On 7 October 2018, the Intergovernmental Panel on Climate Change (IPCC) published a special report on the impact of a 1.5°C increase in global temperatures (1). It makes for sobering reading, but it also sets out 5 concrete steps that we can collectively take to limit global warming to 1.5°C:
This magnitude of challenge can seem overwhelming, and some of these steps seem out of our direct control. But we as individuals and we as businesses not only can but must contribute to global efforts if we are to succeed. The way we travel and consume, the way we produce and how we manage waste will all contribute to success. There is no quick and simple fix here, we relentlessly need to explore all avenues.
The role of inventory management in improving environmental outcomes was recently highlighted in Britain when fashion brand Burberry received unwanted publicity for destroying unsold stock, some of it being burnt. To be fair to Burberry, they say the energy from burning stock was captured, they have since announced a halt to disposing of obsolete stock in this way, and they are merely confronted with exactly the same challenge that almost all businesses face: finding the best balance between maximising sales and reducing waste.
None of us knows exactly how much demand there will be for any product. If we don’t have enough to meet customer demand, sales and sometimes even customers are lost. If we have too much, we are confronted with waste. Perishability adds to the complication, whether actual (eg foods or medicines) or virtual (eg fashion goods or newspapers – they don’t literally perish, but their usefulness drastically diminishes past a certain point in time). Companies can calculate the sweet spot using a form of the so-called newsvendor model (2), which in its simplest form works out statistically how much to stock in order to maximise profit.
Governments can influence environmental impacts by encouraging companies to generate less waste through environmental taxes and regulations. But even without such external impetus, our experience at nVentic suggests that almost all organizations have a strong purely commercial business case to improve their inventory management and thereby reduce their environmental footprint.
For every item produced that is not sold there are a series of wastes: wasted raw materials, wasted energy used for extraction and conversion, wasted storage, wasted transport and wasted disposal. Happily, this is an instance where commercial self-interest is aligned with environmental stewardship – reducing all of these wastes saves money! Inventory optimization reduces waste while protecting sales – you strive to get as close to what you actually need as possible, normally erring on the side of having a little too much.
Of course, even with the best inventory management in the world there will still be waste. You can’t totally eradicate waste from the apparel supply chain unless you start telling people what to wear. For essential medicines to be immediately available in hospitals a very high proportion of the time, buffer stocks are a necessity. Given their perishability, there will be some waste most of the time. The higher availability you need to guarantee for your products, the more waste there will be.
But the impossibility of perfection should not discourage us from targeting improvement. We come back to our contention that most organizations can do significantly better than they do today. We can’t completely eradicate waste, but we can certainly reduce it, and inventory optimization balances waste reduction with delivering great service to consumers.
So why have more organizations not optimized their inventories? Management consulting companies like REL and PWC produce regular reports on working capital (3) and these reports depressingly show very little progress in inventory reduction at a macro level over the last 5 years, even if individual companies can and do make major progress, and even if the potential commercial benefits are huge.
The challenge, in a word, is difficulty. Inventory optimization requires sophisticated use of statistics and a concerted organizational focus on it as an objective. Many organizations have tried and failed to get inventory under control; or maybe they have succeeded in delivering a reduction in the short term only to see levels creep back up again.
But just because something is difficult, it does not mean we should set it aside. Limiting global warming to 1.5°C is going to require a number of difficult things to be done.
This is a call to action. Add inventory optimization to your priorities list. Your shareholders will thank you for it. But so will your grandchildren.
nVentic is a business consulting firm specializing in inventory optimization, not an environmental or non-profit organization. If you would like to talk to us about how to improve your inventory management, please contact us: information@nventic.com
What is artificial intelligence (AI) and how can it help with inventory optimization?
A working definition of AI might be anything that makes a computer appear intelligent. And relative to inventory optimization, we might define AI as the ability of machines to take inventory “decisions”, such as when and whether to place an order, and of how much.
Inventory optimization is a complex endeavour, as we have outlined elsewhere. As such, it is desirable to enlist the help of advanced technology to help with it. Several tools on the market promise inventory optimization, some of which use techniques that are described as AI.
One problem with a buzzword like AI is that it is used to mean different things. In this article we are not so much interested in a theoretical discussion of AI as in considering the potential of inventory optimization technology more generally, whether powered by something you consider to be AI or not. And how reliant such technology is on the old-fashioned kind of intelligence that you find displayed by your people.
AI is one of a number of concepts, like natural language processing (NLP), data lakes, low-code, machine learning, robotic process automation (RPA), and so on, that are making great strides each year. There are wonderful opportunities for businesses to take advantage of all this innovation, but in these times of flux there is also a risk of investing a lot of time, effort and resources into interfaces that are out of date within a year. We believe there is always value in experimentation, building a platform for the next generation, but not all organisations have the time and resources to invest in something without a clear and immediate business case.
So what does the supply chain professional need to know about AI and how can you take advantage of it in your inventory optimization efforts? This discussion is also relevant to other fields in supply chain and beyond.
