Jul 20, 2026 | Analytics, Operations, Strategy
If a job that took eight hours now takes one, most managers will call that a productivity gain.
They are right up to a point.
Economists usually measure labour productivity as output per hour worked. If we produce the same output in one hour instead of eight, productivity has risen whether we use the remaining seven hours to win another customer, dissect the accounts, or head for the beach.
The beach does not cancel the efficiency gain, it simply exposes the limits of the measure.
The conventional measures tells us how efficiently we produced what it can already see. It tells us very little about what happened to the released capacity, whether the output improved, or whether technology enabled valuable work that nobody previously attempted.
It is a stopwatch with a blindfold.
This matters as businesses adopt AI. Most discussion starts with replacement: write the report faster, answer the email sooner, produce the first draft without paying someone to stare at a blank screen.
Useful, certainly but not transformative.
If AI cuts eight hours of work to one and the business fills the other seven with more of the same work, the gain will eventually appear in output, margin or capacity. If the business simply adds seven hours of meetings, the technology has delivered potential while management has squandered it.
That is not a measurement failure alone. It is a management failure with excuses.
The larger opportunity comes when AI extends what a business can do.
Most SMEs sit on piles of data they barely use. The accounting system contains patterns in customer profitability, purchasing, margin leakage and payment behaviour. The CRM contains clues about lead quality, objections, lost sales, response times and repeat purchases.
Managers have always suspected gold lies in those records, but lacked the time, skills or patience to dig it out.
AI changes the economics of the digging.
A business can now examine why apparently similar customers generate wildly different margins. It can compare the language used by prospects who bought with the language used by those who disappeared. It can identify which products create revenue but consume so much handling, discounting and after-sales attention that they destroy profit.
None of this necessarily replaces an existing task. Often, nobody did the task in the first place.
That makes “hours saved” a miserably small way to assess the value.
The better questions concern decisions improved, waste prevented, risks spotted and identified opportunities leveraged.
My commercial history includes a significant time driving sales and profitability of dairy products.
Take yoghurt for example. The supermarkets report a yoghurt market. That broad category helps retailers size the category shelf space, then allocate space to product groups, brands and individual products. It creates a ‘category map’ products and preparation of management reports. It conceals much of the behaviour that drives the sale to the time poor, budget limited, harassed by kids shopping consumer.
Shoppers choose between fruited and plain, low-fat and full-fat, pot-set and stirred, indulgence and health, adult and children’s formats, single serves and family tubs. Then they behave inconsistently, driven by the behaviours at home that result in consumption. Shoppers have never read the segmentation presentation, and do not care about it at all. The same persons trolley can contain Greek yoghurt for breakfast, a sweet pouch for a child, a set in pot natural yogurt for a fussy daughter, and a premium dessert for Saturday night, and be different on every visit to the supermarket.
Traditional analysis could reveal these patterns, given enough clean data, statistical skill and time. AI does not invent the possibility. It lowers the cost and effort required to pursue it, then allows a manager to question the data repeatedly rather than wait three weeks for another report.
They can uncover submarkets hidden inside the convenient fiction called “the market”. It can improve assortment, pricing, promotion and product development. The value comes not from producing the old market report faster, but from seeing choices the old report blurred out.
Then comes the hardest category: genuine innovation. Not the pack design change, or the ‘new improved’ standard product, genuine, category generating new products.
Every important general-purpose technology eventually enables products, services and business models that people could not sensibly describe at the beginning. We struggle to measure these gains because measurement follows observable activity. It cannot count a commercial possibility before somebody discovers it.
Nor do conventional accounts handle quality and unpriced value in anything but hypothetical terms. A better diagnosis, a more useful forecast, improved process, or a tailored customer answer may create considerable value without creating a measurable additional unit of output. We do not need to throw out existing productivity measures. They answer an important question and allow comparisons over time.
Businesses need a second dashboard.
Alongside output per hour, managers should track the use of released capacity, the valuable work newly performed, improvements in product quality and management choices, improved cycle and takt times, and new revenue or margin that technology made possible.
That is the productivity challenge in businesses.
AI will save time. This is an opportunity also available to competitors and especially new market entrants, unencumbered by past choices and technology. The sustainable advantage will go to the businesses that know what to do with the no added cost capacity that better use of time can deliver.
As an interim GM 20 years ago I was contracted to a manufacturing business that while marginally profitable, was struggling with capacity. Analysis revealed what everyone knew at some level, that a key step in the manufacturing process was acting as a bottleneck, strangling capacity. A number of relatively simple process changes that required no capital and no personnel changes enabled the key step to operate an extra 6 hours a day, effectively nearly doubling capacity. That resulted in shorter customer lead times, dramatic increases in DIFOT, abolition of outside warehouse costs, and very happy customers increasing orders at the expense of competitors. The changes took a couple of months to work their way through the financial system, but when it did, the impact on profitability was substantial. However, it was only with hindsight and deep digging into the numbers, that the changes in productivity numbers could be tracked.
The foregoing examines the productivity challenge of an individual business.
In the recent budget, and following discussions, the Treasurer has loudly and consistently pointed to the ‘productivity package’ contained in the budget papers.
The challenge in delivering on the promise, and having the electorate understand the complexity of the productivity challenge faced by the Australian economy is immense. It is easy to pick at the edges and observe ‘nothing to see here’ because the policy documents are shallow, predictably lacking in any real and measurable project timelines and checkpoints, and the press releases are as useless as the claims of a carnival hawker. It is however a start.
Nov 24, 2025 | Operations
Continuous Improvement is a mindset, the improvements sought are on their own often tiny, seemingly irrelevant in isolation. The point is in the compounding of the improvements over time that delivers the improved outcome. Proclamations from the CEO, group ‘Love-ins’ and slogans on the lunchroom wall have no impact.
It is also true that not all improvements are easy to measure. How do you measure the culture that supports and feeds continuous improvement?
However, there are things you can measure that will be leading indicators of CI
- Cycle time. Measuring the cycle time of processes, seeking to shorten them is always an indicator of improvement. Almost all improvement activities I have seen and been involved with use time as a measure of performance.
- Product quality. A common problem with measuring quality is defining just what the term means. To me it is very simply compliance to specifications, which is generally easy to measure, once you have agreed the specs. The most common tool is a ‘Control chart’ that graphs the upper and lower limits of acceptable adherence to specifications. It can be used equally well to measure the tolerances on a machine output, to cycle times of any process, and responses to a lead generation program.
- Customer satisfaction. Asking customers is a good place to start. There is plenty of research around that indicates that the degree of customer satisfaction an enterprise thinks they are delivering, and what the degree is when their customers are asked differs wildly. Independent surveys can be very informative, and tools like the net promoter score framework, can deliver the numbers sought by the corner office. To me the very best measures are the rate of return customers and lifetime customer value compared to industry peers.
- Ratios. Driven by the strategic priorities, every business will have the opportunity to employ differing ratios that reflect the alignment with the strategic priority. For example, revenue/employee, right-first-time/installations, new customer revenue/total revenue, the list can go on. However, the catch is to have as few KPI’s as possible, cascaded through the organisation that enable the drivers of success to be made very visible. For example, a former client instigated a company wide KPI of Gross margin/employee. This KPI was used company wide, and within individual functions and work groups through the organisation. It focussed company wide attention on activities that drove revenue and the COGS.
- Employee generated ideas. Have a formal process of encouraging, gathering, sorting, and acting on the ideas coming from the front line. It is always the case that those closest to the action see the opportunities better than those further up the line. Engage them in genuine process improvement, which as a huge side benefit. This sort of employee engagement builds a robust culture. A culture that measures, celebrates, and implements small ideas is the real engine of continuous improvement.
- Employee satisfaction. The old wives saying ‘happy wife, Happy life’ applies equally to employees. Happy, motivated employees are perhaps the best way to ensure that customers are well treated, and therefore return, and are prepared to refer you.
- Financial ROI. The last in this list, but most obvious and most often used. You make an investment, you want a return, and the accountants will deliver up a way to count it. Benefit divided by Cost of implementation. The challenge is putting some numbers around the benefit. At best these measures are appropriate in specific circumstances where there is some hard capex being made to improve one of the above parameters.
Header cartoon courtesy of GapingVoid.com
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Oct 8, 2025 | Analytics, Leadership, Operations
We have learned over time, led by Toyota, that ‘root cause analysis’ thereby seeing the root cause of problems is the road to continuous improvement.
At any time when there is a problem, do not let it get papered over, do not let the symptoms be treated, dig and dig until you understand the root cause and then fix it.
Often this is a challenging task, root causes by their nature are usually well hidden, and often ambiguous until there is a forensic examination. However, they are always there and rooting them out enables a compounding of improvements over time.
That analysis requires a cultural context in which to work, as it takes time, consumes resources, and is never completed, as there is always another problem to be analysed. That is the nature of problems, root out one bottleneck, and the blockage just moves to the next spot, previously hidden by the former one.
However, we also seem to look at a process from its beginning, setting out to define a hidden problem occurring inside the process.
Should we reverse the order, and look at the causes of success?
Why and how has Toyota managed to remake themselves from the crappy stuff carrying the lousy quality implications of ‘made in Japan’ from my childhood to an icon of quality, and in the process, driven change through manufacturing globally?
What is the root cause of their success?
My contention is that the root cause is a simple piece of rope.
The Andon cord.
Toyota put Andon cords through their factories, so that any person on the line could stop the line at any time when they saw a fault.
Not only were they empowered to stop the line, they were expected to do so any time a problem occurred that could not be fixed in the time allowed at that station in the line. When the line was stopped by a worker, the supervisor immediately went to the stoppage point with two objectives:
- Solve the problem to ensure it would not be repeated, and that the problem got not one step closer to a customer.
- To congratulate the worker for stopping the line so the problem could be fixed. This ensured there was not any reluctance to address a problem by such radical means as stopping a whole factory.
This is an extreme example of empowering the front line, making those who can see problems as they face them all the time, responsible for fixing them.
When introduced, this must have caused headaches, as the productivity would have plummeted. The number of cars produced dropped off a cliff, but those that got through would be as good as they could be, and slowly, as problems were solved, productivity rose, quality rose, as over time Toyota became the benchmark for motor vehicle quality around the world.
All from a simple piece of rope, and the surrounding culture that delivered to those at the coal face, the responsibility to exercise their right to pull it.
What is the equivalent of the Toyota Andon cord in your business?
Jun 25, 2025 | AI, Customers, Lean, Operations
The idea of the OODA loop is to get inside the decision cycle of your opposition. Once inside, you control the outcome in the absence of some externality.
Toyota used this idea to destroy Detroit.
The Andon cord placed the power of tactical decision making about quality right at the point where it was needed, with the workers on the production line.
By this means, quality problems were identified and fixed before they moved a further step towards the customer.
It also did something else.
By identifying and fixing problems at the source, the cycle of problem fixing was accelerated greatly. Not every problem can be fixed immediately at the line, but there are processes for escalation, from the front lines to the lowest level that is empowered to address the problem. That escalation involved suppliers when the problem was caused by a supplied part that was substandard.
By contrast, Detroit was driven from the top down, being run by spreadsheets (handwritten until the 90’s) by executives who may never have seen the inside of the factory.
A problem as it escalates up a chain of command has many opportunities to be buried, forgotten, miscommunicated, all of which will happen, driven by all sorts of human frailties and power games. The end result, the little problem in the factory compounds and becomes a big problem with customers, which costs a lot to address, and ruins reputations.
Toyota got well inside the time it took Detroit to respond to problems. While Detroit was escalating or hiding quality problems, Toyota was fixing them and moving on the next improvement.
They were inside the OODA loop of Detroit, and it destroyed the American car industry.
AI is now giving users an easy tool to get inside the decision cycle of their competition, while seeing the productivity benefits drop to their bottom line.
How are you going to deal with that?
Derek de Solla Price was a British physicist and scientific historian perhaps best known for his work on the Antikythera mechanism. After researching scientific papers and their authors, he proposed that in any field half the ground breaking work comes from the square root of the number of participants in the field.