Supply Chain science and tech
Data is both an asset and a liability. Supply chains require extensive historical records for tracability purposes and to ensure the accuracy of demand forecasts. However, data leaks are damaging events both for the company and its clients. Supply chains have to protect both their physical and software infrastructures.
Any nontrivial demand forecasting model becomes a black box for supply chain practitioners, that is, an opaque subsystem that produces numbers that are difficult to understand and to challenge. Whiteboxing, as part of the Supply Chain Management practice, is the answer to this problem. Practitioners don't need to understand the 'how' but need to understand the 'why'.
Pricing optimization is typically not considered as part of the Supply Chain Management (SCM) practice. Yet, pricing is a factor that strongly influences customer demand. Thus both production capacities and stock levels are highly dependent on prices, and must be jointly optimized.
Data lakes are data storage technologies intended for bulk reads and bulk writes. They are particularly well suited to address supply chain challenges, because many situations require an inspection of the company's entire history of orders and stock movements.
Supply chains are complex systems made of many moving parts: goods, people, machines. POCs (Proofs of Concept) routinely fail when attempting Quantitative Supply Chain initiatives because problems get displaced instead of getting solved.
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The relevant amount of historical data when considering large supply chains frequently exceeds one terabyte. As a result, inventory control requires two distinct flavors of software: transactional software (e.g. an ERP) to manage the resources, and predictive software (e.g. Lokad) to optimize the resources.
Safety stocks are an inventory optimization method that enforces an extra quantity of stock beyond the expected demand in order to maintain a target service level. This method relies on key statistical assumptions about the demand forecast, most notably that the error is normally found in the distribution.
Machine learning is an umbrella term that includes diverse algorithmic approaches. In supply chain, the historical way of doing machine learning was time-series forecasting. However, this approach has been superseded by a series of superior forecasting approaches.
The lead time is the total amount of time, typically counted in days, associated with the inventory replenishment cycle. The amount of stocks that a supply chain needs to operate tends to be roughly proportional to its lead times. Accurately estimating future lead times is critical for accurately estimating the amount of inventory needed to fulfill future demand. However, it is a fundamental factor that is often overlooked by companies, with a far greater importance being placed on forecasting.
In supply chain, the service level defines the probability of not hitting a stock-out during the next ordering cycle. However, the fill rate defines the fraction of the customer demand that will be properly served. Service levels and fill rates are distinct, and should not be confused.
The Min/Max inventory method defines two stock levels: first, a replenishment threshold, referred to as the 'min', and second, a replenishment target, referred to as the 'max'. Yet, despite its popularity, this method is not suitable for most modern supply chains.