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Most geniuses—especially those who lead others—prosper not by deconstructing intricate complexities but by exploiting unrecognized simplicities.- Andy Benoit.

And it holds good for our Spend Management as well! When it comes to Spend Analysis, it has been too easy to recognize the most visible parts of the business.
After all, it makes sense to keep spends that directly impact your business on a tight leash, right? Most firms agree and manage their direct expenditure in a tightly monitored, centralized way, with regular checks into probable saving opportunities.

But this tunnel-focus on Direct Spend can cause crucial Indirect Spend data to fly under the radar. And within this ‘out-of-focus’ indirect spend area, some spends go rogue. These are known as Maverick spends. These can include off-contract spends, fragmented spends, low-value transactions, non-PO spends, one-time purchases, unclassified/misclassified purchases, and more. They’re almost always a part of indirect spends, and are almost always invisible to firms

Because of this invisibility, it is pretty common for procurement professionals to look back at the end of a financial year and realize that their indirect spends have gotten completely out of hand.

If you find yourself in the same boat, let us tell you that you’re not alone; Managing invisible indirect spends can be quite a tough nut to crack.

And keeping the difficulty of the task in mind, it is also worth pointing out that global indirect expenditure can be as high as 50% of a company’s total cash out–flow to suppliers. And with most firms dropping the ball in this area, it can be a real opportunity to rise above the fold.

Here’s what most firms struggle with when it comes to invisible indirect spends, and how you can ensure you don’t:

Improper Categorization

Owing to the diverse nature of requirements, indirect spends can span across innumerable categories, and without correct classification, it is impossible to make sense of them, let alone glean insights from them. Manual or weak-tech-based solutions are error-prone, time consuming and offer too little to no respite.

The best bet to tackle this mammoth is to deploy machine learning algorithms. These automate the categorization of spend data by directly picking up information from fields including material, supplier or line-item description. Adopting an ML–based solution can sky-rocket classification accuracy and give high visibility into all sources of expenditure.

Decentralization & Lack of Visibility

Indirect Spends are highly scattered, and undertaken by multiple internal stakeholders with independent spend protocols and budgets. It is nothing short of a nightmare to track them

You can combat this by classifying spend categories into specific workflows, partnering with Account Payables (AP) and Finance teams to devise targeted response and escalation protocols. Also, maintaining a central repository of organization-wide spend data is critical and makes auditing and reporting simpler.

Bloated and Weak Supplier Pipeline

Multiple touch-points in indirect procurement can often mean an over-abundance of suppliers. And because many purchases are ad-hoc undertakings, the firm’s relationships with suppliers tend to be more tactical than strategic.

Consolidating suppliers and pooling volumes can help unlock economies of scale. It can be achieved by improving supplier performance and risk evaluation. And by filtering out undesirables, better prices can be negotiated among a smaller pool of quality partners with whom better relationships can be maintained.

The above challenges and more plague the indirect procurement functions of firms on the regular, and ignoring them can prove costly.

And what’s more? A recent survey by Gartner found that managing indirect spends can deliver greater ROI than managing direct, or hybrid spends.

The complexity and size of this invisible spend data necessitates smart, automated solutions; One’s that can not only offer multi-level deep visibility, but also actionable insights and seamless integration of relevant data across all stages of procurement.

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