Case Study

Intelligent Knowledge Management for Livestock Sales

Banking
The global livestock industry is now estimated to be worth ~ $450B. This is up from ~$350B in 2020. The industry is expected to top $700B by 2030. This growth has presented new challenges for the industry as companies struggle to cope with increased volumes of transactions, market disparities, and pricing volatility. The continued reliance on legacy, non-digitized, information management solutions has exacerbated the problem.

Optimizing organizational knowledge to maximize business outcomes

Current Challenge

Common Problems Faced by Enterprises

Many existing information management solutions were deployed to support livestock industry conditions that existed two or three decades ago. Those solutions tend to be more analog in nature, inflexible in design, and self-limiting in capability. These shortcomings create several operational and business disadvantages.

01

Inefficient knowledge retrieval

Slow access to stored knowledge results in missed opportunities and/or non-optimal actions.

02

Inability to automatically segment data by region

Creates additional work burdens and increases potential for errors in regional business assessments.

03

Loss of tacit knowledge due to employee turnover

often results in permanent loss of critical information.

04

Manually activated processes

rely on human touch points that are prone to error and inefficient in execution.

05

Lack of natural language processing (NLP) capabilities

restricts the ability to analyze large volumes of textual data to glean valuable insights into customer and market trends.

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Plasma delivers intelligent Knowledge Management Systems (KMS) tailored for the specific needs of the livestock industry; KMS that enable organizations to improve internal efficiencies, maximize knowledge utilization and retention, improve market response times and drive revenue growth.

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The Solution

Intelligent Knowledge Management

Key capabilities of Plasma’s intelligent KMS:

  • AI-enabled knowledge search and discovery
  • Rapid access and retrieval of current and historical information
  • Roles and permissions-based access to sensitive data
  • Tacit knowledge retention despite employee turnover
  • Efficient and cost-effective storage of critical operational data
  • Self-learning algorithms that provide hardened protection of stored information
  • AI-enabled workflows for data collection, analysis, and action
  • Automated decision-making based on perpetual self-learning
  • Reduction in the number of human touchpoints reduces risk for error and improves security
  •  
  • Automated mining of unstructured data for hidden insights
  • Provision of customer service that is faster and more precise
  • Recursive data analysis to predict upcoming trends and potential problems
  • Proactive vs reactive use of knowledge acquired by the enterprise
  • Extension of visibility horizon for competitive advantage

Business/Operational Impact

The Intelligent KMS Impact on the Livestock Industry

 
Problem
Solution Impact
Knowledge Retention
Loss of organizational knowledge due to employee and system turnover
AI-enabled collection and storage of knowledge for permanent availability
Knowledge Retrieval
Delayed access to data creates slow reaction to fast-moving business opportunities
AI-enabled rapid search and discovery of information needed to support decisions and actions
Market Insights
Inability to maintain real-time contact with rapidly changing livestock market conditions
NLP technology provides large scale market insights based on recursive analysis of textual content
Process Workflows
Legacy workflow solutions that require human intervention and that do no evolve
Automated intelligent workflows that learn and evolve to maintain maximum efficiency and accuracy
Predictive Intelligence
Inability to accurately predict market conditions and decision impacts
Self-learning algorithms that utilize current and historical data to predict future market conditions