Product-quantity (PQ) analysis ranks every product a plant makes by volume over a period, plotting quantity as descending bars with a cumulative-percentage curve, so you can see the high-runners that deserve dedicated flow lines and group the rest into families for shared cells. It is a Pareto chart aimed at your product mix.

Most plants try to run every product the same way, and it is why so many lines feel chaotic: a handful of high-volume items and a long tail of low-volume specials all fighting for the same machines. PQ analysis is the first, cheapest step toward fixing that, a ranked picture of what you actually make in quantity, which tells you where to build flow and where to pool variety. This guide covers how to read a PQ chart, how to run the analysis, and how it drives cell and line design.

What is product-quantity (PQ) analysis?

Product-quantity analysis is a volume ranking of your product mix, built exactly like a Pareto chart: total the quantity of each product over a representative window, sort them high to low, and draw the bars with a cumulative-percentage line across the top. It rests on the Pareto principle, the observation that a small share of causes drives most of the effect, which ASQ documents as the 80/20 rule behind the Pareto chart, one of the seven basic quality tools (ASQ, Pareto Chart). Applied to products, the principle usually holds: a minority of your part numbers accounts for the majority of your volume.

That single picture reframes a plant's layout question. Instead of asking "how do we run all 400 products?", PQ analysis lets you ask two sharper questions: which few products run in enough volume to justify their own dedicated flow, and how do we group the remaining many so they share equipment efficiently? The first question points toward flow lines; the second points toward cells.

A product-quantity (PQ) chart with runner zonesThe PQ chart: volume high to lowquantity100%A: runnersB: repeatersC: strangers (the long tail)A few products (zone A) carry most of the volume; the long C tail is many products at low quantity each.
A PQ chart sorts products by volume high to low with a cumulative curve. Zone A runners carry most of the volume and justify dedicated flow; the zone C tail is many low-volume products that should share cells.

How do you read a PQ chart?

Read the chart in three zones from left to right, and the layout strategy falls out of where each product sits. The left-hand bars, the runners are the few products that dominate volume; the cumulative curve climbs steeply through them. The middle, the repeaters run in moderate, regular volume. The long flat right-hand tail, the strangers is a large number of products each made in small quantity.

The cumulative curve is where the decision lives. Find where it crosses roughly 80 percent of total volume and drop a line: the products to the left are candidates for their own dedicated flow because their volume alone can keep a line busy. The products to the right, the many strangers, cannot each justify dedicated equipment, so the move is to group them by shared processing steps into cells that pool their variety. The chart does not make the decision for you, but it draws the boundary you decide around.

How do you do a product-quantity analysis step by step?

The analysis is straightforward arithmetic on data you already have; the discipline is choosing an honest window and using real quantities.

  1. Pick a representative period. Use six to twelve months of demand so seasonal swings and one-off orders do not distort the ranking. Too short a window flatters a temporary spike into a false runner.
  2. Pull the quantity per product. Total the units made or shipped for each part number over that window, in consistent units. Do not mix eaches with cases.
  3. Sort high to low and total. Rank every product by quantity descending, and sum the grand total so you can compute shares.
  4. Add the cumulative percentage. Running down the sorted list, accumulate each product's share of the total. This is the curve that reveals the vital few.
  5. Draw the PQ chart. Plot descending bars for quantity and the cumulative-percentage line across the top, exactly as you would build a Pareto chart.
  6. Mark the zones. Split runners, repeaters, and strangers where the curve changes slope, commonly near the 80 percent crossing, and label the candidate strategy for each zone.
  7. Group strangers into families. For the tail, look past volume to routing: products that share the same sequence of machines belong in the same cell. This is where PQ analysis hands off to cell design.
Grouping strangers into families by shared routingVolume finds runners; routing finds familiesproducts × machinesM1M2M3M4M5P-AP-BP-CP-Dfamily 1 -> cell 1family 2 -> cell 2Products that share the same machines become a family, and a family becomes a cell.
Once volume has found the runners, a product-process matrix groups the remaining products by the machines they share, and each block of shared routing becomes a candidate manufacturing cell.
From PQ analysis to line and cell designPQ analysis points each product to a layoutPQ CHARTranked by volumeA: runnershigh volume, low mixB: repeaterssteady volumeC: strangerslow volume, high mixDEDICATED FLOW LINEone product, one lineSHARED CELLa few similar productsMIXED-MODEL CELLgrouped by routingRunners earn a dedicated line; strangers are pooled into cells grouped by shared routing.
PQ analysis routes each zone toward a layout: high-volume runners to dedicated flow lines, steady repeaters to shared cells, and the low-volume tail into mixed-model cells grouped by common routing.

