As a consumer society we have experienced the positive and not so positive aspects of algorithms including nuisance advertising and social media tracking that erodes trust and breaches privacy. To advance procurement 4.0, the ethical use of algorithms in artificial intelligence is essential.
The concept of algorithms were once very much associated mathematicians. Today, algorithms have become pervasive in modern society – the way we shop and behave online can be tracked and companies can predict and influence everyday purchases and decision making.
There are a growing number of use cases of using machine learning and artificial intelligence in the procurement supply chain – from inventory ordering to category spend analysis and supplier performance and compliance. Data is crucial for procurement teams because, without the data, they cannot effectively track spend or manage supplier and vendor relationships. New technologies are now available that can learn more about the activities of stakeholders, giving procurement and suppliers with the means to anticipate and adapt to market trends, and to guide decisions to help attain sustainable competitive advantage.
Using artificial intelligence for good
When artificial intelligence is used for good, data can now help to trace fraudulent activity online, and track the journey of a parcel from A to B. The impact of data-driven innovation to shape procurement 4.0 has been researched and well documented by the Open Contracting Partnership, Transparency International, the World Health Organisation and the OECD – all of whom advocate for the release of open data to make government more transparent.
Procurement 4.0 has been discussed by industry for years now, and it will be digital procurement pioneers that hit the go button to get the ecosystem going. How can new technologies increase agility and accelerate digitization within the procurement function? And what will the new procurement 4.0 look like?
This is one of the hottest topics being discussed in procurement circles right now after the industry lagged too long in modernising their systems, and subsequently suffering the fallout of supply chain disruption and COVID-related contract mismanagement and corruption.
Trends and red flag analysis in procurement
Hindsight is a wonderful thing, but so is embracing technology and all that it has to offer with advancements in cloud security, process optimisation and deep learning.
The criminal world and law enforcement has been using data-driven approaches in their sting operations for years, yet procurement is one of the most vulnerable industries to bid rigging and collusion. Some of the applications that data can be used for in procurement include:
- Identifying number of wins by companies Are the same companies winning all of the time? Which regions or sectors are we seeing these patterns, and do they have diversity and inclusion policies?
- Is there bid rotation occurring? Can we see a time series view of the winners?
- Few or low participants in the bidding processs. Can we see the distribution of participants? Was the timeframe unreasonable to push out open competition?
As the industry transforms, we will likely see simplified tendering, better supplier relationships, and time-saving solutions to optimise procurement programs.
Building the ecosystem foundation with connected data
We will finally see uptake and a strong appetite for efficiencies that connect buyers and sellers across the supply chain, and the adoption of best-practice models for sourcing and engagement. Open Contracting has been long ignored by governments and yet data is the very thing that can improve business decisions and deliver competitive differentiation.
In using data we can create a new procurement system to bring:
- Transparency to public contracting
- Openness that will lead to fair competition
- Competition that will lead to potential cost savings and more innovative solutions
- And more innovation will lead to better and faster impact and outcomes for communities.
So back to algorithms. When algorithms are used for good, they can be used to measure the success of past activity, and predict the likelihood of future outcomes. By predicting success or failure, it can improve decision-making, enabling companies to plan their business strategies, optimise their resources more effectively, and develop solutions based on learnings to improve quality, safety, and to reduce service gaps.
By defragmenting public procurement data and placing it into common formats we can enable supply to meet demand; and demand can be based on what’s needed, not what is budgeted for. Open data that is linked will help procurement to detect duplication vertically and horizontally; there can be better resource allocation, and suppliers can detect red flags or opportunities for collaboration.
With better data, we can have a better procurement system. And with the 30% (or a cool $5 TRILLION) savings that can be made, why don’t we put that towards the world’s to do list and advance the 17 Sustainability Development Goals?