The rise of IoT in supply chain planning

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There’s no denying the Internet of Things (IoT) has taken hold of nearly every aspect of our lives. With the number of connected devices estimated to surpass six billion next year and more than 20 billion by 2020, the steady stream of data these devices are providing can easily crowd and clog your supply chain planning processes if you’re not prepared. Gartner Research Director Andrew Downard addressed the issue during his presentation at the Gartner Supply Chain Executive Conference by outlining three macro trends affecting supply chain planning, chief among them IoT. He defined IoT as a system of inanimate internet-connected devices linking the physical and digital worlds, and predicted that retailers engaged in IoT partnerships with major manufacturers will take significant market share from their competitors as early as 2018. When it comes to your supply chain planning, the data sourced from IoT-enabled devices lets you continuously sense, communicate, analyze and act.

Real-world examples

Examples of this already exist in the marketspace. Coca Cola’s Freestyle machines, which enable end consumers to select their own unique beverage flavor combination has already inspired the creation of new mainstream and traditionally distributed products. Take the launch of Cherry Sprite. The data coming from the Freestyle machines let Coca Cola see which flavor combinations were most often selected by people across locations and determine if there was any seasonality to it. What they found was a demand for cherry flavoring added to traditional Sprite. HP’s Instant Ink has transformed a section of the printer manufacturer’s business model into a subscription-based revenue model, and transformed part of its supply chain in the process. HP’s new venture uses smart printers that sense each drop of ink being delivered to a page so it can monitor ink levels and order and ship new ink cartridges before you run out. That means better forecast accuracy for HP, but also a change in how and where to handle distribution and logistics. Grocery store Tesco has altered its business by creating virtual grocery stores in subway stations in select regions. Commuters have the option to digitally browse for goods on virtual store shelves, selecting and purchasing products and then scheduling home delivery for a time that’s convenient for them—say 40 minutes later when their commute is over. All of these examples relate to the second of Downard’s macro trends—digital business.

Digital business

Digital business is about ideas and models that blend the digital and physical worlds. It creates a convergence of people, business and things. While the above real-world examples of how IoT is affecting supply chain planning were mostly related to retail or business-to-consumer (B2C), there are business-to-business (B2B) examples of digital business, as well. Lockheed Martin is building smarter airplanes, where engines can run self-diagnostics, alert flight crews to any issues and even schedule its own maintenance based on flight schedules and service bay locations. The same is happening in the trucking industry. One trend that is helping shape the reality of digital business is the notion of an IoT order button, the most notable example of which is an Amazon Dash button. Essentially, it overcomes the challenges of abandoned online shopping carts, which have sat at a staggering 70% abandonment rate for the past decade. That means seven out of 10 online orders will never be completed. Regardless of how or why this phenomenon occurs, IoT buttons are finding a way around it. These buttons allow users to order a select item instantaneously without the need to go through an oftentimes-lengthy online checkout process. In many cases, we’re talking about physical buttons. The infamous Staple’s ‘Easy’ button, which started out as a novelty item, will soon be able to actually place orders for you. There are currently close to 300 brands using Amazon Dash buttons, and early reports show they’re driving big business. Amazon estimates that more than 50% of its orders are now coming directly from Dash buttons, and Dash button orders have grown consistently by 70% per quarter. However, this new way of ordering is fundamentally changing how supply chain planning and execution works. In order to fulfill Dash requests quickly and efficiently, manufacturers need to rely on a network of distribution and retail centers to meet the need. That means before you jump into this new reality, make sure your supply chain planning is ready to handle IoT button orders. Downard suggests the following:

  • Choose low variability, high volume products with well-defined fulfillment pathways
  • Prepare for direct and indirect demand drivers across multiple channels
  • Use participation in IoT button programs to deepen the retailer relationship in other areas

With the advancement of IoT and digital business comes Downard’s final macro trend—algorithmic planning.

Algorithmic planning

Algorithmic business, powered by algorithmic planning, represents a future where supply chains can act and negotiate on their own. It’s the evolution of decision support levels, culminating in non-optional automation where smart machines and AI make the decisions and we abide by them. Here are the levels of decision support as outlined by Downard:

  • General information—give me the facts (use historical data)
  • Specific information—give me a suggestion (statistical forecasting)
  • Advisory guidance—help me as I go (automated alerts)
  • Opt-in automation—Do this complex task for me (run multiple forecasting models and recommend the best fit)
  • Automation that can be over-ridden—take responsibility for this task until I tell you otherwise (automatic re-order points for replenishment)
  • Non-optional automation—take responsibility for this task and don’t let me or anyone else interfere

But we’re not ready for machines to take over supply chain planning entirely. There’s still a divide between machine-led algorithmic planning, which at this point in time centers on sensing, analyzing and responding, versus human-led consensus planning. In human-led consensus planning, it’s about testing, learning and experiment. Cause and effect isn’t already known. To further bring machine-led planning to the next level requires a different skillset among your supply chain.

IoT supply chain talent impact

All three of these macro trends—IoT, digital business and algorithmic planning—are causing a shift in supply chain talent. More emphasis is being placed on algorithmic skills. That means delegating manual and simpler tasks to robots or machines, and investing in talent with a better blend of supply chain, IT and analytical skills. Downard suggests moving from having five demand planners to three demand planners, one data scientist and one data engineer. Your data scientist should have advanced analytics expertise and be able to solve data science problems. Your data engineer should help build data infrastructure and work to make data consistently accessible. But be aware the demand for these data scientists and engineers is already on the rise. If you want to start prepping your supply chain for the impact of IoT, you’re going to need to start recruiting and training this new mix of talent now.


Downard ended his presentation with some recommendations to get you ready for IoT in supply chain planning. They are to:

  • Understand the fundamental changes required for digital maturity in supply chain planning
  • Ensure supply chain planning is a strong, recognized player in your company’s decision to build digital business partnerships
  • Identify data that you can leverage in supply chain planning algorithms
  • Balance the skill set of your team to match what’s needed for digital business and algorithmic planning

How has IoT influenced your supply chain planning? Let us know in the comments area below.

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