The IoT tsunami is creating a huge opportunity to streamline and drive waste out of a costly and largely inefficient post-sale supply chain. However, most organizations are at a loss when it comes to using IoT data to solve these challenges.
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A quick look at the 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 top and bottom lines. The post-sale supply chain fuels aftersales service, which accounts for up to 80% of core profits. Plus, 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 products, 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.
The IoT fix
Every day your products tell you, via connected machine log files, how to improve your business. IoT log files provide 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 — this voice of product data can have a significant impact on post-sale supply chain health and outcomes:
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 dispatch
When 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 triage
Inbound 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 found
When 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 costly remorse returns and NTF instances.
It’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.
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 turn what could be negative, costly events into positive experiences for your customers, and money-saving, profit-generating outcomes for your business.
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