The big data growth we have been witnessing is set to skyrocket with developments in AI, IOT and machine learning and the increasing uptake of the applications we employ every day to improve our business processes (and our lives).
In procurement and supply we all understand the important role data plays in helping us make good decisions about spend management, whether that be what, when and where to buy, giving us robust market intelligence and spend and risk reports, or aiding us in supplier relationship decisions. We also know that organisations with intelligent data strategies are more likely to take a larger share of available dollars from the market and maintain a higher customer retention rate.
At Spend Matters we have gained some expert insight from a leading global provider of consulting, software and managed services solutions to the procurement and supply chain industry, and wrapped it up into three interlocking papers which are freely available on our site. The first looks at master data management, which when done properly provides the means to identify and mitigate risk (the second), and facilitates the ability to forecast supply and demand with accuracy (the third).
Master Data Management – why you’re doing it wrong and how to fix it
This article looks at the challenges associated with how master data can be managed to allow it to feed the data analysis machine that will produce the accurate intelligence you need. The fact is you can make use of the most recent AI or algorithm-based developments, but you will not reap the true benefits they can offer unless the core data on which they depend is complete, correct and rationalised. If the judgements you make as a business are based on inaccurate, out-of-date or false information, then you are creating more risk in your decisions by not having the full picture of the truth. So the article then explores how you get to trust your core data so that you can take it into a meaningful business context.
Part of that discussion centres on looking beyond the ERP system towards AI-powered solutions that are more fully equipped and better designed to harness and produce trustworthy data in one accessible source. The article goes on to outline what those systems’ capabilities need to be. Read that here.
Third-party risk management – owning and mitigating threats
The second article looks at the importance of identifying third-party risk, which is becoming a board-level issue. But the precise consequences of disruption are difficult to measure and quantify, as the scale on which it is now taking place is generally much larger than in the past, with new risks like the threat of high-profile business failure, accountability for illegal third-party action or regulatory enforcement with punitive fines. So we are encouraged to look beyond the supplier and into the whole ecosystem that surrounds a business which is susceptible to a dangerous ripple effect from any failure, and think about risk strategically.
From planning to strategic sourcing all the way through to vendor selection, supplier selection, due diligence, contract negotiation, monitoring and termination, the whole circle is a very strategic (and non-transactional) operation, and risk can come as much from upstream as from downstream. So the article gives several important areas to consider strategically: data modelling, looking beyond spend and contracts, and recommends a holistic approach to risk, governance and mitigation, encouraging us to consider who it is that owns the risk, and to not forget about inherent risk and the fact that outsourcing risk management does not mean the risk has gone away. All of this points to why we should be considering risk as a priority. It outlines what a CPO can do to kick-start the process of taking risk more seriously. Read that here.
Knowing your supply and demand in advance
This article focuses on using demand sensing to forecast demand for goods and services as a foundational stone for making business-critical assumptions around budgets, profit margins, cash flow, capital expenditure, risk assessment, capacity planning, resource allocation and so on. Again this is a science that is becoming more precise. Determining where to position your stock for distribution and sales, what to manufacture, what you buy, at what time, and at what price and quality is changing. And that is because today’s market and supply chain conditions are not ‘normal’ or steady, but fluctuate at the mercy of economic, climatic, geographical and political influences.
With advances in machine learning and machine algorithms in the demand-prediction space we are producing data that is more far-reaching, reliable and real-time than ever before, helping us reach a common goal of eliminating waste, driving sustainability and responding to public sentiment. It looks at what it is that is differentiating providers in this market and their capabilities when it comes to the many advantages of using the cloud and harnessing structured and unstructured data, which cycles us nicely back to the need for good master data management. Read that here.