Pharmaceutical
The Challenge
A Global 500 pharmaceutical company was struggling with the aftermath of a significant merger and acquisition. The management faced the daunting task of realizing synergy targets committed to the board, particularly in consolidating processes, teams, and systems within the supply chain operations. These critical assumptions had yet to materialize, putting the CFO and the entire management under pressure to deliver on their promises.
Industry
Pharmaceutical
Location
Global
Attempted Solution before Scout
Demanding
12,000-
15,000 hours
of business users involvement was unsustainable
The CFO, Head of Supply Chain, and Chief Digital Officer, along with their internal digital transformation team and system integrator, embarked on a mission to rectify the post-merger challenges.
They deployed a process mining solution and organized manual ‘discovery workshops’ to understand the work patterns of their teams. However, these methods proved to be time-consuming and inadequate, demanding an unsustainable 12,000-15,000 hours of business user involvement across 66 processes and 13 countries, without providing the insights needed.
Enter
The CFO and Chief Digital Officer then evaluated and onboarded Scout to find and fix these problems. Scout’s AI model could be quickly put into action because it didn’t need complicated integration with current systems. The AI analyzed how supply chain teams interact with various applications, mapping out how and why work happens the way it does within the supply chain operations.
This led Scout to discover a key insight: the root of the problem was not just within the supply chain teams but due to poor visibility of key process information and lack of clear SOPs.
Scout’s AI model could be quickly put into action across
5 clusters in 13 countries,
because it didn’t need complicated integration with current systems.
Scout’s AI to “find and fix”
Step 1: Find
As the first step, the AI decoded the work patterns of the supply chain teams and connected them to business activities, simply by analyzing interactions between the teams and their software. It then automatically classified these work patterns as either core or non-core supply chain operational activities.
Based on this analysis, within two weeks, Scout’s AI model provided the following insights:
58%
effort was spent on core supply chain applications
Contrary to popular belief, only 58% of the team’s effort was spent on core supply chain applications and activities. This surprising statistic pointed to a significant work recall gap across the team, highlighting the disparity between the team’s understanding of how work is being done and how work was actually being done.
42%
time was spent on manual follow-ups
The AI also determined that the remaining 42% of the time was spent manually managing multiple spreadsheets, organizing and feeding data into various supply chain systems, addressing queries and coordinating with regional and central supply chain teams and suppliers on communication apps.
It also pointed out regional discrepancies, like teams in Southeast Asia spent an average 30% of their time on order management in Excel spreadsheets whereas the teams in India, Bangladesh and Sri Lanka spent 70% of their time on Excel spreadsheets.
Scout’s AI model further uncovered operators 1200 context switches per operator per day, due to toggling between applications due to information silos and disconnected systems.
These bottlenecks were forcing the team to incessantly toggle between different applications, ultimately resulting in a significant loss of productivity.
Step 2: Fix
Based on the above insights, Scout’s AI model recommended two levels of fixes:
Quick Fixes
These were ‘no-code’ fixes based on standardization and user training
Focused User Training
Scout identified that new joiners were taking significantly longer to complete processes. By auto-generating SOPs integrating best practices, the team could effectively train new joiners, improving processing time by 40%.
Central Repository for Key Excel Spreadsheets
Scout AI revealed that key Excel spreadsheets were being duplicated multiple times within teams. Some excel sheets were duplicated up to 26 times in a team of 15 people. By creating a central repository for these documents, the team reduced unnecessary effort and improved productivity by 10-12%.
Deep Fixes
Systemic and long-term fixes planned across the organization
Standardization of Processes across the 13 Countries
Scout’s insights showed that only 23% percent of the same processes overlapped across 13 countries. By standardizing and streamlining these processes, productivity improved by 32%.
Integrated Workflow System
The company created an end-to-end workflow system that integrated information inputs, validation, business rules, transaction processing, reporting, and compliance.
Creation of a Credible Automation Pipeline
The automation COE automated various key process clusters like Order management, Return Management, Rebate settlement, Inventory Reconciliation, Sample order management and administrative reporting, achieving 40% automation and significantly reducing manual effort.
It is important to note that in all of these recommendations, privacy was of the utmost importance – and no employee data was shared. Empathy was at the core of all recommendations and the focus was on addressing and improving the core issues i.e.
Getting teams to work together across functions
Fixing disconnected systems
Simplifying and optimizing work processes
Strategic Payoff
The strategic implementation of Scout's recommendations led to:
30%
Reduction in Operational Costs: Streamlining processes and reducing inefficiencies.
10%
Improvement in Cash Flows: Reflecting a more efficient and reliable supply chain process.
25%
Reduction in Delays and Errors: In order processing, enhancing reliability and customer satisfaction.
AI connects interaction data to business outcomes
Scout lights up the ‘dark side of the moon’.
Your business generates billions of data points from team-machine interactions. Scout, our AI model, deciphers this interaction data to reveal what often remains unseen—the hidden challenges your teams face at work and how they affect business outcomes, whether it's cost optimization, revenue growth, customer or employee experience, or business continuity.
The AI then provides data-based recommendations to address these challenges, paving the way for improved outcomes.
