Supply Chain science and tech
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.
Growth, and more generally trends, needs to be taken into account in order to deliver accurate demand forecasts. However, growth; as a statistical pattern, proves to be more difficult and more elusive to capture than other well-known patterns such as seasonality.
Seasonality is one of the major cyclical patterns that can be used to improve forecasting accuracy. Most supply chain processes tend to be seasonal to some degree. Not only because of demand, but also lead times.
The ABC analysis is a widespread inventory categorization method used in many supply chains. Its intent is to prioritize management's attention to where it matters most. Yet, this method has many flaws, and can no longer be considered as state-of-the-art.
While the physical infrastructure supporting most supply chains is highly modular, their software infrastructure counterpart, e.g. inventory control or demand forecasting systems, tends to be monolithic and brittle. As a result, large scale software supply chain failures are still ongoing.
Sales and Operations Planning (S&OP) is a corporate practice intended to deliver superior supply chain execution by leveraging a deeper alignment with other divisions beyond supply chain - most notably sales, finance and production. Despite the claims from multiple vendors that the best-in-class businesses operate under S&OP, most implementations suffer from similar flaws, which are intrinsic to the very nature of S&OP.
Supply Chain Management (SCM) practices are increasingly data-driven and quantitative. New roles have appeared such as the Supply Chain Scientist. Companies need to make strategic decisions about whether these competencies are developed in-house or externalized.
Predictive supply chain optimization relies on heavily prepared data. The purpose of this data is twofold: first, the historical supply chain data is used to build the forecasting models, second, the data describing the supply chain's current state is used to drive the optimization of the decisions.
New products do not have a sales history that can be represented as a time-series. As a result, time-series forecasting models don't work for new products. Forecasting demand for new products requires alternative forecasting models capable of leveraging data, such as product attributes, that do not come as a time-series.
A good Supply Chain Management (SCM) practice includes a healthy dose of conservatism as the cost of failure tends to be high. Yet, rejecting all change is not an option in a world where innovation drives laggard companies out of business.
Managing supply chains and optimizing them is particularly challenging from a software perspective. The 'Software Frankensteinisation' refers to the technological decay that plagues entreprise software when faced with its own evolution over multiple decades.