Discussion with Joannes Vermorel at the Ecole des Mines de Paris (French)

Jeune entreprise française de 50 personnes vendant l’essentiel de ses prestations à l’étranger, Lokad optimise la supply chain de ses clients. Au travers d’une offre originale d’experts assistés par une plateforme logicielle propriétaire, elle prend progressivement en charge la responsabilité de la supply chain en vue de l’opérer au quotidien de manière optimisée, sans trahir les intentions de l’entreprise. Les gains pour cette dernière sont considérables. Dans cette discipline à fort contenu scientifique, la cible de l’optimisation ne cesse de changer et d’être contrariée par divers acteurs et événements. La bonne démarche, plutôt contre intuitive, consiste notamment à pousser une hypothèse jusqu’au bout afin de constater ses conséquences, souvent absurdes et non désirées, pour pouvoir ensuite reformuler une hypothèse plus pertinente. Un apprentissage collectif improbable qui déroute… avant de séduire et convaincre jeunes ingénieurs et clients exigeants.
Séance organisée en partenariat avec le mastère spécialisé Management industriel et Systèmes logistiques de Mines Paris - PSL.
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Timestamps
00:00:00: Presentation of Lokad and the role of Supply Chain Scientists.
01:01:10: Questions from the audience.
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[@gobreg]

Try to understand it using auto translate 😂. Lokad content always interesting for me since not a lot of people cover material management in aviation MRO

[@ttarabbia]

- What are these typical operation research topics that I'm saying don't work? - 11:55 - First Time series in demand forecasting - Doesn't work well for unusual demands - For example, where you have consumers who buy switches one at a time and construction companies who buy 500 at a time. - The order for 500 is announced well in advance, while the one offs are immediate, yet a time series will not differentiate between these cases and squash the demand together - For substitutions in fashion - if the wrong size is there - can't sell it, but if its a slightly different color, the customer wil likely still take it - Diapers and market basket effects - Diapers are expensive and have high brand loyalty on average - However it's not the loss of the diaper sale that is the most impactful - it's the fact that parents will then not buy all of their other groceries at the hypermarket if the diapers aren't there. - Time series also hides the fact that the future is blurry and in which direction - Deterministic forecasting works pretty well with consistent values - But a deterministic forecast of something like a soup in a supermarket with promos and demand swings of >100% it makes no sense to generate a point forecast - 1st order effects can be measured such as impact of promotion, but those indirect 2nd order effects e.g. consumer behaviour changes are hugely critical to take into account - 27:15 - For example a spare parts organization of an aircraft company had a recommendation to purchase a part, the employees said absolutely not! - It seemed a reasonable recommendation to make - however it was a spare part for a 747 - and since the part had a 30 year life, and the 747s are being deprecated within the next 10 years there was no need for it - Another interesting effect in airlines is the one-way standard. - A plane is allowed to have a part matching the old standard, however as soon as a part passing the new standard is placed - you must only use new parts going forward. - This means if you have inventory of old and new parts - each time you replace with a new part- you are modifying the composition of your demand for spare parts across your flotilla - Just because we don't know what to optimize for, doesn't mean we can't find out - 29:17 - It just can't be done in a top down cartesian way in which we split the problem down into constituent pieces and come up with an answer that way - We arrived at the need for supply chain scientists to operate under the idea of Experimental Optimization - To optimize you must create logic to generate a decision - you will then have people who object - Their reasons for objecting are typically correct - Use anecdotes to find the reasons it won't work - Then we will feedback into the dollarized/financialized decision making system to add the constraints and requirements that will meet the edge cases which are critical to that customer - For example with the spare parts problem we had taken into account the lifespan of the part, but not the lifespan of the plane itsself - In our supply chain books we tell you the demand is a Gaussian, lead times are a Gaussian - is there any way to falsify this? no, it's in the abstract, divorced from reality - 31:50 - Our goal is to make a mathematical model , maximized for modeling reality, not necessarily mathematical simplicity - An important part of the process is making sure to present the results to the customer in a specific way - not just "here are your optimal stock levels" but - "where should I put my first Euro of inventory investment?" - 80% customers know what they're doing - so this list of prioritized investments in purchasing, manufacturing, or inventory levels is reasonable - though sometimes they may be missing something obvious - Working with a german MRO company - Retrofit and repairs are 2 different things - Repairs are - engineer says this part needs to be fixed, replace - Retrofits are - the manufacturer has some doubts about a part and requires a push to replace within a month - You can't mix push and pull demands together - they act completely differently - You push a big spike of parts - and now all of your spares for that part are synchronized

[@Lokad]

Awesome summary! Cheers, Joannes