Three steps for a successful supply chain data science journey

A data-driven mindset enables more efficient supply chain or inventory management in your organization. Being data-driven is about building tools, abilities, and, most crucially, a culture that acts on data.

Our experts have identified three key steps for a successful supply chain data science journey:

Supply chain data science – step 1: Data collection

The right dataset is not only trustworthy and relevant to the question but also timely, accurate, clean, and unbiased. Here are some important considerations when collecting data:

1. First, decide what details you want from the data. You’ll need to choose which topics the information will cover and which questions will be answered – who do you want to collect it from and how much data you need? Your goals – what you hope to accomplish using your data – will determine your answers to these questions.
2. In the early stages of your planning process, you should establish a time frame for your data collection and a schedule for when you’ll start and end your data collection.
3. You should base the choice of data collection method on the type of information you want to collect, the time frame over which you’ll obtain it, and the other aspects you determine.
4. Once your plan is finalized, start collecting data, and be sure to stick to your plan and check on its progress regularly. You may want to make updates to your plan as conditions change and you get new information.

Supply chain data science – step 2: Data cleaning

This step is vital to ensure that the answers you generate are accurate. When collecting data from several streams and with manual input from users, information can carry mistakes, be incorrectly inputted, or have gaps. Data cleaning is not simply about erasing information to make space for new data, but rather finding a way to maximize a data set’s accuracy without necessarily deleting information.

Supply chain data science – step 3: Data integration

This step refers to the technical and business processes used to combine data from multiple sources such as web data, social media, machine-generated data, and data from the internet of things (IoT), into a single framework to provide a unified, single view of the data. Remember, it’s one thing to have access to lots of data, it’s another to use it. Data is usable when it is accessible, in other words:

1. Joinable: Data must be in a form that can be joined to other enterprise data when necessary.
2. Shareable: You need a data-sharing culture within the organization so that data can be joined, such as combining customers’ clickstream with their transactional history.
3. Query-able: There must be appropriate tools to query, slice and dice the data. All reporting and analysis requires filtering, grouping, and aggregating data to reduce the large amounts of raw data into a smaller set of higher-level numbers. This helps our brains comprehend what is happening in a business. Retailers need to be able to see trends or understand differences among customer segments. Analysts require tools that allow them to compute those metrics relatively easily.

When are you going to start your supply chain data science journey and make data-driven decisions? Find our how we can support you!