Translytics’s advanced algorithm revolutionized a leading footwear manufacturing producer to optimize their Network Design by leveraging advanced analytics.
Success Stories
Home»Success Stories»
Translytics’s advanced algorithm revolutionized a leading footwear manufacturing producer to optimize their Network Design by leveraging advanced analytics.
The Optimal Warehouse allocation algorithm developed by Translytics has transformed a prominent footwear manufacturer, revolutionizing their approach to optimizing Supply Chain Network
Network Design
Translytics's advanced algorithm revolutionized a leading footwear manufacturer producer to optimize their Network Design by leveraging advanced analytics.
Summary
Being a prominent Indian footwear company that specializes in manufacturing and distributing a wide range of footwear products. Founded in 1984, it has established itself as one of the leading footwear brands in India. The company is known for producing a diverse array of footwear, including sandals, slippers, shoes, and other types of footwear for men, women, and children. The customer was struggling with identifying the optimal number of warehouse and Network Analysis for the same. Translytics helped them to achieve remarkable savings by suggesting to implement the most suitable strategy for them.
15%
Overall cost reduction in their Network
Areas of Focus
The Translytics team worked together with the customer to gain a comprehensive understanding of their challenges. Through data analysis and subsequent discussions, the following focal points were identified:
Optimal number of Warehouse which will provide cost savings without much affecting Service Levels
Transportation Cost Analysis for the selected warehouse model
Service Level Analysis for the model selected and comparison with current scenario
Solution Approach
In response to the customer's specific needs and challenges, Translytics utilized sophisticated Network Design algorithms to conduct a thorough analysis in the following areas:
Understand As-Is Network Distribution
Analyse the gaps in the process
Confirm the process gaps through data using analytics on python
Used Inhouse codes to identify the idle number of warehouse and the costs associated with those scenarios were calculated and compared
Suggest actions to optimize the entire supply chain network design
Recommendations how they should use their plant/warehouse if they opt for a Central Warehouse model
Impact
Once the solution models were finalized and deployed, the customer reaped the following benefits:
Reduced entire Network Transportation costs by around 15%
Service levels also improved as customers were catered from nearby locations
Suggestions like Multipoint deliveries and elimination of depot-depot and deliveries more than 2000kms