Parts and service demand follows the machine parc - the population of machines already operating in a territory. Monthly, model-level market data shows which machines entered which zone and how that fleet is ageing, so parts teams can hold the right parts in the right place, plan service capacity and provision for warranty claims. Internal sales data covers your own brand; industry-wide data adds the machines you did not sell.
Ask where market data conversations start and the answer is nearly always the sales team: who sold what, where and how the shares compare. Yet the machine sold today becomes a service obligation tomorrow, and the teams that carry that obligation - parts, service and warranty planning - have just as much to gain from the same numbers. Aftersales planning is production planning and forecasting with market data applied to the machines already in the field. The signal is different, though. New machine demand follows the economy and the projects underway; parts demand follows the machine parc, the population of machines physically working in an area right now. Our customers tell us that without that visibility, parts and service teams are left flying blind.
Parts demand runs on different signals to new machine sales
New machine supply is planned a long way out. The typical horizons we see run from eighteen months to five years ahead, underpinned by supply chains that add months between placing an order and receiving the machine - shipping, customs clearance and, if the factory holds no stock, manufacturing lead times on top. The size of that opportunity is driven by the health of the economy and the projects underway in a market.
Parts and service demand works differently. Parts serve machines that have already been sold, so the key indicator is not the economic outlook but utilisation: what is operating in the territory at this point in time. That makes the machine parc the fundamental lead indicator - and the parc can move. When a large construction project finishes, it is not uncommon for the whole fleet to be shifted off-site to another territory or another country as the contractor sees fit. Fleets held by rental businesses, or stationed where construction is planned for years ahead, stay put; large infrastructure fleets come and go.
So parts planning cannot simply borrow the new-sales playbook. The metrics are different and the lead indicators are fundamentally different. What a parts team needs to know is not just what sold, but what is operating here now and how long it is likely to stay.
The machine parc is an age profile, not just a map
The machine parc is the population of machines operating in an area: which models, in what numbers and of what age. Monthly, model-level market data is what turns a sales map into that age profile. When you know which model entered which zone in which month, you can watch each cohort of machines mature - and the parts mix matures with it.
Machines consume different parts at different ages. In their first few years they need breaking-in parts; as tolerances wear down, older machines may need parts made of different compounds or built to different technical parameters to compensate for the wear. As independent consultant Chris Bartlett, who ran aftersales across several OEMs, explains in his interview on trust and OEM-dealer relationships, knowing where machines actually are shows a parts team where to hold parts - and a machine that is one to five years old needs different parts to one that is five to ten years old.
Warranty planning reads the same profile through a liability lens. Some warranty items are known high-use, frequent-change parts. When a cohort of new machines lands in an area, a manufacturer that can see it happen can provision those parts before the claims come online rather than after. And we regularly hear the same tension from distributors: dealers would like the distributor to hold far more parts than a balance sheet can carry. The fewer parts a distributor holds, the better for its balance sheet - provided they are the right parts, in the right quantities, in the right place. Machine parc visibility is what makes "fewer but right" a plan rather than a gamble.
The cross-brand signal internal data cannot show
For a brand's own machines, internal data does the job. A machine cannot generate parts demand before it is delivered, and once commissioned it will usually run relatively trouble-free for a while - which gives the local dealership a runway to order the right parts. What matters most inside a brand is that the geographic picture flows between departments, so parts and service teams know how many machines operate in their region. In our experience that sharing happens willingly: we have not seen sales teams guard market data from their parts and service colleagues. Where the data does not flow, the gap is usually awareness that monthly, model-level data exists at all - once a parts team understands what it would sharpen, they ask for it and get it.
Market data earns its keep on the machines you did not sell. Machines are exported, moved between territories or sold outside their usual marketing areas, and they sometimes land in a zone where their own brand has no dealer. In projects where the reporting model gives participants brand-level visibility of results, such as open reporting, a local dealer can see that machines from other brands have been sold into their zone. There is no local service agent for those machines - but there is a local dealer who can match part numbers from their own catalogue and supply cross-usable parts. Knowing the machines are there, that dealer can make a data-driven assumption that some of that servicing will come to them, and hold inventory accordingly.
That is also the honest answer on signal speed. For your own brand there is no lag for market data to close - the sales numbers already sit inside the organisation. The signal that does not exist internally is other brands' machines arriving in your territory, and it only arrives if the industry shares its data.
