What’s Wrong with Aftermarket Parts Supply Chain Planning?
This is actually a trick question. What’s wrong with aftermarket supply chain planning really has nothing to do with planning, but with management expectations. This is a case of management not expecting enough from their business.
There is a reoccurring conversation in the parts industry that suggests that demand and distribution in this space is so complex that companies need to settle for less – lower customer service levels, fewer inventory turns, more obsolete or stranded inventory, and ultimately less contribution to corporate profitability. The problem with this message is that’s simply wrong.
Perhaps 15 or 20 years ago there may have been some truth in it, but that is old news. New technologies have enabled far better approaches to planning aftermarket and service parts and far better results. Anyone who is still benchmarking themselves to the old standards is living in the past.
Let’s start with one striking example of a company that is “knocking it out of the park” with their aftermarket parts supply chain planning – Lennox Residential Systems. The Heating, Ventilation and Air Conditioning (HVAC) leader has all the attributes of an extremely difficult planning problem – millions of SKU-Locations, a complex distribution network, and lumpy and intermittent demand. Worse yet, their demand is subject to extreme winter and summer seasonality.
And yet, last year Lennox beat out every other manufacturer (not just every other aftermarket parts manufacturers, but companies from every industry, such as consumer goods) in CSCMP’s highest award for supply chain planning. For more details on their story, see my 2015 blog.
They did it and so have many others. What’s changed? In a word, technology.
Yes the “long tail”—low-volume items with unpredictable demand—makes it tough for parts manufacturers to deliver top-notch service levels across the multichannel sales and multi-echelon distribution networks. But the technology exists now to do it.
Yes, the sheer scale of the distribution networks and numbers of SKUs can be daunting, but the technology exists to tackle it. Look at UK-based automotive spare parts wholesale distributor Andrew Page. They are running a highly efficient nightly planning process encompassing more than 250,000 parts, 11 million SKU-Locations, and a two-level network including 51 warehouse depots. Last year they received a Motor Trader award for their “ability to deliver parts to its sites throughout the day and overnight is driving the best ever levels of availability to customers.”
In addition, more advanced developments are enabling all sorts of possibilities that didn’t exist a decade or two ago. In a recent report on aftermarket, Frost & Sullivan said that a new capability that is helping companies up their game is analytics. They say an automated, data-intensive approach is best practice in aftermarket. Demand planning informed by strong analytics capitalizes on data from order-line items, channels, and points of sale to help aftermarket firms solve their inventory and replenishment problems.
Frost & Sullivan says demand modeling that analyzes histories by channel and individual order line lets aftermarket manufacturers offer differentiated services based on a business strategy that can be tailored to each customer—and then stock and position inventory to match those individualized targets.
Also according to Frost & Sullivan, planning for new product introductions, substitutions, and end-of-life are also hot buttons in aftermarket supply-chain planning, along with vendor-managed inventory and repair and reverse logistics. All these challenges similarly can use a data-intensive analytics approach, so aftermarket manufacturers can plan more accurately and faster. Machine learning can also be employed to automatically process this data—especially crucial to managing seasonal fluctuations in demand and part supersession and replacement needs.
Multi-echelon inventory optimization is also a hot issue in this sector—manufacturers need to keep inventory and service levels in balance across the multi-tiered chain. Analytical, actionable insights can help productive planners reduce inventory, yet increase service levels. They also help lessen the bullwhip effect, where demand errors increase as you move upstream along the chain, like the oscillations of a whip increase along its length.
Frost & Sullivan cites the example of one company that used data-intensive supply-chain planning to increase service levels from 91 to 97% and lower inventory by 16%. Another firm realized a more than 9 percentage point increase in service levels along with a 10% reduction in inventory.