We can agree that bad input data usually results in poor outcomes. Decision-making in procurement is rarely ever perfect, but having a solid data foundation as input for modeling your award scenarios can drastically improve the transparency and impact of your supplier award decisions – saving you millions of dollars on the way.
Overcoming the data challenge
Procurement organizations often quote the existence of data silos or low data quality as a reason for not starting their digital transformation journey. Indeed, inaccessible, duplicated, inaccurate, or under-utilized information is simply a waste.
But it’s not a valid reason to procrastinate the inevitable task. Procurement needs to fix the necessary historic data — but there needs to be a larger focus on looking forward and focusing on getting started. Supplier information is a particularly critical piece that most organizations struggle with due to multiple disconnected ERP systems, ownership quarrels with finance or IT over the golden record, or simply missing or isolated information.
The rise of specialized data providers collecting ESG, financial risk, or supplier diversity information makes it easier for organizations to tap into detailed and current information on millions of suppliers. Data aggregators, especially, provide data enrichment capabilities that can help organizations overcome the data challenge quickly.
The hidden cost of poor data
While the data challenge is not unique to procurement, the consequences can be especially costly when sourcing goods and services. Next to supplier information on qualifications or the above-mentioned ESG performance, risk, or diversity status, procurement needs to manage large amounts of (historic) pricing data and cost breakdowns.
Many of these data points become unmanageable quickly, especially if you lack specialized decision-making support tools and attempt to manage and analyze your sourcing events with Outlook & Excel. Yet, many sourcing tools lack the flexibility to manage and analyze complex pricing structures and are therefore only used for managing supplier communications.
This inability to leverage existing data points for balancing the cost of goods and services with additional internal business considerations often results in less-than-optimal supplier selection decisions. This can cost organizations millions of dollars simply because they don’t have a clear picture of their options + trade-offs available to them or the costs of business preferences or stipulations.
Making data actionable with holistic sourcing decisions
By enriching advanced sourcing capabilities with a strong data foundation, the right technology can truly drive efficiency and impact for procurement teams and organizations overall. Making supplier data on ESG metrics, risk, diversity status, or performance scores available next to commercial information during the supplier selection process allows Procurement to make holistic sourcing decisions.
By comparing available award scenarios, procurement teams can clearly articulate the cost of individual business requirements and bring transparency to the decision process. This way, they can actively drive business strategies and unlock fresh savings opportunities.
Tealbook’s comprehensive supplier information alongside Archlet’s advanced sourcing analysis & scenario optimization capabilities can deliver significant savings to organizations while propelling their supplier diversity and ESG initiatives forward.
Unleash procurement possibilities
By leveraging the power of machine learning and AI, TealBook is transforming the supplier data market by optimizing suppliers, unifying data across systems, and empowering teams. Archlet empowers pioneering procurement teams across industries, company sizes, and digital maturity to make data-driven and holistic sourcing decisions.
Through our partnership, our two organizations will empower our customers to make their sustainability, diversity, and risk strategies actionable. Contact the TealBook team to learn more or schedule an industry-specific demo today.