What a parc view changes on the ground
Consider a scene most parts managers would recognise. The shelf holds four filters of a particular type, and a customer asks to buy all four - he has two machines and would rather hold the spares himself, just in case. The natural instinct of a salesperson is to sell. A parts manager who knows the local parc can do better: sell two, keep two for the next machine that breaks down nearby, and immediately place a back order - telling the first customer the other two he wants are already on their way. The bulk buyer keeps his security, the breakdown customer gets a part on the day and the shelf recovers within weeks. That call is only a wise one if you know what machines of similar specification are operating in the area.
Proximity is a cost decision the parc informs too. In most countries, domestic logistics are good enough that having the part in the country is sufficient - one strong distribution centre, with urgent parts couriered overnight to almost any site. The argument for holding parts closer bites in very large territories: in a market the size of Australia, a machine idle on a major construction site while a filter travels from Perth to Sydney is expensive downtime, and each hour a machine does not work is revenue lost. A zone-level parc view helps decide where that extra proximity is actually worth paying for - the same logic as positioning inventory with market data, applied to parts. When the part is close, the person who wins is the machine owner, back to work sooner - the kind of benefit that flows through to the end customer when an industry shares its data.
Technician planning follows the same logic, with limits. A spike in sales of a new electric variant in a historically internal-combustion category is an early warning that qualified technicians will be needed in that area before the parc grows further. Without them, customers face shipping the machine elsewhere or waiting for a busy specialist to travel - cost and frustration on both sides. The data can foresee that demand; it cannot conjure candidates willing to relocate, so workforce planning sits partly beyond what a dataset solves. As with parts, the signal is most useful where fleets stay put.
An idea worth discussing: aftermarket suppliers as participants
One question we cannot yet answer from experience: what changes when the aftermarket itself joins the programme? We do not currently run a project that combines OEMs with the competitive aftermarket parts suppliers that service their machines through life. So this is a possibility to explore, not a result to report.
The concept is straightforward. Aftermarket suppliers contributing their own sales data as participants would give OEMs a view of their share of wallet in parts and servicing - how much of the aftersales demand their parc generates actually flows back through their own network. The same forward-looking thread covers extending programmes into attachment and parts categories, an appetite participants have already signalled by asking to add new equipment categories to their data. A route for that kind of extension already exists in the way associate members access industry data. If the aftersales market is worth planning for, it may eventually be worth measuring.
Key takeaways
- Parts demand follows the machine parc - the machines operating in a territory now - not the economic indicators that drive new machine sales.
- Monthly, model-level market data turns a sales map into an age profile, so parts teams can stock the right specifications as each cohort matures.
- Internal data covers your own brand's machines; industry data adds the cross-brand view - other brands' machines in your zone that may need parts you can supply.
- Warranty provisioning improves when a manufacturer can see a cohort of new machines land in an area before the claims begin.
- Parc visibility turns a parts counter from order-taking into intelligent rationing, serving the bulk buyer and the breakdown customer at once.
- Aftermarket suppliers contributing data is a forward-looking possibility for share-of-wallet visibility, not something PowerStats runs today.
Frequently asked questions
What is a machine parc?
The machine parc is the population of machines operating in an area: which models, in what numbers and of what age. Parts and service teams plan against the parc rather than against new sales, because parts demand comes from machines already in the field. Monthly, model-level market data lets a team build that picture for their territory and watch how it ages.
How does market data help a parts team forecast demand?
It shows which models entered which zones in which months, so a parts team can infer what is operating nearby and what those machines will need as they age. That informs which parts to hold, in what quantities and where, and when to provision for warranty items as new cohorts arrive. The forecasting stays with the team; the data gives it an industry-wide base instead of internal history alone.
How is parts planning different from planning new machine supply?
New machine supply is planned eighteen months to five years ahead and follows the economy and project pipelines. Parts demand follows machine utilisation in a territory at a point in time, and fleets can move between territories when projects end. The two run on fundamentally different lead indicators, so a parts team needs its own view of the data.
Can market data reveal servicing opportunities from other brands' machines?
In projects where the reporting model gives participants brand-level visibility, such as open reporting, a dealer can see when machines from other brands have been sold into their zone. Where those machines have no local service agent, the nearest dealer able to match part numbers can plan to hold cross-usable parts. That signal does not exist in internal sales data.
Plan the parc, not just the pipeline
The machines already in your territory are quietly setting your parts and service workload for years ahead. Seeing the machine parc - by model, zone and age - lets an aftersales operation hold fewer parts and more of the right ones, extending the same evidence-led approach as production planning and forecasting with market data into the years after the sale. See what a monthly, model-level view of your market would add to your aftersales planning - let's talk.
