Apr 10, 2019

Differentiable Programming in Supply Chain (Part 1/3)

Differentiable Programming is the descendant of Deep Learning. It has unlocked a series of challenges that were previously seen as “unsolvable” and has paved the way for considerable progress and superior numerical results in the world of supply chains.

Mar 13, 2019

Data Security in Supply Chain

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.

Mar 6, 2019

Blackboxing and Whiteboxing

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'.

Feb 6, 2019

Data Lakes in Supply Chain

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.

Jan 16, 2019

Terabyte Scalability for Supply Chains

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.

Dec 12, 2018

Generations of Machine Learning

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 However, this approach has been superseded by a series of superior forecasting approaches.

Aug 8, 2018

Data Requirements for Supply Chain Optimization

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.

Jul 12, 2018

Software Frankensteinisation in Supply Chain

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.

Jul 5, 2018

Probabilistic Forecasting for Supply Chains

Optimizing supply chains relies on having insights about the future. Classic forecasts dismiss uncertainty entirely, and assume that the forecast is perfectly known. In contrast, probabilistic forecasts embrace uncertainty, and reflect that supply chain optimization should remain robust when faced with unexpected events.

Jun 27, 2018

Internet of Things for Supply Chains

For a supply chain management practice to be performant, managers need to have access to the position of every single asset. Unlike classic electronic inventory management, Internet-of-Things (IoT) offers the possibility to gain real-time visibility on all assets, vehicles included.

Jun 20, 2018

Data Preparation in Supply Chain

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.

May 23, 2018

The User Experience Paradox

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.