About the Client
The client is a leading US-based pharma logistics services provider that offers warehousing, logistics and distribution services for medical-pharmaceutical products. The services offered by the client include all processes involved in different phases of pharma logistics, right from professional warehousing to optimized delivery and distribution.
The pharma logistics segment is experiencing major upheavals due to the dynamic economic environment and the technological disruptions in the market. Many companies have been successful in responding proactively to these disruptions by manufacturing and marketing medicines more efficiently, but they’ve invested relatively little effort in re-configuring their manufacturing, logistics and supply chain management operations. However, logistics and supply chain management is just as important to driving process efficiency as it’s the link between the manufacturer and the marketplace.
For years, pharma logistics companies have been challenged by various supply chain complexities. Today, many of them have critical new products on the brink of launch and are forced to redesign their supply chain network. The need to market new products faster with minimum delays have prompted pharma logistics service providers to design an integrated supply chain – one that offers a unified view of all internal and external operations to reduce the time-to-market and deliver better products with the shortest lead time and lowest cost.
Whether you seek to improve your logistics and supply chain management strategy, operations or transform your entire supply chain, our portfolio of advanced supply chain network optimization solutions offers the ideal combination of being tailored to your unique needs paired with faster time to value. Request a FREE proposal for detailed insights.
The US-based pharma logistics client faced similar challenges due to the lack of an enterprise-wide single source of truth & reliable, consistent and controlled reporting. Also, the complexities in delivering insights through complex reporting and dashboarding processes prompted them to collaborate with Quantzig to slice and dice supply chain data and optimize operations using an analytics-backed approach to supply chain network optimization.
As part of a strategy to optimize pharma logistics supply chain operations, the client partnered with Quantzig to implement a solution based on advanced supply chain analytics techniques and machine learning algorithms. Collaborating with a team of supply chain network optimization experts and data scientists, the pharma logistics services provider was able to shorten it’s time to market by optimizing supply chain operations and improving process efficiency.
Quantzig adopted a three-pronged approach to optimize supply chain operations:
For building a supply chain optimization model, the analytics experts at Quantzig leveraged advanced analytics and powerful insights that combined supply chain network optimization and dynamic simulation capabilities in one package, empowering them to measure every aspect of supply chain performance to enhance decision making.
Phase 1: Baseline Scenario Modeling
The first phase of this supply chain network optimization engagement revolved around the creation of a baseline scenario model to analyze the current state of the supply chain and to better understand factors impacting supply chain efficiency.
Phase 2: Data Analysis
The second phase of this supply chain network optimization engagement focused on integrating pharma logistics supply chain data from disparate sources to identify factors impacting supply chain efficiency. The use of advanced data analysis and statistical models helped generate comprehensive insights and analyze the pharma logistics supply chain issues in real-time.
Phase 3: Data Interpretation
In the third phase of this supply chain network optimization engagement, we focused on interpreting data to develop a strategic and tactical approach to decision making with visibility into pharma logistics processes, network design, sourcing, and capacity. The aim was to enhance supply chain network optimization by analyzing what-if scenarios and the cost factors associated with network redesign and decisions based on stochastic parameters.
- 15% reduction in changeover time and cost through optimized production plans
- Accurate demand forecasts to prevent overtime and variable shift patterns
- Reduction in inventory levels through reduced cycle times and increased conformance to plan
- Dramatic reduction in pharma logistics scheduling time through automation
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