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In supply chain optimization, the economic drivers represent a set of factors that define the positive and negative outcomes of a decision, which typically involves either ordering extra units of goods or moving units from one location to another. From a quantitative perspective, economic drivers shape the supply chain optimization challenge into a financial issue with certain constraints. Economic drivers are intended to be combined with demand forecasts, ideally probabilistic demand forecasts, in order to generate optimized decisions. 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 (see the example below). However, this point only remains true as long as addressing the optimization challenge from a monolithic perspective remains technically feasible. Yet, most scientific literature, plus our own experience here at Lokad, 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 business optimization logic from the demand forecasting logic. As business 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 (1). 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 (see Lokad’s quantile grid technology and our page about probabilistic forecasting) assess the respective probabilities for all possible demand levels, which in turn, offers the possibility to assess all possible business scenarios.

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:

- Stores and warehouses have maximum capacities, preventing any additional ordering that goes beyond a certain amount of stock.
- Suppliers only accept orders that are beyond minimum order quantities (expressed in number of units or amount ordered).
- The company has limited access to liquidity, and hence needs to cap its inventory capital allocation.
- When importing goods from overseas, orders may have to be properly sized so that they can fit exactly within one container.

Economic drivers also need to account for all of the above-mentioned constraints. 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.

- ABC analysis
- Backorders
- Container shipments
- Economic drivers
- Economic order quantity
- Fill Rate
- Financial impact of accuracy
- Inventory accuracy
- Inventory control
- Inventory costs (carrying costs)
- Inventory turnover
- Lead time
- Lead demand
- Min/Max Planning
- Minimal Order Quantities (MOQ)
- Multichannel Order Management
- Optimal service level formula
- Perpetual Inventory
- Phantom Inventory
- Prioritized Ordering
- Product life-cycle
- Quantitative supply chain
- Reorder point
- Replenishment
- Safety stock
- Service level
- Stock-keeping unit (SKU)
- Stock Reward Function

- Backtesting
- Continous Ranked Probability Score
- Data preparation
- Forecasting accuracy
- Forecasting methods
- Obfuscation
- Overfitting
- Pinball loss function
- Probabilistic forecasting
- Quantile regression
- Seasonality
- Time-series

- Bundle Pricing
- Competitive Pricing
- Cost-Plus Pricing
- Decoy Pricing
- Long-term maintenance agreement pricing
- Long-term pricing strategies
- Odd Pricing
- Penetration Pricing
- Price Elasticity of Demand
- Price Skimming
- Repricing software (Repricer)
- Styling Prices for Retail