Artificial intelligence is, above all, artificial. It does not work the same way as human intelligence. A computer can process data 24 hours a day, 7 days a week without tiredness or error. But that is also its weakness, since it doesn’t question what it is doing.
A computer does what it is told to do, no more no less, following an algorithm. But this also means that if the algorithm is not universally valid, even if it works flawlessly, then the answer may be incorrect. This will be familiar to anyone using planning technology that they know to be inaccurate for at least some of the items they make or stock.
Underlying any intelligent approach to inventory optimization, whether artificial or human, you find algorithms. They embody the intelligence itself.
A simple algorithm might be used to automate replenishment: if stock on hand is equal to or less than X, place an order for quantity Y. This type of algorithm is widely used in various technology solutions. Its benefits are threefold:
This type of simple, rule-based algorithm lies at the heart of a lot of software including, where transfers of information between systems are required, RPA.
But how, in the example above, do you determine X (the re-order level) and Y (the order quantity)? This is not so simple. In this case you could either go for another rule-based algorithm, just with a lot more conditions and equations, or you could apply an optimization algorithm, that will work out the optimal solution itself.
This more complex type of algorithm could use deterministic and stochastic methods, which is essentially to say that it uses known mathematical formulas, factoring in variability. Or it could use techniques like genetic algorithms and data mining, which are useful when you are not entirely sure what you’re looking for or how to find it, and which are much more computer-intensive.
These more complex algorithms also have benefits:
However, compared to the simple algorithm, these complex algorithms also have a number of weaknesses:
Trust is the critical concept when it comes to getting value from AI. Let us consider three scenarios:
Scenario 1, a simple algorithm. The human planner understands how it works and has experience of it working well.
Scenario 2, a complex algorithm. The human planner does not understand how it works, but knows through repeated experience that it does.
Scenario 3, a complex algorithm. The human planner does not understand how it works, and knows that it works only sometimes, but can’t predict when it will work and when it won’t.
We can already see that in scenario 3 it will be hard to induce the human planner to use the technology. And even model 2 might have limited usefulness in periods of volatility and uncertainty. (Witness all of the advanced forecasting models quickly ditched when Covid struck in 2020.)
Within complex challenges like inventory optimization there is actually a tension between simplicity and accuracy. The more variables you factor in, the more complex the algorithms become, and so less comprehensible to humans. Whereas the simpler you keep the algorithms, the less accurate and reliable the results. A lot of technology in this space is in the unhappy situation that the algorithms are already too complex for most humans to follow (even if they were made transparent, which they are usually not), but the results delivered are frequently too approximate or conditional to be trusted and used blindly.
So what should you do? Exciting developments are ongoing and failure to engage with them leaves you at risk of falling behind. The fear of missing out is great.
On the other hand, the promise of fully autonomous supply chains, with all data and systems connected in smart networks that can manage themselves, is still a long way off. As different technologies compete with each other, there is also a risk of committing too fully to the wrong one.
The best approach comes down to finding the right balance between experiment and risk mitigation, while not neglecting the human factor:
If you were hoping to read here that AI is able to take away the difficulty of achieving inventory optimization then, for now and for the foreseeable future, you will be disappointed. But it is not a question of all or nothing. Technology available now can help you achieve a lot.
In the end, what it comes down to is this: to take best advantage of all that AI has to offer today, you need the most powerfully intelligent machine known. The good news? Every one of your people has one between their ears.
The discussion above is not of purely theoretical interest to us at nVentic. We have built our own inventory analytics technology that we leverage to help companies deliver significant improvements to their inventory positions. So what do we do and how do we navigate this tension between complexity and trust? We can split this into what technology we use and how we then use that technology.
We have synthesized the best scientific work on inventory optimization into a series of algorithms that work on large business data sets to identify optimal inventory levels by item. We use a number of techniques commonly referred to as AI, in particular hill-climbing, but we avoid using the expression AI, which is applied to such a wide variety of techniques, from the simple to the very complex, that it risks being meaningless. “We use AI to do it” wouldn’t really tell you anything.
Our technology approach is very much based on the algorithms themselves, which are fully documented and reference the peer-reviewed scientific work underpinning them. We have coded these algorithms in C++, supporting multi-threaded calculations, so machine-near and fast but not hardware dependent. We have also automated a number of routines that produce the analytical outputs in a selection of formats for easy and flexible use by our clients.
Our technology is not a planning tool, but an inventory diagnostic. Because of the many issues with producing numbers that inventory planners can and should trust, we break the algorithms down into their main constituent elements, make these transparent, and then put a lot of effort into building understanding of the results among client teams. Because we work outside of the source data environment the operational data cannot be compromised.
At least as important as our technology itself is what we do with it. Our strongly-held belief is that there is a significant skill gap preventing organisations from taking advantage of the best inventory optimization technology on the market, including our own, without significant investment in people. The input to our technology is data, the output is information, but to get real value from it you need insight and this is where the human brain needs to come back into the picture. Insight into understanding what the analytics means and insight into what you need to do to take advantage of it.
In other words, in order to take full advantage of the best in AI, you need plenty of I.