The following guest blog is contributed by Mike Russo, Director, Customer Engagement at Compunnel Inc.
Much before Harvard Business Review had named ‘Data Scientist’ as: “one of the best jobs of 21st century,” procurement professionals were already using data to make decisions on spend, supplier management, transactions, and much more. But that was data, just data, mostly un-organized, un-collated, and hardly insightful.
Good news, it was all about to change!
Transitioning from Simple Data to Big Data
As procurement shifted focus from being a cost-reductive technique to more of a value-addition approach, automation was the first thing on everyone’s mind. From invoice processing, supplier performance analysis, spend analysis, to contract lifecycle management, every process was machined to save time and improve productivity.
In fact, according to a study done by a Deloitte Global CPO Survey in 2016, 40 percent of procurement professionals had a clear digital strategy in mind that included solutions like analytics and crowdsourcing.
In no time procurement organizations were generating tons of data, huge data sets that were beyond the reach of conventional processing techniques. It wasn’t long until they realized the wealth of data they were sitting on. This data could not only be used to consolidate and connect supplier and spend metrics across an enterprise, it could also help organizations unlock insights, reduce costs, and improve quality. Most importantly it could be used to find the most ideal supplier!
The big question was – how could we make sense of the humungous data at hand?
Was Data Science the Answer?
It was evident that the problem at-hand required a multi-faceted approach, one that combined statistics, mathematics, programming, and analysis to comprehend the data. Yes, sophisticated data science techniques were the only resource that could help extract knowledge and insights from the humungous data at hand. And procurement had slowly but significantly taken notice!
Top Data Science Techniques and Algorithms
Data Science Could Form the Base for a New Supplier Information Management System
Challenges aside, data science techniques could really help improve business intelligence, analyse sourcing patterns, measure supplier risk and performance, and most importantly provide higher visibility and compliance, everything that a traditional organization was vying for. If analysed properly, all this information could be used to create a new Supplier Information Management system. A system that could change the way businesses collect what they know about the suppliers, evaluate their performance, and do risk-assessment. The potential for the CPOs was truly immense.
And It Wasn’t Long Until CPOs Got a Whiff of the Words Digital and Data
Could technology provide new tracks of opportunity, could data science, cognitive analytics and digital reporting help make informed decisions. Well, if a survey by Deloitte is anything to go by, then CPOs are showing great interest in digitizing their procurement lifecycle, reducing focus on transactional and operational functions, and re-focusing on the most important part – Insights. Here are some of the key findings from the survey.
- 70 percent of CPOs focused on engagement and experience through self-service portals
- 42 percent felt mobile technologies could present opportunities for improved value creation
- 45 percent felt Cloud computing could impart better efficiency
- 16 percent felt social media could form a vital part of the digital strategy
Source – Deloitte Global CPO Survey 2016
Areas Most Likely to Receive Investments
According to the same survey, supplier relationship and supplier risk management remained among the top 5 concerns for CPOs, and were amongst the areas most likely to receive investments.
- 38 percent felt investment in Spend Analysis was the right step forward
- 29 percent felt that investment in Supplier relationship management could impart efficiency to the whole process
- 13 percent felt that Procurement Performance Management could be improved with the right technology
- 17 percent were ready to invest in Supplier Risk Management
- 11 percent felt that Supplier Information Management was still not optimized, and that technology could play a big role in improving it
Source – Deloitte Global CPO Survey 2016
The Big Question – How Can Data Science Help CPOs Connect with the Best Suppliers?
So, we knew digital transformation was underway, that data was going to form the backbone of every process, but how was all this going to work. How could data science be used to do wonders in the field of Supplier Performance Management, Supplier Risk Management, Sourcing Excellence, and Self-Service Analytics. Let’s have a look!
- Spend Analysis: By extracting, aggregating, and cleansing real-time spend data, organizations can better understand spend patterns across different functions. Analysing all this data can help them understand and segregate spend according to geography, supplier, and category. Using proper analytics channels organizations can also help forecast spend depending on the current market scenario.
- Supplier Performance Management: By correlating supplier performance against a set of efficiency metrics, studying score card performances, and understanding their sourcing patterns, procurement teams can take better decisions when it comes to choosing the right supplier or extending their existing contracts.
- Supplier Risk Management: By studying the data related to previous business risks including compliance, geography, pricing etc, procurement teams can devise better counteractive measures, and also a more effective supplier screening mechanism.
- Connecting with the Best Suppliers: As you learn more about your requirements, behaviour, spend, and favoured sourcing channels, you’ll be able to connect with better suppliers, assess real value faster, and also negotiate well.
- Overall Predictive Analytics: By analysing all this data, procurement can identify trends that can unlock valuable insights. This will help understand pricing, supplier performance, and spend data better. In fact, many top companies are already using data science to optimum effect. A good example would be Compunnel (a premier staffing firm in the US) which is using predictive analytics to determine candidate suitability for a particular role.
Data Science is coming off age, and with the power of analytics, procurement professionals can really unlock valuable insights from raw data, and realize great efficiency across the organization. If used properly, finding the right supplier would never be a concern!
About the Author:
With more than 20 years of experience in Staffing and Recruitment, Mike has been a part of both high volume up cycles, and also some of the recessionary woes the industry has experienced over the years. He has sourced multiple skill sets across the world and helped some of the biggest organizations derive greater value from their permanent and contingent workforce.
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