Service parts planning can be a head scratcher. Aftermarket supply chains usually require multi-tier distribution networks, lots of part numbers, and intermittent demand. Traditional supply chain planning solutions don’t address this “long tail” problem of small quantities and infrequent orders, so it’s especially difficult to maintain high service levels across differentiated channels.
The Harvard Business Review says, “An after-sales network has to support all the goods a company has sold in the past as well as those it currently makes. Each generation has different parts and vendors, so the service network often has to cope with 20 times the number of SKUs that the manufacturing function deals with.“ A huge parts portfolio is an inventory management challenge in many automotive, industrial and HVAC equipment and electrical/electronic components.
But this challenge is also an opportunity, says IDC Manufacturing Insights, in a paper by Heather Easton titled, Business Strategy: Spare Parts Planning for Service Excellence. “As manufacturers continue to competitively differentiate themselves within various industry settings, aftersales service will increasingly become more important in the overall business and profitability strategy. Many discrete manufacturers can expect to capture upward of 30% of revenue from service and service-based product strategies.”
“Aftersales spare parts are driven by highly variable and often unpredictable demand patterns that call for more non-deterministic, stochastic planning methods anchored in probability methods,” says IDC. These new methods, sometimes referred to as “demand modeling”, provide additional ability to forecast intermittent demand and optimize multi-echelon inventory.
They can also improve downstream demand visibility from a distribution or service network. By connecting to the dealer network or service providers, they can identify sell-through demand down to the daily level.
IDC also points to the proliferation of mobility—field and service management technicians, third-party contractors, or partners seeking the status of parts inventory or trying to determine the correct part to utilize. “Many [solution] providers indicated current or planned future support for mobile-based devices including tablets and smart phones,” IDC says. For example, ToolsGroup’s Demand Collaboration Hub (DCH) uses mobile devices such as hand-held tablets, allowing service providers to add field intelligence.
This concept of leveraging data closer to the end point of demand can be taken a step further via the Internet of Things (IoT). IDC says “smart, connected products… will provide meaningful impacts in the automatic sensing of spare and consumable parts demand, the monitoring of equipment performance, and the tracking and managing of service parts inventory. In one current-day example, a ToolsGroup consumer goods manufacturer has begun employing telemetric demand sensing of field-deployed dispensing machines.” This manufacturer downloads data from 3500 kiosks every 15 minutes.
Another service parts planning supply chain innovation is machine learning. Also called “cognitive learning”, it can solve very large problems that contain lots of data, such as sifting through millions of SKU-Locations to automatically identify “clusters” with similar demand or seasonality profiles. Other examples include diagnosing failure rates from operating hours data or identifying repair causality from installed base statistics. “An increasingly popular path for enabling higher levels of service management innovation is in leveraging information discovery, smarter data, and predictive analytics,” says IDC.
Next week we’ll focus on a specific case study of a company dealing with a complex aftermarket parts planning environment.
For a short Executive Brief on service parts (aftermarket parts) planning, click on the image below: