Oct 19, 2022 | Analytics, Operations
No matter your businesses size, digital capability has become a driver of commercial sustainability over the last decade.
It has become a clear case of digitise or die.
This does not mean you have to go from an analogue starting point to fully digitised in one step, that is unrealistic. However, failure to start the digitisation journey will eventually be the undoing of your business.
There are a number of logical steps you can take that will build capability quickly, without massive investment, although some investment, particularly of management commitment, is necessary. However, like any investment, you should expect a return.
If you are starting the journey, the following is one set of the steps you might expect to mount, not necessarily in this order, but this is a common pattern I have seen.
Step 1. Assemble a clear picture of the currently available data. Mostly this will be ad hoc, and manually collected. Machinery purchased over the last few years will have the facility to capture data that is often unused, or under-utilised. This might simply require some connection between the data logger in the gear to your server, or better still, to a cloud application.
Step 2. Build a common system for the assembly of data that will enable it to be analysed in a consistent manner. Many factors have differing sets of ‘data languages’ based on legacy practices, and short-term convenience. Creating a common data language is important, and the best tool for doing this are to map all the processes in the factory, and break them into what is in lean parlance, ‘value streams’. The languages can then be tailored to make sense to all who meet them.
Step 3. Invest in further data capture. In the early stages, this is often a case of retro fitting devices onto existing machinery and downloading it all into a common data base. Depending on your operations this can be as simple as excel. There are many available low level options that are of a modular design, so that as capability grows, the modules can be implemented progressively.
Step 4. Invest in the capability to analyse the data and turn it into actionable insights. It is at this point that people become invaluable to the system. Any digital system can only respond to inputs in the way they have been instructed. They are no good at assessing the inputs for which there has been no or little precedent, you need people for those vital tasks.
Step 5. progressively implement data generation and analysis to inform operations. Use the feedback to constantly improve the quality of the data and the analysis that is used to manage and improve operations.
Step 6. Rinse and repeat. Digitisation is not a task with a completion date, it is a journey without an end.
As I headed towards the ‘publish’ button, a notification of a new program by the Victorian government popped into my inbox. The ‘Digital jobs for Manufacturing‘ program will fund training of employees of eligible Victorian manufacturers in a 12 week part time course run by Victorian universities. Have a look.
Header credit: Tom Gauld who takes an ironic, but widely felt frustration felt by SME’s at digitisation at www.tomgauld.com
Oct 14, 2022 | Analytics, Innovation
We set out to measure things, to give us a sense of achievement, to allocate priorities, and simply to keep score as we proceed. It is an engineering perspective.
We do not have any way of objectively measuring how we feel, but how we feel is what drives our behaviour.
This would not matter if we all perceived the world objectively, but we don’t. We observe the world through the complex frosted window of our experience, context, opinions, and did we get our coffee this morning.
So, how do we dig away at this problem, and it is a problem, simply because we use objective means to make subjective decisions, and it sucks.
We ask better questions, and we ask those questions from as wide a variety of perspectives as we can. We must ask those better questions, and experiment, test stuff, be allowed to fail, as in the outliers you will find the unexpected. However, you must also expect the unexpected, you just cannot predict where it will be.
Bees, amazing insects that they are, have the process nailed. They have a behavioural characteristic scientists call the ‘Waggle dance’, which is a communication medium that leads others to the source of nectar. Bees must find nectar, that is the job on which their lives rely.
Depending on variables like the weather, location, season, and others, a percentage of bees, 10 – 20% ignore the waggle dance, and go off in a different direction. When they find a new source, they start the ‘waggle dance’ to attract other bees to that new source, thus keeping the hive healthy and well fed.
This is experimentation, sacrificing a small part of the current returns to build for the future.
As we consider how to best allocate our available resources, we can learn from bees.
Bees in blogs
Oct 4, 2022 | Analytics, Management, Operations
Too often KPI’s are all about the result, rather than the drivers that will deliver the result. When you are measuring just the outcomes, you have missed the opportunity to improve and optimise the actions that will lead to change the outcome being delivered.
Take for example the Cash Conversion Cycle (CCC) time. This measure is a fundamental tool in the improvement toolkit.
By improving the rate at which the cash outlaid to generate a customer service or product is turned back into cash by the payment of invoices, you reduce the amount of working capital required to keep the doors open.
The real benefit is to be found in the active management of the drivers of the CCC. The days taken to complete the cycle is just tracking the result of a set of actions that take place elsewhere, Manage the inputs, and the days will reduce.
For example, detailed examination of the debtors ledger will tell you which customers are slow to pay, thereby decreasing the speed of the cycle. It then becomes an item of potential improvement. Perhaps the salesperson responsible is not diligent enough, perhaps your collection processes are not explicit, you lack follow up, or clarity on your terms. In the end, perhaps you can decide that that a specific customer should be handed over to one of your competitors, weakening their cash position.
