- Manifeste pour la logistique quantitative
- Le test de la performance logistique
- Synthèse la logistique quantitative
- Prévisions probabilistes généralisées (en anglais)
- Optimisation fondée sur des décisions
- Moteurs économiques
- Préparation des données
- Ingénieur données logistiques
- Calendrier d'un projet classique
- Livrables projet
- Évaluer les résultats
- Antimodèles logistiques

- Analyses des ventes
- Analyses du stock
- Analyses de la marge brute
- Analyses des fournisseurs
- Prévoir la demande et les délais d'approvisionnement
- Optimisation avancée de la tarification
- Données utiles à la tarification
- Une première stratégie de tarification (en anglais)
- Test A/B de la tarification
- Stratégies courantes de tarification (en anglais)

- Optimisation avancée de la tarification
- Données utiles à la tarification
- Une première stratégie de tarification (en anglais)
- Test A/B de la tarification
- Stratégies courantes de tarification (en anglais)
- Prévision avancée du stock
- Rapport des priorités de commande
- Ancien format des fichiers d'entrée des prévisions
- Ancien format des fichiers de sortie des prévisions
- Choisir les taux de service
- Gérer vos paramètres de stock
- Ancien rapport Excel de prévision
- Utiliser les Tags pour améliorer la précision
- Les bizarreries des prévisions classiques
- Les bizarreries des prévisions quantiles
- Gérer le biais des ruptures de stock sur les prévisions
- Agrégations au jour, à la semaine et au mois

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From the quantitative supply chain perspective, economic drivers represent the financial quantification of the positive and negative outcomes of supply chain decision. Economic drivers shape supply chain optimization challenges into tractable optimization problems; where the optimization metric happens to be financial. Through the quantification of the economic drivers, it becomes possible to assess the

While it might seem counter-intuitive,

Generic statistical metrics (ex: MAPE, MAD, MSE, etc.) don’t have any business affinity. Simply put, these metrics put an emphasis on the

Example. Let’s consider a product sold in a store with only 1 unit sold on average per week, with a lead time of 1 day (every day replenishment). The *best* median demand forecast for this product for any given day is *zero units*. An *average* forecast might have produced a fractional quantity of 1/7, but the median forecast just indicates zero. While the 1-day demand to be covered is very close to zero, the actual stock that needs to be held to properly service customers is probably much larger; 2 or 3 units are likely to be required in order to meet customers’ expectations in terms of the quality of service. The problem here is not that the forecast is inaccurate, because if the demand is stationary and truly random then the forecasts we just mentioned are perfectly accurate from a statistical perspective. The problem is that business-specific drivers have been ignored.

As far as numerical optimization goes, there is a general principle that states that it’s always preferable to optimize the problem as a whole, rather than to optimize parts of the problem in isolation. However, this point only remains true as long as addressing the optimization challenge from a monolithic perspective remains technically feasible. Yet, most supply chain literature, and us as well, tends to agree on the fact that demand forecasting is a complicated undertaking which combines statistics, algorithms, software engineering, and possibly distributed computing when a cloud computing platform is involved. Thus, isolating the demand forecasting aspect of the challenge offers the possibility to deliver advanced demand forecasts without burdening the technology with a myriad of domain-specific considerations.

Likewise, a similar advantage is obtained by isolating the supply chain optimization logic from the demand forecasting logic. As supply chain optimization remains "protected" from the technicalities involved in demand forecasting, this makes it possible to delve much deeper into the fine details of the economic drivers: caps on storage space, price breaks, varying stock-out costs, varying obsolescence costs, etc. Having a more detailed understanding of the economic drivers generates better decisions that are more closely aligned with the risks and opportunities of a company.

Example: Let’s consider a company that has two warehouses and serves exactly the same parts from both warehouses. The two warehouses are located nearby, but out of habit, all customers tend to always order the parts they need from the same warehouse. When a part becomes no longer available in this specific warehouse, the warehouse staff call the other warehouse to investigate the availability of the part in the other warehouse, and if the part is available there, it is then shipped to the warehouse that finds itself out of stock.

Classic forecasting tools put great emphasis on mean or median forecasts, which completely misses the point from a business point of view. Indeed, no matter how accurate this type of forecast might be, if the business scenario of interest lies at the statistical extreme, then the forecasting tool will fail to provide the relevant statistical projection to quantitatively assess the probable financial output of the business scenario. In contrast, probabilistic forecasting tools assess the respective probabilities for all possible demand levels, which in turn, offers the possibility to assess all possible business scenarios. In practice, quantitative supply chain require probabilistic forecasts to work at all.

Unsurprisingly, probabilistic forecasts require a lot more computing resources that their classic single-valued counterparts, because, in a way,

One of the most common supply chain decisions consists of ordering one more unit for one item. If there is immediate demand for the ordered unit, the company will service the unit at a profit. This represents the gain associated with the ordering decision. If there is no immediate demand for the item, the company will have to incur the carrying costs of stocking this extra unit. This represents the cost associated with the ordering decision. Establishing the economic drivers for an ordering decision consists of writing down both the resulting gains and the resulting costs of the decision for a given demand scenario.

Besides gains and costs, constraints also shape the range of acceptable supply chain decisions:

**Storage capacity**: Stores and warehouses have maximum capacities, preventing any additional ordering that goes beyond a certain amount of stock.**MOQs**: Suppliers only accept orders that are beyond minimum order quantities - expressed, for example, in number of units or amount ordered. Those MOQs can also be interpreted and modeled as fixed costs on supplier purchase orders.**Capital costs**: The company has limited access to liquidity, and hence needs to cap its inventory capital allocation. Gaining access to more capital can be highly time-consuming for the management of the company, and may not be aligned with the strategic orientations either.**Transport capacity**: When importing goods from overseas, orders may have to be properly sized so that they can fit exactly within one container. Containers have both a maximum weight and a maximum volume. Containers can also be interpreted as a form a fixed cost on purchase orders.

Economic drivers need to account for all of the above-mentioned constraints, and many more in practice. Indeed, if constraints are not accounted for, then the system that combines the demand forecasts with the economic drivers would most likely suggest decisions that could not actually be executed in reality; such as trying to fill a warehouse beyond its storage capacity.

Economic drivers are incredibly diverse however. In order to manage such diversity, Lokad has introduced Envision, a domain-specific programming language that is dedicated to supply chain optimization. The visible output of Envision consists of producing dashboards, however, Envision’s primary function is to embed ecnomic drivers into forecasts through scripts so that optimized decisions – e.g. quantities to be reordered today - can be computed automatically.

The proper combination of economic drivers and probabilistic forecasts requires policies that can take advantage of those data. For example, the prioritized ordering policy is particularly adequate to deliver quantities to be ordered that fully balance the business inventory risks with the demand forecasts.

In practice, reviewing and formalizing the economic drivers, combining these drivers with probabilistic forecasts, qualifying and sanitizing historical data, generating optimized decisions that match the exact set of applicable business constraints; all these tasks are performed by Lokad’s team through a monthly subscription to an inventory optimization service.