Polarity management is the process of governing the relationship between two paradoxical challenges. In procurement, when cost is at one end of the polarity, any number of factors could be at the other: quality, quantity, consistency and so on. Perhaps the most difficult polarity to manage in procurement, however, is that of cost and relationships.
Most procurement organizations are focused on securing the best deal possible for their companies. In a world where metrics are required to measure outcome, the metrics for procurement organizations often fall to cost management. Focusing on cost (a tangible) can muddy the focus on value (largely an intangible). Focusing on cost distracts us from the relationship component, ultimately straining those relationships and forcing providers to sacrifice the very differentiators that offer value.
Additionally, many procurement organizations try to remove subjectivity from the purchasing decision in pursuit of a fair and impartial vendor selection. Objectivity is a good thing, but seeking it to the detriment of relationships is not. Instead, procurement professionals should pursue a mechanism that lets them measure the relationship health and incorporate that into their incentives, rather than just going for the best deal.
Let’s say a procurement manager works for a large company with a centralized procurement process that deals with operations at scale. If that company’s suppliers know the procuring company will always award bids to whichever supplier submits the least expensive request for proposal, those suppliers are incentivized to submit projects with the cheapest possible cost projections. This is true even if the propensity for low initial bids increases the likelihood of expensive change-orders or missed project deadlines. The procurement manager will achieve the goal of getting the lowest possible cost, but the method won’t really save the company any money.
This is where polarity management comes into play. Procurement managers have to do more than focus on achieving the lowest costs; they must reckon with how cost-controlling processes affect their relationships.
Precision through technology
Often, during the procurement process, interaction between the two businesses is kept to a minimum. This is a good way to avoid favoritism, but it further diminishes the value of relationships. An unbalanced cost-relationship polarity doesn’t simply incentivize negative behaviors, either; it can also leave even more value on the table. In fact, PwC notes in a 2019 white paper that supplier management can be procurement managers’ best bet in preventing “contract value leakage,” or lost value that comes from performance levels below what an original contract or bid projected. The paper also shows that procurement managers face an average of 25% value leakage in contracts.
Even if company executives take a dollars-and-cents approach to procurement, there’s a financial incentive to balancing pure cost control with better relationship management and analysis. The problem is that polarity management is tricky to put into practice. Procurement is difficult enough when the only objective is cost. That’s why procurement managers should supplement their efforts with new technology — not only for cost control and spending analysis, but also for more precise and effective management of procurement relationships.
The procurement industry has been slow to adopt new technologies. Research from Bain & Company found that less than 10% of companies in the procurement industry adopt established procurement solutions related to big data, the internet of things or blockchain technology. Even companies that think they’re adopting the latest data analysis technologies are rarely looking at the data through the lens of relationship health and mutual benefit. They might assess whether they’re getting the best price through market analysis or if the spend is in line with expectations. But reaching the next level requires leaping into new technologies, such as artificial intelligence (AI) and machine learning (ML).
Fortunately, AI and ML enable quick recognition of behavioral patterns that can potentially erode value or provide opportunities to cocreate value. These technologies also allow procurement managers to experiment with different approaches and compare results all the way through a project life cycle, such as by using A/B testing of procurement strategies to optimize relationships. AI and ML also help users manage intricate transactions that sometimes vary from instance to instance, identifying trends in behavior or patterns that emerge across systems.
In addition, by deploying the tools with the right data feeds (not just between company and vendor, but also within the market itself), these technologies support control over the polarity of objectivity and subjectivity. In essence, they become a system of checks and balances, helping to remove inadvertent bias.
A more holistic opportunity
Using a variety of models can provide new dimensions to the decision-making process. Each buying decision is a little different, so the models need the flexibility to weigh the importance of certain attributes. If there is a time-critical project, then a vendor known for on-time delivery might rank higher than a vendor that offers the lowest price. Where AI and ML come into play is by helping buyers see the relationships between these attributes and determine if decision criteria are correctly considered.
For example, a machine learning model could tell you that a particular vendor known for delivering on time more often than the competition also has an 80% chance of issuing change orders that increase the project’s overall cost. Yes, they get it done on time, but the final cost is unstable. On the other hand, that same model might tell you that the second-place vendor has a 30% chance of encountering time delays but historically takes on the costs of the overrun rather than burden the purchases with increasing cost. In this case, the project’s management team may be able to accept a 30% risk of time delays, given that the likelihood of staying on budget will decrease the chance that leadership will cancel the project due to cost pressures.
Interestingly, procurement is also often better equipped than other verticals in a company to deploy AI and ML. After all, the deep transaction history created by the supplier-relationship life cycle provides a rich source of data from which an algorithm can draw conclusions and insights. Once deployed, these solutions create a technological feedback loop than can make polarity management more seamless. An algorithm may reveal a potential solution, which then yields more data, which can suggest an even better solution.
In an ideal world, procurement managers would be able to have a full digital record of a product’s life cycle, from initial order all the way through fulfillment. Of course, the data surrounding those orders doesn’t always make its way cleanly through the system, and this creates uncertainty about whether orders are fulfilled correctly. On the other hand, a well-trained model can take all the disparate data and reconcile it, creating a more complete picture of order fulfillment from end to end. The result is a more efficient and cost-effective procurement system — exactly what everyone wants.
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