In order to realize the full range of productivity advantages from digital transformation, procurement organizations will need to embrace a cycle of continuous improvement that generates value throughout the entire lifecycle of automated processes, according to The Hackett Group’s “Digital Continuous Improvement” Point of View paper.
Far from independent advancements, businesses will need to combine automation programs with advanced analytics to interpret process performance data for additional optimizations and value opportunities. The Hackett Group estimates world-class organizations enjoyed a 68% productivity advantage in highly integrated areas like supplier onboarding and contract management, sourcing, and dynamic buying while reducing costs 55% compared to peer organizations.
The Hackett Group defines smart automation as capabilities that optimize execution of business tasks through the deployment of advanced technologies. Robotic process automation (RPA) can be used to read and transmit data from invoices and other sources into ERP systems, while smart data capture interprets unstructured supplier documents and organizes them within supplier relationship management (SRM) portals.
Pattern recognition tools can compare contract terms and supplier events across hundreds of thousands of data points to identify favorable opportunities, while conversational bots or “chatbots” provide responses to common queries and guide users to the resources they are searching for.
Combining these capabilities allows procurement organizations to automate structured work like maintaining pricing files and catalogs, submitting purchase orders, or reviewing contract terms.
Smart automation also enhances knowledge work, providing decision makers with a deeper view of global, regional and local raw materials, supplier data, and production capacity. Internal and external communications are also boosted as company policy is more easily managed and enforced and information flows more freely, while internal surveys or forecasts can be conducted much more efficiently.
Past, present, predictive
In the past, procurement groups generated descriptive and diagnostic analysis, including analysis of historical trends, variance and correlation to try to address why things occurred.
Increasingly, The Hackett Group sees procurement teams being called on to generate predictive and prescriptive analysis to understand what will happen and how the business can achieve the desired outcome.
Machine learning, advanced statistical algorithms and neural networks are being employed in these roles to interpret vast quantities of unstructured data. These tools can enhance spend and sourcing using predictive analytics to efficiently shift between buying channels to reduce delays and take advantage of favorable commodity pricing.
Predictive analytics and market intelligence can also help identify impending geopolitical or weather-related risks, while flagging suppliers and partners who represent a compliance risk. All of these applications grow stronger over time, as learning algorithms become more effective from the continuous feed of process performance data being generated.
How to pursue continuous digital improvement
The Hackett Group provides a number of recommendations for organizations pursuing continuous digital improvement.
Fundamentally, procurement teams must ensure the digital transformation strategy aligns with the overall business strategy and use a framework to guide planning that accounts for interdependencies between different systems.
Source: The Hackett Group (Click to enlarge)
To discover areas for development, teams should benchmark performance externally against other organizations using established diagnostic tools. When building use cases from smart automation and analytics, consider elements of work — like structure based, knowledge based, or interaction based — instead of existing roles, allowing for maximum use of continuous improvement within different work structures.
Source: The Hackett Group (Click to enlarge)
Finally, The Hackett Group recommends prioritizing skills development in areas of data science, strategic thinking and customer-experience design that are essential for the widespread deployment of smart automation and advanced analytics.
