When all you have is a hammer, everything looks like a nail. The hammer long favored by the supply chain community has been time-series and, as a result, in supply chain circles all problems look like time-series forecasts. The hammering temptation is compounded by the extensive literature that exists on time-series forecasting beyond supply chain use cases.
Incrementalism in supply chain includes improving the forecasting accuracy, improving the service level, reducing the stock level, reducing the lead time. Despite its prevalence among large companies, this approach rarely yields any tangible benefit.
In terms of predictive optimization, most supply chains are stuck in the early 1990s. As we started to address the root cause of predictive optimization failures, Supply Chain as a Service emerged as our business model.
The emergence of a terminology is, at best, a haphazard process. Supply chain is no exception and, in hindsight, a sizeable portion of supply chain terminology is inadequate. Confusing terminology hurts newcomers and seasoned practitioners alike. Newcomers struggle more than they should with accidental complexity. Practitioners may not realize that the premise of their field ismore shaky than it appears.
Lokad employs a team of around 30 supply chain scientists. The scientist is responsible for the data pipeline, the forecasts, the economic modelling and the end-game decisions. But how many SKUs do they manage?
My first professional supply chain experience happened back in 2004. At the time, I was a computer science student at the Ecole Normale Supérieure (ENS), a university in Paris. My interests covered a wide range of wholly theoretical subjects, yet, I was also intrigued by the idea of testing out those theories “in the wild”.
Captain Obvious has been working overtime in supply chain. His natural leadership has inspired many who are now following the same path. Yet, underneath the uniform, there is little to be found but a great deal of confusion.
Many domains are complex, irredemiably so, and supply chain is certainly one of them. So what could an itemized list of tricks help you to achieve?
I would like to share some insights into my motivations, as a CEO, for undertaking such a series of supply chain lectures. It boils down to the four key challenges faced by Lokad.
Lokad’s supply chain practice is to refresh all the data pipelines - forecasts included - at least once a day. Why is that?
What are the importance of feedback loops and how do we use them in our forecasts
What solutions can Quantitative Supply Chain provide for large manufacturing companies?
