The ROI of implementing Big Data analytics in daily Supply Management operations
2020 has been in many respects 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, we understand that 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% losing revenue due to supply shortages
· 11% realizing brand damages directly resulting from supplier issues
· Only 26% are able to predict risks at their supply base)
(Per the Deloitte Global 2021 Chief Procurement Officer Survey)
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. Researches 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 in order to compare suppliers’ performance and 20% to 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, in spite of 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.
Per a CPO: “Data have always been out there. Companies started to accumulate the data long ago, so the term ‘big data’ does not have a completely new meaning. However, in the last few years, the mentality of the buyer has changed; now he/she is aware of the fact that the data at his/her disposal can be employed in order to increase the efficiency of the procurement activity. In the past, due to a lack of technology, it was unthinkable to process the data. Today, it becomes possible, and several opportunities arise in the procurement process as well as in other departments.” (Increasing the effectiveness of procurement decisions: The value of big data in the procurement process Antonella M. Morettoa,?, Stefano Ronchia and Andrea S. Patruccob.)
"Few companies, however, have been able to apply... the "big analytics" techniques that could transform the way they define and manage their supply chains. In our view, 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. " (Big data and the supply chain: The big-supply-chain analytics landscape (Part 1) - Mckinsey February 16,2021").
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 18x more likely to have fully deployed 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.”
(Per the Deloitte Global 2021 Chief Procurement Officer Survey)
2 examples of 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.
As part of our ongoing work to develop prediction algorithms based on advanced analytical tools such as machine learning (ML) to help the supply chain organization better manage their suppliers, we've developed a module that predicts the lead time variation % of a supplier manufactured part compared to the current static lead time maintained in the enterprise purchasing system. This module analysis big data captured from various systems:
1. Purchasing information from the enterprise system
2. Goods received information from the enterprise system
3. Daily supplier data captured in our systems: late lines, late qty
4. Supplier portal inputs such as date difference in supplier po confirmations
Blending this wide range of information and sources helps build an accurate module and after training it, It helps build an accurate prediction system, which 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 which includes WIP (Work in process) and inventory qty 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 your supplier's on-time parts delivery problems and not after they shut your lines down has a great positive impact on OTD 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.
We've based our PPA (parts prediction algorithm) on the analysis of 4 suppliers data inputs from various systems:
? Historical inbound shipments Data from the ERP system
? Manufacturing data via the suppliers portal
? PO confirmations in the supplier portal
? Supplier portal updating trends
After studying and analyzing the big data gathered from the above systems, the algorithm provides a scoring system that is adjusted on a supplier level per the engagement level allocated to each supplier.
This prediction system gives an indicator for manufactured parts at suppliers and gives supply chain managers the tools to communicate and make the right decisions to achieve on-time deliveries, eliminate hidden factory costs of late parts, redeploy labor from expediting to value-added activities, and focus on growth and not resolving delivery issues.
This is of huge potential benefit to manufacturing companies, especially those that rely on just-in-time component and materials delivery, and will improve OTD rates and assembly lines shutdowns with the costs associated with such problems.
5 Ways Big Data in Procurement Can Improve Your Bottom Line
Big data analytics is playing an instrumental role in improving supplier 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 as a result of poor supplier data. Probably the most shocking result was that 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 your 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 decision 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, nonstructural, 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 your suppliers are performing as expected. This will help you 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 (BD) . 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 (FTEs) than typical organizations.