visibility can enable.
From linear supply chains to an agile ecosystem
Yet there’s more. Yes, a solid data foundation that yields total supply chain visibility can be used to respond to supply chain events faster and even avoid problems with greater predictive power. But more importantly, it can help give you the supply chain agility required to deliver what customers want – and even seize new opportunities faster.
The simple fact is that value chains are increasingly complex today. To deliver what customers want, companies need to be firing on all cylinders – with all teams working together toward the common goal of delivering great experiences for customers. This requires collaboration – not only internally (across sales, marketing, R&D, planning, manufacturing, logistics, etc.) but also across partners such as customers, suppliers, logistics service providers, and contract manufacturers.
As assets and products get “smarter,” we are also seeing the growth of asset networks that share information about their state, location, and performance with one another and the business systems to which they’re connected. This provides a bigger universe of asset data to draw on – which allows machine learning and predictive analytics technologies to detect even more patterns for greater insight.
Add to all of this the rise of cloud-based business networks. These networks allow organizations to discover new supply partners quickly and onboard them in a flexible manner. If you detect new opportunities in the market, you can use the collaborative capabilities of business networks to pull together new supply chain partners and seize the opportunity at a pace previously impossible. When supply chain people talk about agility, this is exactly they mean.
Join an interactive session featuring Jeff Hojlo, Program Director, Product Innovation Strategies at IDC, and Hans Thalbauer, Senior Vice President, Digital Supply Chain & Industry 4.0 at SAP, to get inspired about how best-in-class companies are reinventing their supply chain. Register here.