For a long time, supply chain specialists have been dependent on traditional forecasting models and techniques to estimate demand for their goods and services. Both the digital transformation and the fourth industrial revolution –industry 4.0– are advancing rapidly. This has a fundamental influence on the way human beings live and work. The introduction of digital identifiers and the internet of things (IoT) produced a huge amount of data. One of the main impacts of this situation is that supply chain management decision-making is increasingly driven by data instead of experience. However, the traditional strategies and methods used in the different parts of the supply chain impose many constraints that prevent gaining full advantage of data.
Join Dr. Christoph Kilger and Dr. Boris Reuter as they discuss the supply chain performance gap, and where you can start closing it. Most businesses collect large amounts of data, yet supply chain analytics doesn't always translate into concrete measures that are executed. How can you start digital execution management that leads to real results?
A digital twin is a highly detailed digital replica of any system that uses comprehensive data to emulate the working of the system at all times. Therefore, a supply chain digital twin is a simulation model of a supply chain. You feed the model real-time data from all sources and systems of the organizations that can exactly work out the effects of macro and micro-changes on the system using advanced analytics and learning models.
Even though it's hard to pin down an exact percentage, research has shown that on average, about half of strategic initiatives fail to be executed successfully. For those conceptualizing strategies, this is, of course, a sobering statistic: this means half the strategic work you're doing is not leading to the desired results. In this article, we'll look at some of the reasons why strategic measures aren't executed, and help you ask the right questions to increase the impact of your strategies.
In our ongoing series, the AIO Data Science teams publish tutorials that go along with AIO on GitHub. Today, aioneer Maryam introduces read_and_write. This function is necessary because often, CSV files provided by clients contain bad_lines. These are lines with too many fields, like, for instance, too many commas. As a result, these CSV files cannot be read by Qlick, so they need to be cleaned. Doing this one by one would be very time-consuming. Secondly, sometimes it is necessary to combine data files if we receive data in several files or sheets. These need to be concatenated. It would be great to be able to do this in a simple function. In the tutorial, Maryam shows you how to do this!
At aioneers, we create a lot of dashboards and do lots of machine learning tasks. Some of those analyses are done regularly. To deliver these dashboards and machine learning, we need to do a lot of data transformation, like cleaning data and joining tables. To automate data transformation, we use Databricks clusters to run Python and, sometimes, R scripts. Databricks allows us to automatically run data transformation scripts on schedule, but it is missing one essential feature: email notification on how the data was loaded.
AIO on GitHub is our open source project to support the supply chain community with data science tools. Bit by bit, we are releasing content and showing you how to use it. I have created a hands-on tutorial showing you how to do an XYZ analysis using our tools.
Today on our blog: watch Titus and Sebastian explain why we started AIO on GitHub, our OpenSource project for the supply chain community.
A comprehensive, forward-looking open-source library of code for supply chain scientists does not exist, which means we are all working from scratch for every project. We are taking old scripts, copying them, and adapting them for every new project.
The introduction of new technologies changes the way the supply chain is organized. Cloud computing has initiated a movement from functional supply chains to network-based ecosystems. Through cloud technology, information-based tasks like forecasting, planning, order management, analytics can easily be shared with specialized service providers – enhancing the performance of the supply chain and reducing costs. Cloud technology accelerates Taylorism, giving rise to Supply-Chain-as-a-Service (SCaaS).