Aug 9, 2023 | Change, Operations, Strategy
Almost every SME I visit or work with needs to one degree or another to be moving down the path towards ‘digitisation’.
For some, this means considering how the sudden appearance of LLM trained AI will impact on their competitive position, for others, it is still how to write a simple excel macro, and move bookkeeping from Mavis in the corner to a cloud package.
Just what does ‘digitisation’ mean?
For most of my clients it means automating some or all of the existing processes driven by bits of unconnected software and spreadsheets, liberally connected by people handing things over.
It is usually a real mess, and the evidence of incomplete solutions, misinformation, and shattered hopes lie everywhere.
The world is digitising at an accelerating rate, so keeping up is not only a competitive imperative, it is a strategic challenge. To survive you must evolve at least the same rate, just to keep up.
On of my former clients is a printing business, an SME with deep capabilities in all things ‘printing’ that enabled the company to be very successful, in the past. Their capabilities are terrific, highly competitive, if we were still in 1999.
If I use them as a metaphor for most I work with, there is a consistent pattern.
They do not see digitisation as an investment in the future, rather it is seen as an expense. This means that the challenges are not considered to be strategic. There is no consideration of the application of digital to their product offerings, beyond the digital printing machines, services beyond those that made them successful 20 years ago, and their business models, beyond what is demanded by the two biggest customers, who between them deliver well over 35% of revenue.
They have not considered digitisation of operational processes, beyond a 20 year old ERP system, which has not been updated in any meaningful way for a decade, and they still only use a portion of the capability. The reason for this is simply a lack of internal capability and awareness, and the lack of cash to invest for the long term.
They have not modified their organisational and operational culture. No digitisation effort can succeed without the support of an operating culture that encourages ongoing change. Organisational processes can be modified by decree, but they will not stick. It takes everyone in the boat to be pulling in the same direction, in unison, to make the forward progress proposed by the digitisation nirvana. This takes leadership, and a willingness to be both vulnerable internally, and a strong ability to absorb the stuff from outside. You need to ‘get out of the building’ not to smell the roses, but to see the lie of the land, and understand where the opportunities and challenges are hiding.
The recognition of the critical necessity of change is where you get given one point out of a possible 10. The other 9 are reserved for taking action. A daunting prospect for most.
Following are the 5 steps necessary to become ‘match fit’.
- Map the existing operational processes so you know what you are changing. The starting point!
- Map and change the mindset of the people, so everyone understands the extent of the challenge to the business, and to them personally. This will prove very tough for some, so expect push-back.
- Take small and incremental steps along a path that all understand leads to a digital future, which means that a lot of collaborative planning has been done. Look for some low hanging fruit where early wins are likely.
- Ensure that there are the necessary opportunities for all stakeholders, but particularly employees to grow and change with you. Those that choose not to, also choose to work elsewhere. There are no free rides.
- Ensure the resources of time and money are allocated uncompromisingly to the long-term outcomes. It is just too easy to put aside something that is important but not urgent for something that may seem to be urgent, but is not important to the transformational effort.
Most need outside help to get this done. Usually that help in the early stages is not found amongst software vendors who have a dog in the fight. It is amongst those who have ‘been there, done that’. It will also be a resource hungry beast, but assuming you feed it, and you have the right mix of project management and technical capabilities, the investment will generate returns quickly, just not tomorrow.
Header cartoon credit: Tom Gauld
Jul 12, 2023 | Analytics, Marketing, Operations
Anyone who reads my stuff on any sort of regular basis will know I have been deeply engaged with the potential impact of AI on all of us, since I stumbled across ChatGPT in early December last year. Of particular interest is the apparent potential for efficiency gains, particularly amongst the SME manufacturers I serve.
On one hand, I have been excited by the potential of AI to generate efficiency and expand the operational scale of SME’s. On the other, scared shitless at the potential for bad actors to sneak into our collective pockets and steal everything.
I need to write to think.
It forces me to sort out the stuff swimming around between my ears, as when I can articulate it sufficiently to write about it in some coherent manner, it leads to some level of understanding.
So, here is my list of the good stuff, followed by the bad, as it relates to the core of my business: strategy and marketing, starting with SME’s and the written word.
The good things AI can do for you.
- Summarising large blocks of copy, even when it seems very messy.
- Brainstorming; ideas, subject lines, complementary ideas, headlines.
- Editing and grammar. (I have been using the editing and ‘speak’ functionality of word for years, it is essential to me, and is AI that we now just treat as part of the furniture)
- Assembling descriptions and fact sheets
- Looking for logical holes in an argument
- Repurposing copy from one platform to another
- Research
- Outline and first draft.
- Translation and transcription
The stuff AI is no good at doing.
- Humour
- Reflecting current news and events
- Factual reliability. (Sometimes, it just makes stuff up)
- Finding a good metaphor
- Being creative. The great irony with creativity is that AI opens a whole new set of what is possible with visual tools, which can then combine with verbal cloning tools to completely alter apparent reality.
- Looking ahead
- Breaking complexity down to ‘first principles’
- Pouring another glass when faced by a blank page and a deadline.
Then there is all the other stuff AI will do, and evolve to do in the very near future, that is not writing. Graphic design, integrating currently separate digital systems (API’s on powerful steroids) identifying trends and holes in huge masses of data. The impact on medical technology is already profound. When the human genome was first successfully mapped in 2000, the cost of that first success was in the tens of billions of dollars. Now you can send away a sample and have it returned with your genome map for a few dollars overnight.