The same analysis becomes second nature around the inventory numbers: raw materials, WIP and finished goods. Improving those numbers without hurting the levels of customer service can dramatically improve the productivity of the investments made in the enterprise. As an added benefit, customers will thank you and give you more business.
As with anything, the absence of the information that details the drivers of an outcome, makes it hard to make improvements.
Another example. Sales personnel are often compensated by commissions on their total sales. If you want your salespeople to be out hunting for the next customer, rather than glad-handing existing ones, paying a commission on all sales is a poor strategy. It discourages the more time consuming and riskier task of finding and converting new customers. Existing customers in most cases can be professionally managed by an internal customer service function. The better use of commissions might be to encourage business development.
When you spend time identifying and managing the drivers of outcomes, the dollars will follow.
Sep 19, 2022 | Analytics, Communication
Social media platforms all compete for your attention, not just with other platforms, but with the rest of your life. Then, once you have given it, the real test begins.
What do you do with it.
The nature of social media is almost instantaneous. When something comes through your feed, an increasingly rare event for unadvertised material, it has a second, occasionally a couple, to grab and hold your attention, and encourage you to take the next step, whatever that might be.
It is not the long slow romancing of that great looking person in a bar, or at dinner party with friends, it is more like Tinder.
Swipe left, or swipe right. In or out. More information please or no thanks.
Your marketing task on social media, if you are to use it effectively, is to pass the initial tinder test, and have the other party look for more, and then pass on the post to their networks.
So how do you achieve that end, the referral of your material to others?
Most of the advice around is pretty accurate:
- Promise an explicit outcome to a specific cohort of potential customers.
- Photos of people should be front lit, and eyes not looking directly at the camera. This is to avoid the photo looking like a mug shot from the local cop-shop.
- Simplicity and consistency of design
- Make a clear and explicit call to action
- Make it easy for them to contact you
Remember always you only have a second to make the impact that will encourage them to swipe left, then the challenge is to add value, so they stay.
Better make that first second count.
Jul 28, 2022 | Analytics, Leadership
Psychology drives our behaviour, and yet we struggle miserably to forecast the impact it will have. Therefore, we cannot predict behaviour with any real accuracy, except with the benefit of hindsight, or across the average, assuming we ask the right questions.
There are five important psychological factors that profoundly impact the sorts of decisions, big and small we make every day.
Status, Certainty, Autonomy, Relatedness, Fairness.
Psychologists put them together into the ‘SCARF’ model as they set about understanding the drivers of behaviour, which centres around ‘away’ movements to minimise threats, and ‘towards’ movements to maximise rewards.
Status. We all know it is important, that is how Mercedes manage to squeeze 4 or 5 times the money out of buyers than a perfectly adequate, reliable, and outfitted with bells and whistles Korean or Chinese alternative. It is why people pay tens of thousands for a watch, assume crushing debt to have a luxury car in the drive, and Louis Vuitton is the world’s most valuable luxury brand.
Certainty. Uber nailed this one. The time we wait for a taxi is different to the time we wait for an Uber, even when the Uber wait is longer. This is because we are waiting with certainty, we know when the Uber will arrive, we know where it is right now, and we can walk out of the building as it pulls up, which adds a feeling of status to the equation. By contrast, call a taxi and then wait, uncertain when it will turn up.
Autonomy. We all like to feel we are making our own decisions, even when we are not. We love that feeling of freedom, even when it is an illusion, or inside a tiny arena of personal space.
Relatedness. Human beings are social animals, we like to feel like others are aware of us, and concerned with our needs, views, and ideas. It is like being in a book club, there are psychological rewards to being in a group that values your presence. We also need the group for protection, as it is the outliers that become a lion’s breakfast.
Fairness. Instantly we rate things on a fairness scale, we like to be seen as fair, even when we are diddling the books. Is it fair that the bloke next door who does the same job gets paid 20k more?
None of these things appear in economic models.
It was Einstein (amongst others) who said, ‘not everything that matters can be measured, and not everything that can be measured matters’.
Jul 25, 2022 | Analytics, Marketing
Almost every marketing so called guru, yours truly included, will bang on about calculating an ROI from your investment in marketing.
Marketing like any other investment should seek a return, and there should be accountability for those numbers.
Almost nobody will disagree.
The challenge is how you do it.
How do you attribute an outcome to any specific activity or individually weighted group of activities?
The amount spent divided by the sales, or margin returned from that activity.
Pretty easy in the case of a piece of machinery, another matter entirely for anything beyond a specific tactical action, such as an ad in Facebook or Google where the response can be counted.
In the case of marketing investment, how do you allocate the sales outcome to that activity?
When a sale is generated, was it because of the activity we are calculating for, or was it the phone call from the sales rep, attractive copy on the website, clean delivery truck, or the referral from some other satisfied customer?
How can we tell?
When some analytics nerd cracks the code on attribution, he will become histories fastest billionaire.
So, when some fast talker promising world market domination will result from investing in their new ‘thing’, run as fast as you can, unless they can prove they are the one who cracked the attribution code, which I do not expect any time soon.