By the numbers: the 80/20 behind PQ analysis

PQ analysis works because product mix obeys the Pareto principle. ASQ traces the principle to the economist Vilfredo Pareto and its quality use to Joseph Juran, who separated the vital few from the trivial many the roughly 20 percent of variables that drive about 80 percent of the effect (ASQ, Pareto Chart). In a plant, the vital few are the high-runner products, and finding them changes where you invest: a dedicated line for a true runner pays back, while the same investment spread across the stranger tail is wasted. The exact split is rarely a clean 80/20, but the shape, a few products carrying most of the volume, is nearly universal. Where Harmony fits: a PQ analysis is usually run once off a year of history, but demand mix shifts, yesterday's runner fades and a new one climbs. When production quantities are captured live by product at each line, the PQ picture stays current, so your layout keeps matching what the plant actually builds instead of what it built last year.

How is PQ analysis different from a plain Pareto or a PQR analysis?

A PQ analysis is a Pareto chart; the difference is purpose. A generic Pareto chart can rank anything, defect types, downtime causes, complaints, whereas PQ analysis specifically ranks products by quantity to drive layout and flow decisions. The tool is the same; the axis and the decision are what make it a PQ analysis.

There is one important caveat, sometimes called PQR analysis, that adds revenue or cost to the picture. Ranking by quantity alone can mislead when a low-volume product carries most of the margin, or a high-volume one barely breaks even. Volume tells you where the flow pressure is; it does not tell you where the money is. For pure layout and flow design, quantity is the right axis, because machines feel units, not dollars. But before you commit capital to a dedicated line, sanity-check the runner against revenue so you are not optimizing flow for a product that is not worth the floor space.

What are the most common PQ analysis mistakes?

The first is choosing a bad window: a month that happens to contain a big one-off order promotes a stranger into a false runner and sends you building flow for demand that will not repeat. Use enough history to see the real pattern. The second is stopping at volume and skipping routing: two products can have identical quantity and completely different machine paths, and only routing tells you whether they belong in the same cell.

The third is treating the analysis as permanent. Product mix is one of the fastest-moving things in a plant, and a PQ chart two years old can point your layout at products you barely make anymore. The fourth is ignoring the revenue caveat above and optimizing flow for a high-volume product that contributes little margin. PQ analysis is a starting lens, not the whole decision.

How does PQ analysis connect to the floor?

PQ analysis is the front door to layout and flow design. Its runner products become candidates for dedicated value streams its families feed cellular manufacturing and once a cell or line is chosen you size it with a process capacity sheet and balance it against takt time using line balancing. All of it serves the goal of lean manufacturing: match the layout to the real demand so product flows instead of fighting for shared machines.

The catch is that the PQ picture is only true on the day you draw it. Demand mix drifts constantly, and a layout designed around last year's runners slowly stops fitting this year's orders. Run the analysis off a stale export and you optimize for a plant that no longer exists. When production quantities are captured live by product and line, the PQ ranking can be refreshed against current demand, and a product climbing toward runner status shows up while there is still time to give it flow. That live picture is what Harmony gives a plant through station-level capture turning PQ analysis from an annual slide into a living view of what the floor actually makes. CLS made exactly that shift, from production data found the next morning to production data visible during the shift. No rip-and-replace.