How Smarter, Stronger Supply Chains Improve the Customer Experience
This Article originally appeared in Inbound Logistics on October 23, 2019.
Companies today are increasingly leveraging digital technologies like artificial intelligence (AI), deep learning, and machine learning (ML) to build smarter supply chain operations.
They are turning “big supply chain data” into real insights that improve efficiency, bring products and services to market faster, and deliver differentiating customer value, all key to remaining competitive in the new digital economy.
But, before organizations can start to leverage these technologies, they need to digitize and integrate their supply chain fully and then understand what degree of “big analytics” techniques can transform the way they manage their supply chain.
With end-to-end trace-ability of parts through the supply chain, in and out of global warehouses and customer sites, massive amounts of historical and real-time service parts data can be utilized.
Companies gain insights from their data by applying various ML modeling techniques to two areas: 1) predictive and proactive planning and 2) faster repair times.
Predictive planning and forecasting can present ongoing challenges due to the unpredictability of demand for service parts. For more accurate demand forecasting, domain expert parts planners and data scientists must collaborate to develop and supervise a data-driven digital ecosystem that uses deep analytics to identify and focus on variables, build predictive models, and generate plans for global inventory.
Generating plans without human input also reduces the time and resources spent on the front end of this process. Once done, parts planners need only to review and adjust them before approving. As the planning tool continues to “learn” from planner modifications and usage patterns, and as AI continues to evolve, companies can move to a fully autonomous planning tool, freeing planners to focus on more complex issues.
When product repair is needed, organizations want to make the process efficient and straightforward. A predictive repair engine can combine relevant data science and analytics to recommend what parts are required before a unit arrives at the repair depot.
Traditional operations process flows need to evolve into an answer-first model. The process flow also needs to accommodate repair paths that optimize current and future-state models. There are many ways to solve this. For one, piloting variations of these lines on small product sets lessens the impact and risks to customers.
Reverse supply chain data that comes from built-in system diagnostics, tech support workflow, hands-on diagnostics, defective part evaluations, and other sources also informs predictive analytics that identify the likelihood of failures and help quicken repair times.
As predictive technology learns from accurate recommendations, efficiency will continue to improve. Customers will benefit because you can anticipate their demands quicker and connect the supply chain network to react to the need.
They will also benefit from the predictive repair engine that learns from post-event failure analysis of parts, as this creates valuable information for product engineers working on next-generation systems.
Innovation and transformation are critical to staying competitive. Companies should apply that same innovation to the supply chain to deliver better customer experiences and outcomes.