By Joannes Vermorel, December 2016
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:
- All possible futures must be considered; a probability for each possibility.
- All feasible decisions must considered; an economic score for each possibility.
- There are no absolutes, only relative costs and opportunities.
- Being in control requires automation of every mundane task.
- Data requires more efforts and brings more returns than you expect.
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bewildering processing power, and while assessing all those probabilities requires significant processing power, this no longer represents a blocking issue.
2. All feasible decisions must be considered; an economic score for each possibility
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.
3. There are no absolutes, only relative costs and opportunities
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 price must be balanced with purchased quantities. Any optimization is fundamentally dependent on the metrics that are being optimized. Bypassing financial metrics is not an option because every decision yields its own costs and benefits, while every indecision generates lost opportunities. Metrics are not given but hard-earned. Refining the metrics to be optimized may require as much effort as refining the optimization itself.
4. Being in control requires automation for every mundane task
Automation is the key for giving back management 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.
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 any market experiences change, the 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. Data requires more efforts and brings more returns than you expect
If your supply chain is significant and has been operating for years, then preparing your supply chain data is a major undertaking. Very few practitioners realize how much depth tends to be present in data, and as a rule of thumb, the IT department almost never does. The primary challenge lies in establishing the semantic of the data: what data actually means. The semantic is dependent not only on the software being operated, but also on the many operational processes being followed as well.
Yet, the upside is that uncovering the precise semantic of data nearly always yields benefits on many different levels - control, reliability, productivity - with most of the gains not even planned for. Subtle causes that have been wreaking intermittent supply chain havoc for years are finally uncovered. Possibilities for simplifying processes and systems become visible at last. Finally, management gets figures that can finally be trusted to support more ambitious project initiatives.
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.