Supply Chain science and tech
Properly preparing the data is a requirement to achieve success for any data-driven initiative. When considering supply chain challenges, data preparation is difficult because it involves complex enterprise systems that have not been designed with data science in mind.
In this episode we talk about this major buzzword and its application to supply chains.
Forecasting promotional demand is necessary in order to allocate the correct amount of stock. However, time-series forecasting models are typically not a good fit to address pricing-related demand patterns. More complex machine learning forecasting models are needed to properly take into account past promotions, and to reflect the upcoming impact of those that are planned.
Supply Chain Management (SCM) systems feature complex user interfaces. Among them, demand forecasting subsytems are not only complex but complicated as well. Better user inferfaces are needed to tackle this complexity.
Modern supply chains are complex, and the most direct answer to complexity is a 'specialization of labor'. Unfortunately, this approach results in 'silos' that fail at delivering decisions that maximize returns for the company.
Supply chain challenges are frequently quantitative and data driven. This makes them a good fit for a data science practice. However, understanding the business is a frequently overlooked aspect of the data science practice in supply chain.
Blockchains and Bitcoin do have applications for supply chains, but not the ones most prominently put forward by software vendors.
Artificial Intelligence (AI) is an umbrella term that covers many high-dimensional statistical methods such as Deep Learning or Differentiable Programming. These methods can be used in various ways to improve the operational performance of supply chains. However, both problems and solutions differ vastly from mainstream AI problems such as Natural Language Processing (NLP).
Achieving a highly accurate demand forecast is classically considered as the first step towards the optimization of a supply chain. More accurate forecasts are expected to reduce stock levels and to improve service levels.