Smartly Tackle Data Barriers to Use Advanced Analytics
Using AI/ML to Continually Reduce Operating Costs
"less than half (44%) of data and analytics leaders reported that their team is effective in providing value to their organization"
“Harnessing the true power of data driven insights is the holy grail of future business. A wealth of this data comes from the supply chain. But, while the information is there, companies are not yet capitalizing on its real value as a source of insight capable of shaping the future of the enterprise.”
Companies are struggling to use data and analytics to continually find ways to reduce operating costs and protect margins. According to Gartner, “less than half of data and analytics (D&A) leaders (44%) reported that their team is effective in providing value to their organization”.
So why are so many companies still struggling with the adoption of Machine Learning / Artificial Intelligence (ML/AI) technologies to handle today’s inflationary pressures? The reasons can vary but some of the most common complaints are:
Our data still sits in silos and it is difficult to integrate.
We have pulled all our data together but people still don’t trust it.
As a large company, we have a long way to go to be able to support advanced analytics with the current state of our data.
Case In Point: A meeting was held with the CFO, COO and SVP of Supply Chain for a well-known apparel company. They knew that they needed to build analytical capabilities, but were skeptical because of their perception of the current state of their data. Fortunately, the SVP of Supply Chain had previous experience in working with a solutions provider in tackling this issue. He convinced the others to take a first step that would demonstrate that their data could be turned from a liability to an asset to produce meaningful insights on opportunities to reduce costs and increase profit margins.
Action: A Project Team was assembled and all sources for their supply chain and sales transactional data were identified. Data Subject Matter Experts were involved to address any data issues. Consensus was reached by the Team on how the data should be intentionally transformed to build a foundation for SKU and Customer specific cost and profit performance information. ML/AI technology was then used to further validate data quality problems and to create specific and actionable financial performance insights that could be refreshed periodically. The Project team found a potential inventory working capital reduction in excess of $10 million dollars.
Data integrity issues can be proactively addressed to ensure that operating data becomes a valuable asset. An asset that allows for visibility into actionable insights that drive trusted, fact-based decisions. The companies that take this seriously will consistently move ahead of their competition and drive additional cost reductions and profitable performance.