The key it seems, is to be very good at explaining to the tool what it is you want, in the detail a 5-year-old will understand. As the header cartoon illustrates, being human while driving this stuff will rapidly become the differentiator.
Header credit: GapingVoid.com.
Jun 30, 2023 | Change, Lean, Operations
Several years ago I became aware of ‘Wrights law‘. In the 1930’s, Theordore Wright an aero engineer proposed that: ‘For every cumulative doubling of units produced, costs will fall by a constant percentage’. This insight came from observing the performance of his own factories building aircraft during the thirties and over the course of the war.
While I do not have the numbers, intuitively after 50 years of observation, it holds very true.
That truth seems to hold over any manufacturing I have seen and read about, unlike its much better known sibling Moore’s Law. Gordon Moore observed the increase in the number of transistors that can be stuffed onto a silicon chip in a given period of time, and predicted that a doubling of numbers would hold consistently over the long term.
Therein lies the significant difference that manufacturers have come to rely on.
Moore’s law refers to technology improvements over time.
Wright’s law refers to the manufacturing cost reductions that come with scale.
I would suggest that the cumulative impact of the combination has had a potent effect on manufacturing costs of everything from the manufacture of simple widgets to solar panels, to the cost of human genome mapping. Wrights Law applies as scale builds, and technology provides a catalyst to a tipping point that radically alters the growth curve, after which the graph finds a new normal in the relationship between volume and cost.
Australia for lack of leadership, foresight and capital has shied away from the investment required to light that catalytic fire many times in the past.
A primary example is solar panels. We have known for a hundred years that solar energy could be harnessed. As a kid I used to burn leaves, paper, ants, and occasionally myself, with a magnifying glass. However, it took researchers at the UNSW to invent PERC (Passivated Emitter and Real Cell) technology in 1983 to kick off Australia being the international leader in Solar cell technology. Funding and the foresight to commercialise could not be assembled here, so the technology was used to develop the manufacturing industry in China, where Wright’s law has facilitated the growth of a dominating share of the world market for wafers, cells, and completed solar modules.
Forecasting manufacturing costs is at the core of every successful manufacturer. While in the early stages of commercialisation there will be a host of variables you need to be able to model, understanding the relationship between your cost base and scale will remove a significant weight from your shoulders when planning capital requirements.
Australia again finds itself on the cusp of being an international leader in Quantum computing, biotechnology, Hydrogen sourced energy, and rare earth extraction and value addition. Let’s not allow ourselves to be distracted this time, we may not get another chance.
Successful economies all have one thing in common: they manufacture stuff others want to buy. Australia’s history is littered with great ideas, and technical innovations that are commercialised elsewhere for lack of foresight, leadership and capital. We would be desperately stupid to let it happen again!
Jun 13, 2023 | Management, Operations
There is a simple answer, but the money is just a bit harder to find.
It is tied up in your current operations, consumed by all manner of things that do not add value to a customer.
Machine down time, rework, waste, on line inventory, double handling, and a host of other things that get in the way of a steady, predictable and continuous flow through a factory.
Progress to completion through a production process can only go as fast as the slowest point in the process. Working around these choke points entails either building WIP inventory, or slowing the faster parts down to the speed of the slowest part of the process. There is no third internal option, but ‘outsourcing’ the slow bits is sometimes a productive choice.
Progressive removal of any impediment to a predictable even ‘flow’ and you will save money. However, even more importantly, you will free up capacity that will give you the opportunity to sell more from the same fixed cost base.
That is where the gold hides: Money for nothing.
Do you want it?
Mar 1, 2023 | Collaboration, Lean, Operations
Our supply chains are suddenly under great scrutiny given the frailties surfaced by Covid. Calls for a greater proportion of domestic procurement are now more common than ever, but is domestic availability the only answer?
Most supply chains are actually run by procurement and logistics people. While there is senior management oversight, the actual purchase choices are routinely made in lower levels of most organisations. To affect change, this is where we need to start, in the bowels of the organisation.
The KPIs of procurement personnel are generally around invoice cost, as it is easy to track. In future, the decision should be more about security of supply, and total procurement cost, which are much harder to measure, and availability which is relatively easy to measure, but in my experience is often ignored.
The huge caveat of course is that the CEO must give ‘permission’ for the procurement people to go off the reservation, and make the necessary changes, and risk buying other than from ‘IBM’.
We also need deep supply chain mapping that captures the dynamics of the chain, and all the transaction costs that apply, as well as the visible financial costs.
The KPI’s of procurement must change if we are to build the resilience of our supply chains.
- Collaborative DIFOT analyses through the chain
- Switch KPI focus from cost savings, usually measured against the invoice cost, to give greater weight to availability.
- Tracking of the drivers of cost, quality and delivery throughout the supply chain.
- Quantifying transaction and opportunity costs, (particularly of management time) at all points through the chain.
- Measures of resilience such as alternative, qualified, and immediately capable suppliers, utilising differing logistics
Together these measures will give you a measure of the resilience of your supply chain, or its ability to recover competitive performance after a failure. The greater the number of nodes in a chain, the greater the risks, which become amplified as you move further way from direct control.
Local suppliers will have to be prepared for the scrutiny of their sourcing. Company A, procuring from Company B, where there are sub-assemblies necessary will want to stress check the suppliers to company B as part of their procurement processes. This will take supply chain transparency to a whole new level. To this point the concerns have been mostly about cost and the time in the chain. In future, it will go much deeper, digging into a range of items that deliver resilience and reliable quality.
The speed of recovery of your supply chain after the inevitable disruption will be key to competitive performance.
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
