The world of supply chain management is moving at a breakneck pace. With advancing digital transformation trends, leveraging data to drive everyday decisions is no longer optional: it’s crucial to the longevity and sustainability of your supply chain operations.
Today, supply chain managers have access to tools we could only have dreamt of a few years ago. ETL (extract, transform, and load) platforms for advanced data analytics, cloud storage services for robust storage, and other industry-specific data visualisation tools are now commonplace in warehouses and other facilities, allowing supervisors and business owners to keep the guesswork out of supply chain management.
To fully take advantage of your supply chain’s performance data, it’s vital to invest in the right tools, training, and data collection processes for your enterprise and personnel. In this article, we’ll go through some basics of data interpretation for supply chain managers.
Invest in Tailored Data Solutions for Your Supply Chain
To fully utilise data for supply chain management, you need access to the right tools. The good news is that with the advancement of AI and machine learning technologies, there are thankfully many cutting-edge solutions available that can help supply chain managers maintain dynamic data interpretations that ultimately aid in identifying accurate correlations and making more informed business decisions.
For instance, you can use an ETL platform to collect, combine, and analyse data from multiple sources. Today, advanced ETL platforms utilise intelligent capabilities like generative AI to make the data analysis process easier for supply chain managers.
You can ask your data solution questions just as you would ChatGPT and get an answer in easily understandable sentences based on the data the platform has collected from your everyday operations. This saves supply chain managers from having to delve through the figures themselves, streamlining business development processes over the long term.
Using the capabilities of IIoT (Industrial Internet of Things) technologies, your ETL platforms can also gather data from a wide range of connected devices, including sensors attached to your machines, cloud data storage solutions and company-wide communication tools.
To maximise the efficiency of your supply chain, it’s important to leverage a wide range of these technologies and use them to ultimately transform your supply chain into a smart and streamlined digital operation.
Optimise Data Analysis Models to Provide High-Value Data Sets
Using the right models is essential to supply chain data analytics. In order to optimise your data analysis models, you first need to define your goals. Do you need to run a cost-benefit analysis to determine whether or not to buy a new machine for your factory, or do you need to use regression analysis to find out whether a new production method is positively impacting efficiency?
To optimise your data analysis models, you also need to figure out the sort of information you have at your disposal. Different types of data will be more suited to creating predictive, descriptive, and prescriptive models. By tailoring your data analysis models to your specific needs, you can optimise your processes and enhance the efficiency of your supply chain.
Test Your Hypotheses with Digital Twinning
To use data effectively, you often need to test multiple scenarios and hypotheses and move forward with the one that produces the best results. This can be a challenge, as running real-life A/B tests is extremely expensive and time-consuming.
This is where digital twinning comes into play. Digital twinning is the process of creating a virtual representation of a particular object or system. In the context of supply chain management, this might mean digitally simulating two different shipping routes to determine which one is faster.
With tests like digital twinning, supply chain managers can investigate multiple hypotheses at once, allowing them to find the best solutions for their operations promptly and in a cost-effective manner.
Invest In Data Analytics Training for Personnel
To effectively utilise data, you want to empower your team to perform data analyses and come up with insights to improve your supply chain. The best way to do this is to invest in data analytics training.
An effective training programme in Industry 4.0 should include handling various data collection and analytics tools, performing relevant data analytics procedures, and generating actionable insights based on the harvested data.
This training should be conducted across your entire organisation to ensure that all personnel who are using data solutions on a daily basis are empowered to think critically about the figures in front of them and how they may relate to the sustainability and productivity of your supply chain.
Supply chain managers can also supplement their existing workforce by hiring data analysis experts. Look for professionals with experience in fields like machine learning, big data analytics, and business intelligence. By bolstering your supply chain team with training and the right talent, you can equip your company to make more informed supply chain decisions.
Build a Culture that Promotes Data-Driven Decision-Making
Finally, as data insights are available to all personnel with access to centralised data platforms, you want to make sure that your staff feel confident in approaching higher-ups about any insights they may have.
So, in other words, alongside supply chain managers being able to observe correlations in data sets, their team members should also not only be free, but feel free to make their own interpretations.
Managers should seek to build a culture where innovations can be generated bi-directionally, and department heads can collaborate with their teams to turn raw data into actionable insights. Creating an environment where each team member is empowered to come up with their own insights is crucial for building a culture that promotes data-driven decision-making.
And truthfully, establishing this culture is key to ensuring your supply chain stays robust, adaptable, and scalable during Industry 4.0.
In the age of Industry 4.0, data interpretation is essential to doing business, and nowhere is this more applicable than in the world of supply chains. As a supply chain manager, you need to equip your company with the resources and expertise needed to make decisions based on data.
In this article, we’ve gone through some data interpretation basics for supply chain managers, including choosing the right data solutions, investing in data analytics training, and optimising your data analysis models. Apply these tips to your own practices today to build a more efficient supply chain and make smarter decisions.
Article and permission to publish here provided by Marla Doman. Originally written for Supply Chain Game Changer and published on August 14, 2024.
Cover image provided by Marla Doman.