2020 has been the year of the supply chain, as all aspects of the function have been exposed and challenged and few businesses have emerged on affected. As we move through the myriad of 2020 supply disruptions, procurement has a supplier data problem, the side effects of which are costly and far-reaching.
As supply risk continues and effective suppliers’ relationship management and assurance becomes even more challenging, as can be seen by recent 2021 research findings:
· 41% of firms reported needing to expedite shipping to keep critical supply lines flowing.
· 36% are losing revenue due to supply shortages.
· 11% realized brand damages directly resulting from supplier issues.
· Only 26% can predict risks at their supply base.
So, what do we have to look forward and how can supply chain and procurement executives prepare for success?
The solution might be found in investing time and resources to make sure that supply chain and procurement teams are empowered by a cleansed, harmonized and enriched big data foundation.
Big data is one of the most commonly discussed topics today, and many companies are attentively monitoring the evolution of this trend. Several studies have shown that managers are able to make the best decisions when armed with data and tools to gather insight. Researchers report that a 15-20% increase in ROI can be achieved by introducing big data to enterprises’ business analytics.
Traditionally, purchasing and supply management (PSM) has strongly relied on data management, as procurement managers need to dispose of, clean and update data of different natures to compare suppliers’ performance and 20-50% of working time in procurement is related to searching for information accordingly.
Big data analytics have obvious applications and represents a new era in the PSM field as they link and aggregate all relevant information, thereby facilitating and speeding up strategic and operational procurement activities significantly and are a critical source of meaningful information that can help supply chain stakeholders to gain improved insights and gain a competitive advantage and maximizing speed and visibility, improving supply chain relationships and enhancing supply chain agility.
However, despite the relevance of data management, the PSM field has been relatively slow to identify the potential role of new technologies and businesses have been less quick to implement big data analytics in PSM than in other areas, such as marketing or manufacturing.
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Few companies, however, have been able to apply the "big analytics" techniques that could transform the way they define and manage their supply chains. The full impact of big data is restrained by two major challenges. First, there is a lack of capabilities. Supply chain managers—even those with a high degree of technical skill—have little or no experience with the data analysis techniques used by data scientists. As a result, they often lack the vision to see what might be possible with big data analytics. Second (and perhaps more significantly), most companies lack a structured process to explore, evaluate and capture big data opportunities in their supply chains.
But, once applying these technologies, high performers are 4-5 times more likely to have fully deployed advanced analytics/visualization, have fully deployed predictive analytics capabilities (12% vs. 0% for others) and are 18 times more likely to have fully deployed artificial intelligence (AI)/cognitive capabilities. Strong digital capabilities can help procurement organizations improve data visibility and the ability to collaborate/synchronize with suppliers, enabling greater agility both within these organizations and across the extended supply networks. CPOs can work toward building use cases for the Internet of Things, 5G, blockchain, control towers and collaborative workflows enabled by AI/machine learning to up their digital game in these areas. How big data can be used in the daily procurement teams work:
1. Supplier lead time analysis. In most enterprise purchasing systems, supplier's lead times are entered upon supplier agreement signature and are kept as static data on a part level, which is not updated frequently or at all.
Since supplier lead time plays a critical role in the timing and sizing of purchase order decisions, many purchasing professionals have recognized this importance and are looking to accurately predict lead times and to develop strategies for coping with problems created by lead time variations.
To build an accurate module, it helps to build an accurate prediction system that highlights the following to the supply chain organization:
1. Fine-tune supplier's metrics to better predict if parts will be shipped on time or not.
2. Specify lead time data on a part level that includes work in process (WIP) and inventory quantity recommendation for both buyers and suppliers.
3. Return updated lead time to enterprise system to better manage the purchase order life cycle.
2. Predict suppliers' late deliveries. The use of advanced prediction algorithms to foresee supplier's on-time parts delivery problems and not after they shut lines down has a great positive impact on out-the-door performance.
These prediction systems help set expectations and give supply chain managers the tools to make the right decisions for on-time deliveries, eliminate hidden factory costs of late parts, redeploy labor from expediting to value-added activities and focus on growth.
5 ways big data in procurement can improve the bottom line
Big data analytics is playing an instrumental role in improving suppliers’ management. It resolves several pain points at strategic, operational and tactical levels. Big data is making an impact on all supply chain activities. It ranges from improving delivery times to identifying ways to reduce the communication gap between manufacturers and suppliers.
A recent survey revealed a staggering number of critical issues that organizations are dealing with a result of poor supplier data. For example, 93% of procurement and supply chain leaders had experienced adverse effects of misinformation about their suppliers, and nearly half (47%) experience such negative effects on a regular basis. Consequences include wasted time (63%), delays in projects (47%), and worse, terminated supplier relationships.
Here are five ways big data can really improve the bottom line:
1. Fact-based decision-making. With big data, fact-based decision-making has a chance to become ubiquitous reality. We all know that critical business issues are often discussed anecdotally. With a big data approach, procurement executives could consistently ask for data-oriented evidence for all major decisions and reported business issues, such as quality problems.
2. Supplier knowledge. In the past, organizations faced laborious processes that took several weeks to gather internal and structural data from the operations and transactions of the company and its partners. But, today, at a significant speed, in real-time, in many cases, all of the diverse structural, non-structural, internal and external data generated from automated processes are made available to these organizations. Basing supplier selection, monitoring and control on more data and information will improve procurement performance at the supplier level. As mentioned above, the main benefit is cost reduction.
3. Suppliers’ performance. The adoption of Big Data in the procurement process improves suppliers’ performance mainly in terms of cost, but also potentially in terms of time, quality, innovation, flexibility and sustainability.
4. Increase savings by learning more about suppliers. Ideally, you would constantly monitor each supplier to verify that their operational performance is up to par, their bottom line is stable and healthy, their product is of consistent quality and their sourcing meets compliance standards. Ongoing supplier analysis can capture every detail, flag every anomaly and verify every transaction to show you whether suppliers are performing as expected. This will help determine how much you stand to save or risk by switching to another supplier or ordering from a different region of the world.
5. Predictive analysis. One of the biggest advantages of embracing Big Data analytics within your organization is that it creates the ability to become predictive rather than reactive so activities from strategizing for supply, the selection of suppliers, the management of supplier relationships and ordering and expedition has been claimed to have huge potential in benefiting from big data. A recent Hackett report found that world-class organizations that take advantage of procurement technology are more efficient, with 22% lower labor costs and 29% fewer full-time equivalents than typical organizations.
