Improve Bid Negotiations

Enhancing Negotiations with Advanced AI Solutions

Business Challenges

The key business problems to address are:

  • Predictive Bid Amount: Develop a model to predict an optimal bid amount that represents a favorable deal.
  • Deal Prioritization: Implement a system to prioritize deals acknowledging the limited availability of negotiation resources.
  • Guidance on Negotiation Amounts: Provide actionable insights guiding negotiation teams on the optimal amounts during the negotiation process.

Our Approach

  • Data Management: Establish a robust data creation and storage system integrating information from diverse sources.
  • Predictive Modeling: Identify and leverage key features for a Bid Prediction Model ensuring accuracy and reliability.
  • Expert-Driven Design: Craft the solution based on industry expertise and validated research aligning it with the unique dynamics of the beverage manufacturing sector.
  • Automated Bidding Pipeline: Implement an automated bidding modeling pipeline ensuring adaptability to changing trends through weekly model training.
  • Optimized Negotiation Guidance: Deliver a dynamic range of bid amounts to facilitate efficient contract landing significantly reducing negotiation time.

Use Case

The client, a prominent beverage manufacturer in the USA, faces the challenge of strategically navigating negotiations given resource constraints. Engaging in agreements with shipping organizations, negotiation becomes a crucial aspect of client operations. This initiative seeks to enhance the precision and efficiency of these negotiations, enabling the client to navigate the complex landscape of deal-making with strategic acumen and resource optimization.

Results

  • Up to 50% improvement in bidding.
  • A range of achievable bid amounts for landing contracts.
  • Up to 50% improvement in achieving contracts & customer satisfaction.
  • Enhanced model performance with real-time adjustments.
  • Expanded prediction coverage to over 95%.

Key Takeaways

  • Azure platform for development and deployment purposes.
  • Python programming language was used for the development of the algorithms.
  • Trained multiple machine learning models for different levels (National Clusters Lanes SDPs).
  • Automated model selection for each level based on evaluation metrics.
  • Provides predictions against each feature set along with Bands (Upper & Lower) and Trust Score.
  • Performed Bayesian Analysis, created and deployed Bayesian Model.
  • Operational efficiency and resource optimization in negotiations.