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Leveraging IoT Data for the Post-Sale Supply Chain:A Framework for Connected Product Analytics & Actions

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Leveraging IoT Data for the Post-Sale Supply Chain:A Framework for Connected Product Analytics & Actions

Everybody’s talking about the Internet of Things (IoT), and with good reason – it’s all around us. Every day, 5.5 million new things get connected, and by 2020, 20.8 billion connected devices should be in use around the world.1 This connected tsunami is creating a huge opportunity to enhance the service supply chain by tapping into machine data. But while there’s a general sense of excitement around connected device potential, nobody really knows what it means for their business. Or how to leverage connected info in a way that substantially impacts your top and bottom lines.

To understand how to do this, it helps to first examine the post-sale supply chain challenges that IoT data addresses.

Post-Sale Supply Chain’s Daunting Challenges

How efficiently and intelligently you handle everything from service triage and parts shipments through inventory carrying and reverse logistics, can make an enormous impact on your business’ top and bottom lines. The post-sale supply chain fuels aftersales service, which accounts for up to 80 percent of core profits.2 In addition, in the face of an increasingly globalized economy and commoditization of many product types, post-sales service, if done right, can provide a much-needed competitive differentiation.

The challenge is, the post-sale supply chain is highly complex with many moving parts and stakeholder interdependencies. Consider the complexity of trying to service millions of products in the field, each aging on a different clock, in constant customer use, with a high variation of service agreements and supporting a service vendor ecosystem all running on their own SLAs, process and systems. Exacerbating this is the fact that most companies lack the data, visibility and insights needed to optimize the service supply chain. That’s one reason problems like No Fault Found / No Trouble Found are so persistent, comprising 68% of returned consumer electronics products3, for example. It’s also why companies often find themselves in crisis mode when it comes to shipping replacement parts for failed products and scrambling to address inventory stock outs: downtime can cost end customers millions of dollars. As a result of all this, companies unnecessarily spend many millions of dollars annually and leave just as much profit on the table.

The Internet of Things—also sometimes referred to as the Internet of Everything—can help turn all this around.

The IoT Fix

You probably never thought of your products as talking, but they do. All the time. It’s called Voice of Product (VoP), and it tells you every day, via connected machine log files, how to improve your business.

IoT log files provide you with detailed information about what’s happening with each product in the field, pinpointing current and potential issues with software, infrastructure capacity, configuration, hardware and more. When analyzed alongside other critical post-sale supply chain data—including Voice of the Customer, Voice of the Process, real-time and historical operational data—VoP can have a significant impact on your ability to predict machine failures so you’re better equipped to handle, and even avert, issues; shift from reactive to proactive modes across post-sale supply chain functions; and improve overall supply chain health and outcomes.

WHITE PAPER: Leveraging IoT Data

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Service Parts Inventory to support Parts Dispatch For the last 50 or so years, the mathematical models supply chain planners have used to calculate inventory requirements have been based on factors such as past demand, variations in demand, the amount of stock in the market and lead time from suppliers. This time-series approach, although standard throughout the industry, has proven less predictive and reliable than companies would like. To combat this, many of them overstock inventory so that when customers’ products break down, replacement parts can be readily available. But purchasing and storing all that extra safety stock is very costly.

Through joint research with OnProcess Technology, Massachusetts Institute of Technology recently developed a new model for spare parts forecasting and inventory planning that incorporates machine failure predictability into the equation. The study found that by using IoT data, you can significantly reduce both costly inventory stock and stock-outs — even with relatively low predictive power. The higher the failure predictability, the greater the reductions. This also enables businesses to improve their ability to meet service levels and, in the process, save millions of dollars every year.

Transportation Order Management to support Parts DispatchWhen products fail, vendors rush to ship spare and service parts. By general contract or business practice, parts are likely to be sent via costly Next Flight Out, Same Day or Two-Day transport. However, imagine if, instead of waiting for failures to happen, you could monitor the product’s log files to predict why and when a part is likely to break down. With this knowledge in hand, you can inform the customer of the pending problem and proactively ship a replacement part via slower and less-expensive means. Not only will this reduce transportation and process management costs substantially, it improves product uptime and, as a result, customer satisfaction.

