Predictive analytics is on the horizon for the total supply chain and the after-sales service landscape in an industry projected to be worth more than $9 billion by 2020. Predictive analytics — the ability to use data to predict future activities — enables real-time decision making and forethought on both strategy and performance. The proactive nature of this strategy is what will make it the next big thing in supply chain business intelligence.
Manufacturing Machine Learning in a Predictive Environment
Machine learning — a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed — is part of nearly every aspect of our day-to-day lives. Take credit card fraud alerts, for example. Machines learn your regular activity over time, so when a suspicious transaction is made with your credit card, the credit company immediately puts a hold on your account.
Data is the theme behind this growing trend in technology. Simply put, there’s more data available than ever before, so there’s enough data to fuel predictive algorithms.
Modern organizations are adjusting business practices in this predictive environment to adapt to changing demands. For manufacturers specifically, there are several areas where machine learning could positively impact their bottom line as well as the overall customer experience with predictive analytics.
Forecasting the Predictive Analytics Future
In the wake of these advancements, which industries are fertile ground for transformation through predictive analytics? The Internet of Things (IoT) and predictive analytics can deliver after-sales service value across innumerous industries, but they are most attractive in industries with part-intensive products that are field or depot repairable.
As technology platforms mature and the cost of implementing IoT and predictive analytics solutions decreases over time, manufacturers will begin to accelerate their pace of turning predictive analytics into strategic advantage. Below are three different areas of opportunity for predictive analytics to take hold.
Area #1: Predictive Demand Forecast
Efficient demand forecasting, which predicts future demand for products and parts based on past events and prevailing trends, is a key component of after-sales service success. With an accurate picture of demand, manufacturers can improve service after the initial sale of a product without having to raise costs.
Predictive technologies such as machine learning and cloud-based inventory management solutions eliminate overstocking and enable warehouses to work with each other — as opposed to each operating in individual silos — to meet demand. The ultimate results are high service-parts fill rates, high levels of product uptime with minimal risk, and increased customer service levels.
Area #2: Predictive Pricing
Many manufacturers today rely on pricing practices of the past, such as cost-plus models and Excel spreadsheets, to price service parts. Unfortunately, this leads to parts and products being sold at different prices in different locations — creating poor customer experiences as well as a missed profit opportunity for manufacturers. When using predictive algorithms to set prices for service parts, manufacturers need to be mindful of the different factors that can affect sales, including part location, seasonality, weather, and demand. With predictive capabilities, manufacturers can incorporate all these factors and others to automatically adjust prices based on what the market will bear.
Area #3: Predictive Maintenance
The break-fix service model is reactive and inefficient. Fifty percent of service attempts fail because the needed service part was not available when needed, which results in extended product downtime, lost revenue for the product owner, and an unhappy customer.
With IoT and predictive analytics, smart parts and sensors will detect when a part is about to fail so the manufacturer can determine when and where parts are needed, proactively routing them to a dealer or repair center instead of storing them in stocking locations around the world. This enables manufacturers to reduce excess inventory and costs, improve part fill rates, avoid the cost and disruption of unscheduled downtime, and, ultimately, maximize customer loyalty.
Optimizing After-Sales Service with Predictive Analytics
It’s no secret that the revenue and margins generated on a manufacturer’s service side of its business are attractive, especially when compared to the product side, making optimizing the service side increasingly important to manufacturers’ long-term financial performance.
Combined with the near-term financial benefits associated with service parts inventory optimization, this is driving companies across multiple industries to explore opportunities to increase the financial value from their service organizations. Predictive analytics is key to after-sales service organizations’ success.
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