“Industry 4.0” is the term being coined for the occasion. This means making computers smarter and enabling machines to make autonomous decisions. This increasing need to rely on artificial intelligence is driven by big data and automation of what to do with it when human intelligence falls short.
Supply chain is certainly not the only industry attempting to tackle big data, but what exactly does this mean for its stakeholders? It means the winners and losers of procurement will be determined by how quickly companies can adjust to the increasing volume and complexity of data.
As an analyst, I receive clients’ raw spend data to evaluate with goals of identifying savings opportunities and determining where stakeholders can improve most on their ROI. The initial process requires conducting a spend analysis, which relies heavily on human input for meticulous data cleansing and standardization. Using a best-fitting taxonomy and following the 80/20 rule, I categorize each supplier into appropriate areas of spend. This process can be tedious, time-consuming, and most consequently, prone to human error. Additionally, changes and additions to clients’ datasets are tracked to provide up-to-date and accurate analyses – meaning continuous manual work. However, Industry 4.0 is here to increase the efficiency of this process.
On a small scale, Industry 4.0 would introduce advanced analytic techniques such as data automation via machine learning and data mining in order to minimize human error. Smart automation would be able to facilitate spend analyses, enabling quick turnarounds for supply chain specialists to clients. Furthermore, this technology increases accuracy without compromising the quality of deliverables. Analysts will be able to face the challenge of dealing with big data and ensure best practice to achieve client goals.