Service Chain TriageInbound calls are the most reactive and least customer-friendly way of dealing with product problems. IoT data can help reduce inbound calls while increasing the use of more proactive and cost-effective means such as outbound calls and self-service portals. IoT opens a range of options from self-service, no touch, low touch, proactive outreach and premium services.

By sharing insights gained from a product’s log files directly with the customer via a portal, you’re providing the intelligence they need to resolve common problems themselves, and offering what is often a faster and preferred method of resolution.

For an even more proactive approach that also facilitates upsell opportunities, you can program log data to trigger alerts, telling your outbound calling representatives, for example, that a particular customer’s product has a part that needs attention. The rep can then contact the customer to suggest remedies such as shipping an advance replacement part, upgrading the product or offering a premium support service.

Remorse Returns / No Trouble FoundWhen customers complain that products either aren’t working and need to be fixed/replaced, or aren’t performing as expected and, therefore, don’t fulfill their needs and should be returned, IoT-enhanced analytics can signal whether or not there’s an actual problem—before anything is replaced or returned. If the IoT data doesn’t turn up any issues, then it’s likely that the cause is a gap in education, where customers weren’t adequately informed during the point-of-sale or simply misunderstood or forgot how to use the product. By having your representative explain functionality and clarify services, you can avoid many remorse returns and NTF instances.

Reverse LogisticsIt’s standard procedure in reverse logistics to send returned products to a central receiving location, where they’re evaluated for repair, inventory or scrapping. Diagnosing each product’s problem can be time-consuming and delay the inevitable next steps. IoT data can accelerate this process.

By leveraging IoT-enhanced analytics, based on the installed equipment’s log file the case can be flagged as repairable or not repairable, before the product is returned. This enables you to eliminate the central diagnostic step, skip the receiving stop and route the product to the appropriate location right away. As a result, you can reduce reverse logistics costs, deliver parts to inventory faster and, when needed, introduce local control on material scrapping to reduce unnecessary repair and transportation costs.

Here are a few examples of how IoT data can be used to improve your post-sale operations:

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Framework for IoT-Driven Analytics & ActionsMany companies that are eager to listen to and leverage connected machine data, struggle with how to do this. Where do you start? How extensively should you incorporate it throughout your service supply chain? How can you tell if it’s benefiting your process?

The first thing to know is that, on its own, product log data has limited value. It’s only by making it part of your overall post-sale analytics that IoT data enables game-changing improvements. With this in mind, and leveraging OnProcess’ expertise in service supply chain and machine analytics, we created the following framework for integrating IoT-based log files into our clients’ service supply chain. To give you a sense of how this proceeds, we include timeframe guidelines for several of the steps, however, depending on the complexity of your project or case, timeframes will vary.

Review Baseline Performance:Approx. timeframe = 2 – 4 weeksTo begin, figure out the current failure rate for the product in question. Even if the information you have isn’t complete, it’s important to capture the best available data.

Review Remedy Options Identify the range of possible solutions for fixing typical problems (i.e., upgrade the product, swap out offending parts, update the firmware) and their approximate costs.

Collect IoT Data: Approx. timeframe = 1 monthThis entails making sure you have access to the necessary raw machine log files, that you understand the data definition, and have the ability to pull it into your systems along with other raw data, such as Voice of Customer.

Select Target Segment: Approx. timeframe = 1 weekSegmentation is critical in supply chain analytics. As Gartner notes, “Segmentation helps you handle complexity more efficiently and effectively by targeting specific differentiated outcomes and creating a portfolio of approaches optimized to deliver them. As the complexity within supply chain increases, the importance of segmentation to enable profitable growth increases.”4

To keep your IoT analytics project from being unwieldly and improve its success, start in a focused way. Pick a target segment, typically a combination of the product plus geography, type of failure mode, post-sale function (i.e., inventory, transportation, service triage, etc.). Treat this as a sort of Proof of Concept, which you can then fine tune and make repeatable so that you can later leverage IoT analytics in other segments.

