The Supply Chain of any CPG company is central to its operational fluidity and profitability.
Digitalizing the entire span of a legacy supply chain would be considered a tall order by most because of the expenses involved and capabilities needed to handle the complexities. Moreover, success is also not guaranteed.
This case study is about a very large Consumer Packaged Goods (CPG) company that refines and sells vegetable oil in Asia. Their top management inferred that there was a significant loss in their supply chain, between raw material arrival from international suppliers by ship and the finished product delivery from their factory gates.
Customer Profile: Large CPG player in Asia
The company’s top-line was growing consistently due to the rapid economic growth of the region, and it was profitable as well. However, the supply chain losses amounted to a massive Lost Profit Opportunity (LPO). The LPO was estimated to be between 11% - 14% of their margin.
In sum, they were losing out on a profit opportunity of approximately USD 5 - 6 million for every USD 100 million of revenue.
The customer had typical business management applications such as SAP (FICO, MM, etc.), reports such as Voyage Data Recorder (VDR), Surveyor report, and a few others. However, much of the critical supply chain data was collected using manual and handwritten logs – e.g., reading from weighbridges, dipstick tank measurements, etc. Due to the manual nature of the field data, validation was not possible.
Excel reports were created on the manually collected data and reconciled with business data monthly and quarterly. The reconciliation showed a discrepancy to the tune of 12% - 15%. However, those reports were not suitable for any specific action - it was just a postmortem report without any actionable information. There was a major disconnect between business management and physical operations.
The management was fully aware of the issues and the urgency to address them. They were initially in talks with large consulting companies, automation vendors, and system integrators for a solution. They suggested complex and large-scale instrumentation & field automation, and the adoption of other IT applications. However, the suggestions & proposals made by these companies were not suitable or convincing enough, and very expensive.
We approached the issue from an overall value chain perspective which spanned the entire horizontal of the supply chain.
The solution was based on a few key strategies:
The idea was to capture timely and accurate data critical for the performance of the digital supply chain. Enforcing compliance with new processes/workflows at the operations level is difficult and usually fails in the long run. The industry is full of examples of large IT applications never being used by the operations staff.
We therefore only automated data capture from existing instrumentation, such as weighbridges, etc. We also turned the offline manual logs into connected forms while maintaining the exact look & feel. The connected forms ensured real-time data validation, auditability, and compliance.
We added new (connected) instrumentation (flow meters, tank gauges where necessary) at critical junctions only to capture discrepancies and anomalous trends well within the opportunity window or in near-real-time.
Existing business applications (SAP, etc.) were also connected to the platform to capture all historical data and any new data in near real-time.
dDriven’s UNLSH platform captured all of the data from business and field systems. The data from both sides were continuously synced, reconciled and analyzed. Exceptions, anomalies, and trends were reported in near real-time and well within the opportunity window. The system allowed:
The solution added unprecedented visibility to the bad actors, bottlenecks, etc., and allowed the management to take fact-based decisions within the opportunity window. The losses dropped by 90% within six months compared to the historical level (computed and analyzed by the solution to establish a fact-based baseline).
It also allowed the management to prioritize capital investments into better instrumentation, automation, and plant machinery. Analysis of Lost-Profit-Opportunity (LPO) at granular level showed the potential payback of any new investment and allowed validation of the LPO post-implementation.
The LPO analytics also demonstrated the payback of this project in just Four Months.
