The Quantitative Supply Chain Manifesto

The Quantitative Supply Chain Manifesto












Home » Resources » Here By Joannes Vermorel, last updated May 2017

We are uncovering better ways of optimizing supply chains by doing it and helping other to do it. Through this work, we have come to value that:

  1. All possible futures must be considered; a probability for each possibility.
  2. All feasible decisions must be considered; possibilities v.s. probabilities.
  3. Economic drivers must be used to prioritize feasible decisions.
  4. Being in control requires automation of every mundane task.
  5. A supply chain scientist must take ownership of the numerical results.

1. All possible futures must be considered; a probability for each possibility

Customers themselves don’t always know for sure what they will buy, when they will buy, or if they will buy at all. Uncertainty cannot be denied and should be embraced instead. Yet, uncertainty does not imply that all futures are equally probable. Some futures are more likely to take place than others. The goal of the forecasting process is to assign a probability to every single possible future. Modern computers have bewildering processing power, and while assessing all those probabilities requires significant processing capabilities, this no longer represents a blocking issue.

2. All feasible decisions must be considered; possibilities v.s. probabilities

Every unit of goods that you have in stock involves making at least one decision per day: to keep the unit where it is or to do something else with it. Every unit that you don’t have in stock, whether because it has not yet been purchased or because it has not yet been produced, also requires making one decision per day: whether this extra unit should be “materialized” or not. All those decisions should be considered every day, for every product, for every location, for every supplier, for every route. Again, while processing power might have been an issue in the past, it is not an obstacle anymore. Hence, all possible decisions should be examined in relation to all the possible futures and their respective probabilities.

3. Economic drivers must be used to prioritize feasible decisions

Zero stocks, zero stock-outs, zero delays are only rather theoretical limits of your supply chain; those are not practical, feasible - and certainly not profitable - options. One key supply chain goal is to minimize the dollars of error, not the percentages of error. Thinking that improving percentages of error automatically translates into cost savings is a fallacy. Inventory costs must be balanced with stock-out costs. Purchase prices must be balanced with purchase quantities. Any optimization is fundamentally dependent on the metrics that are being optimized. In order to achieve such a business-minded optimization, economic drivers need to be introduced. And thanks to those economic drivers, it now becomes possible to prioritize all feasible decisions in relation to their expected ROI. Refining economic drivers may take as much effort as executing the optimization itself; however this is the price to pay for making sure that the results are aligned with the economics of the business.

4. Being in control requires automation for every mundane task

Automation is the key for giving management back the control of their own supply chain. If taking care of the unending stream of supply chain decisions requires an unending stream of manual entries, then supply chain practitioners are the slaves of their own supply chain solution. Being required to manually supplement the solution with unending manual entries is the opposite of being in control.

In fact, being in control means that all strategic insights are properly factored into the millions of decisions being made in relation to your supply chain. Whenever your market situation changes, your strategic insights must be revised too. Revising a supply chain solution in order to account for the new elements in a company’s strategy should be painless, ideally done within hours, not within weeks. What’s more, there should be no limit to the amount of expert knowledge that can be injected into the automation.

5. A supply chain scientist must take ownership of the numerical results

If your supply chain is significant and has been operating for years, then preparing your supply chain data is a major undertaking in itself. Very few practitioners realize how much depth tends to be present in data, and as a rule of thumb, a "traditional" IT department almost never does. The primary challenge lies in establishing the semantics of the data: what data actually mean. The semantics are dependent not only on the software being operated, but also on the many operational processes being followed as well. Uncovering and documenting the data semantics requires considerable skill. Moreover, the delivery of the numerical results requires an adequate modelling of the supply chain, which in turn requires additional skills. Having a supply chain scientist take ownership of the delivery of numerical results is critical for ensuring the success of a project. Without the necessary supply chain science competencies, an initiative is at risk of suffering from unidentified subtleties that may be linked to data, to supply chain processes or to modelization artifacts; which in turn may wreak havoc in supply chain operations once results are put in production.

This manifesto summarizes the philosophy adopted by Lokad for tackling supply chain challenges. Our technology provides the building blocks for implementing this vision in your company. Our probabilistic forecasting engine assigns a probability for each possible future. Our numerical solvers consider and score all possible decisions. End-to-end automation is achieved through Envision, our programming language. Our team provides the expertise and experience necessary for executing the initiative. We will help you craft the metrics your company needs. We will help you make the most of the data you have; even if it's not yet the data you wish you had.