Determine Preliminary Required Predictive Power:Approx. timeframe = 2 weeksMake an initial estimate of how much predictability is needed in terms of pending product failures and its relative risk/reward. For instance, if the chance of a failure for a particular part used in airplanes is 1 in 1,000, and replacing it costs $10,000, because of its critical nature it would still be worth monitoring/analyzing the part’s log files, sending advanced replacement components when appropriate and having the customer take the plane out of service to fix it. But if it’s a disk drive used in non-essential applications that fails 1 in 1,000 times, and it costs $150 to fix and replacing it would disrupt the user for two days, it may not be worth it.

CollectIoT

Data

SelectTarget

Segment

DeterminePredictive

Power

Review Baseline

Performance

ReviewRemedyOptions

P L A N N I N G

Configure Models: Approx. timeframe = 4 – 6 weeksThe key to success is configuring a model that integrates machine log file-based failure predictability with other key data points previously mentioned, i.e., Voice of Customer, historical and real-time operational data. Develop a model that is rigorous, standardized and repeatable so that you can operationalize it. Otherwise, you’re in danger of having to recreate the wheel for ad-hoc projects, which is not only extremely time-consuming, it’s also less accurate because these ad-hoc models haven’t been repeatedly tested and vetted.

Transform IoT Data and Generate Probabilities This is where you translate, or transform, massive amounts of unstructured log file data in order to generate aggregate failure rate probabilities by failure mode. To the untrained eye, it may seem like a firehose mishmash of data points. Specialized analytics resources and personnel are required to produce accurate, reliable information.

Execute and Monitor Action Plans Based on defined probability-based outcomes, develop actions for target segments. At the micro level, this requires having broad and granular visibility into the all the data points, and funneling

the data into an alerting system so that, when defined rules are triggered, it specifies certain actions that should be immediately taken to avoid, alleviate or fix a particular customer’sproduct-related problem.

At the macro level, you can use predictive analytics related to connected machine failures in the aggregate, to inform a variety of supply chain and general business functions. These may include conducting proactive outreach campaigns to targeted customer segments with likely-to-fail products and offering advanced shipment of replacement parts; making changes to product roadmaps to avoid future problems; and creating premium customer support services.

Monitor and Evaluate ResultsWhenever you introduce new data or new models, it’s important to make sure the results you’re getting are attributable to those changes, rather than to other random data points or circumstances. With this in mind, it’s recommended to conduct causal effect analytics, or randomized control trials, to ensure your results stem directly from your use of connected product log files.

The more visibility you have into what’s happening with your connected products in the field, and the more you integrate those log files into your post-sale analytics processes, the better you’ll be able to predict product failures and turn what could be negative, costly events into positive experiences for your customers, and money-saving, revenue-generating outcomes for your business.

To find out how OnProcess can help your business leverage IoT data to optimize your post-sale supply chain, call us at 508-623-0810 or visit www.onprocess.com.

1 Gartner, Inc., November 10, 2015.

2 Everest Group 2015, “Aftersales BPO: Tapping into the Strategic Value of Service After the Sale”

3 Accenture, “A Returning Problem: Reducing the Quantity and Cost of Product Returns in the Consumer Electronics Industry”

⁴ Gartner, Inc., “Use the Outcome Segmentation Model in Supply Chain to Communicate Design and Benefits,” March 6, 2015

Monitor andEvaluate Results

I M P L E M E N T A T I O N

Execute andMonitor Action

Plans

Transform IoTData and Generate

ProbabilitiesConfigure Models

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508.623.0810 | sales@onprocess.comOnProcess Technology, 200 Homer Avenue, Ashland, MA 01721

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