Analytics-based lead prioritization
30% increase in service revenue
Analytics-based lead prioritization
Key use cases
A large industrial OEM realized a 3–5% margin increase on spare part deals, while increasing its deal-making speed 7 times, by implementing a real-time deal support tool
Case example 2 / 9
The company was aggressively looking to grow its spare parts sales. It started with its users and customers, mapping the pain points along the sales journey for both. It introduced an analytics tool to predict lists of parts each customer needed. The tool also valued over 10 non-price elements in real-time to help the sales team with negotiations.
By implementing digital real-time deal support, the company radically shifted its sales business model from transactional to value partnerships. It realized a 3 to 5 percent increase in margins on sales and accelerated its deal-making process speed sevenfold.
A large power equipment OEM launched an advanced analytics initiative to prioritize promising leads, resulting in a 30% uplift from service revenues
Case example 1 / 9
The OEM offered long-term service contracts along with one-off services to its installed customer base but struggled to prioritize promising leads. It launched an advanced-analytics-based segmentation initiative using data already on hand from customer accounts. The company bundled customers into three propensity-to-buy categories (high, medium, low) and sorted potential aftermarket offerings by the dollar value of the transaction.
The propensity-to-buy tool identified and prioritized customers for the sales team. It identified those with a high propensity to buy, followed by medium-rated customers. The tool eventually resulted in a 30 percent increase in service revenues while maintaining the manufacturer’s sales resources.
+30%
increase in service revenues
Real-time deal support
Real-time deal support
3–5% increase in margin
Analytics-based service contract optimization
Analytics-based service contract optimization
10% service contract EBIT improvement
Online spare parts platform
Online spare parts platform
40% cost reduction in sales back office
Analytics-based spare part pricing
Analytics-based spare part pricing
2% price increase per year
Demand forecast model for aftermarket equipment stocking
Demand forecast model for aftermarket equipment stocking
20% inventory reduction
Service part supply chain network optimization
Service part supply chain network optimization
+2% revenue
Predictive maintenance for service cost reduction
Predictive maintenance for service cost reduction
30–35% maintenance cost reduction
AI-based image analytics for residual value prediction
AI-based image analytics for residual value prediction
80% inspection cost reduction
A large aerospace company improved the EBIT of its long-term cost-per-hour service contracts by 10% by introducing an advanced-analytics-based approach
Case example 3 / 9
The company faced significant headwinds on some long-term cost-per-hour maintenance contracts. Higher than anticipated maintenance costs and customer fleet operations changes put pressure on its revenues and EBIT margins. To improve visibility and transparency, the company analyzed what was driving its contract performance and determined the most likely operational profile for each customer. Based on this analysis, it set up a dashboard to monitor expected versus actual contract performance and identified proactive actions to improve performance. It implemented specific contract performance improvement initiatives to increase the time on wing and reduce shop visit costs.
The aerospace company thus improved its service contract EBIT by 10 percent and captured an additional USD 40 million in favorable revenue recognition.
A leading machinery OEM launched an online spare parts platform resulting in 2% top line growth and 40% cost reduction in sales back office
Case example 4 / 9
A pulp and paper machinery OEM had a struggling spare parts business with low digitization levels. The company developed an omnichannel sales concept that fully digitized its aftersales and spare parts business. The resulting platform let customers directly request an RfQ (request for quotation) without need for assistance. Both back office and field service employees can use the platform.
The online spare parts platform resulted in a 2 percent increase in topline growth, a 40 percent cost reduction in the sales back office, and a decrease in the time to quotation from 14 days to less than a day. Besides this direct impact, digitizing more than 2,500 machines helped to enable future use cases like product optimization and lead generation.
~2%
production throughput increase
An aerospace supplier used data and analytics to identify the price increase potential of its spare parts, enabling an annual price boost of 2%
Case example 5 / 9
The organization employed data and analytics to improve its aftermarket profitability. The approach clustered spare parts that represented the bulk of the supplier’s aftermarket revenues. The clusters reflected the acceptability of a price increase from a customer perspective, and assessed a spare part’s criticality, the availability of third-party alternatives, and the part’s type of use.
