Recently, Supply and Demand Chain Executive ran an article on Embracing Complexity that pointed out that supply networks that are becoming increasingly extended and complex; integration between companies and their trading partners is becoming deeper at the systems and process levels; and emerging technologies like radio frequency identification are producing ever-growing mountains of supply chain data and that these and other factors threaten to overwhelm the systems that companies rely on to monitor and manage their flows of goods and 20th century systems may be inhibiting companies from moving toward a 21st century supply chain.
In addition, it presented Lawrence Davis’, a senior fellow at NuTech Solutions, insights into problems with current supply chain technologies. In short, he believes that contemporary solutions do not allow companies to optimize at the appropriate level of aggregation and that companies should be able to use solutions to optimize across their sourcing and procurement, production and distribution processes all at the same time; that software solutions that optimize based on deterministic assumptions about how long it will take for any given process to be completed produce “perfect” schedules that do not allow for breakdowns of machinery, traffic jams, defective parts, and other real-world assumptions; and that stochastic simulations which employ embedded agents that follow the company’s business rules are required.
They got the problems right, but I’m not sure I agree with the proposed solutions. Here’s a short list of reasons why.
- Optimizing at the appropriate level of aggregation has always been a discipline-independent problem and we’ve always managed. It’s as much a process problem as a technology problem. It all comes down to using appropriate levels of abstraction that allow us to connect larger and larger problems. And it works. You don’t need to simultaneously optimize all of your categories and all of your lanes – a problem you can’t solve. You can optimize all of your buys using high-order freight approximations, then collectively optimize your freight costs and distribution network.
- Deterministic models can be used on approximations and ranges as well as precise models. Yes, the results are still “perfect ranges”, but you can capture most of the likely outcomes. Moreover, none of the technologies proposed will capture every exception and you’ll still need exception management.
- Stochastic simulations are a good methodology for determining what could go wrong, but the key is identifying a set of collaborative systems that can embed the company’s business rules – because, as I just said, the processes are as important, if not more so, than the technology.
- The technologies proposed – “genetic algorithms”, “evolutionary computation”, and “deterministic simulation” are not silver bullets – just like the ERP was not the silver bullet you needed to manage your supply chain. They have their uses, but they are not that much better than today’s technologies, if they are better at all (as they all have their drawbacks).
- You’ll never be able to optimize everything. For that, you’d need a model that accounts for everything (and first of all, we can’t model the market), then you’d need an expensive High Performance Computing Cluster with hundreds (or thousands) of processors and a significant amount of memory, and finally you’d need an algorithm that can take advantage of the highly parallel machines – and you’ll quickly find that most of today’s optimization technologies, or at least the sound and complete ones, do not have efficient massively parallel implementations.
It’s true we still have a long way to go in supply chain, and that we do have to embrace technology, but we have to be careful of over-relying on new technologies, particularly those that have drawbacks as significant as the advantages they are being promoted for, to solve all of our problems. Although some things change, some things will stay the same – and the constant is that no matter what, we are going to need more brain power and good old fashioned human ingenuity to get to the 21st century supply chain.
One can wish it were otherwise, but as a technologist and former academic who could spend countless posts educating you not only on “genetic algorithms”, “evolutionary computation”, and “deterministic simulation” but also on “fractal geometry” (the basis for NuTech’s logo), “chaotic dynamical systems”, and “complexity theory”, it’s not the case. Technology is just a tool – the real solutions will come from the brains who can identify the problems, identify the process solutions, and then put the appropriate technology in place to back it up.