Based on these clustering dimensions, the approach used a statistical methodology to cluster over 40,000 parts into seven groups. An analysis of each group identified the price increase potential at the part level for parts that, for example, had historically experienced lower price increases or had lower price-to-cost multiples than the cluster average. This enabled a yearly upward price evolution of about two percentage points, while maintaining customer satisfaction levels and eventually leading to a margin increase of 25 percent in three years.
A US distributor of aftermarket automotive equipment reduced its inventory 20% by implementing a demand forecasting model for its stocking items
Case example 6 / 9
The distributor had over 500,000 stock-keeping items (SKUs) and a privately-owned distribution network. It faced rising stock-out rates and growing inventory levels at its distribution centers. The company built a demand forecasting model for more than 100,000 SKUs, based on historical sales, inventories, and lead time data. Analysis enabled the distributor to identify the three major drivers of the increasing stock-out rates: forecast errors, vendor lead time variability, and purchasing system problems. The company optimized its safety stock calculations to account for lead time variability and added flexibility to the purchasing system to allow users to override system forecasts.
The new demand forecasting model helped the company cut inventory by
20 percent while significantly reducing out-of-stocks.
–20%
reduction of inventory
A global medical device manufacturer with a large services business reduced its service costs by 30–35% by implementing a predictive maintenance solution
Case example 8 / 9
The manufacturer had a large services business and wanted to use centrally captured parametric data to improve customer experience, reduce cost-to-serve, and support technicians. The company had service records available for more than 2,000 machines worldwide (approximately 10 years of data for each). It pooled and grouped the resolution codes associated with repairs, then prioritized them to build predictive maintenance and trouble-shooting models. The predictive maintenance algorithm identifies machine failure probabilities to within 90 days, while the troubleshooting models can predict the root causes of machine failure 80 percent of the time.
Using the algorithm in the request-for-service process to plan service visits, parts requirements, and advise technicians on the workplan, the manufacturer reduced service costs by 30 to 35 percent. The decreased machine downtime also resulted in a customer satisfaction uplift.
–35%
maintenance cost reduction
An automotive OEM reduced fixed inspection costs for off-lease vehicles by 50–80% by using AI-based image analytics to predict vehicle residual values
Case example 7 / 9
The carmaker replaced the labor-intensive inspection and residual value prediction process it used for off-lease cars with AI-based image analytics. The image recognition algorithm is based on a deep learning model that draws on image, vehicle, and meta data (e.g., car age, mileage, and configuration). The AI-based image analytics are fully automatable and improve the accuracy of the visual inspection and residual value estimation for used cars.
The company saw a residual value uplift of 1 percent and an overall fixed inspection cost reduction of 50 percent to 80 percent, as well as shorter standing times before the vehicles could be offered again.
–80%
reduction of fixed inspection costs
~3–5%
increase in margin on spare part deals
+10%
service contract EBIT improvement
~2%
price increase per year
+25%
margin increase in three years
Out-of-stocks
significantly reduced
+1%
uplift of residual value
A leading global automotive OEM used end-to-end supply chain network modeling and optimization to reduce its supply chain logistics costs for service parts by 20%
Case example 9 / 9
The automaker wanted to reduce its service parts supply chain inventory and logistics costs while maintaining its high service levels. Complicating matters, it had a very fragmented organization split into central and regional service parts supply chain responsibilities. The OEM first modelled and optimized its entire international and regional network. Then it used service-part-specific algorithms to meet customer lead time requirements in the new network. The company employed advanced inventory optimization software to segment, model, and reduce inventory.
Part of a larger supply chain transformation that also covered traditional areas like lean warehousing, the program resulted in a 20 percent logistic cost reduction and a
20 percent inventory reduction.
20%
logistics cost reduction
20%
inventory reduction
~7×
speed increase in deal making process
–40%
cost reduction in sales back office