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Agribusiness comprises the economic sectors dedicated to farming and associated commercial activities. It encompasses the entire process of bringing agricultural goods to the market, encompassing production, processing, and distribution. This sector stands as a longstanding pillar in any economy, particularly for nations endowed with fertile land and surplus agricultural products destined for export.

Use Case – Supply Chain & Operations Planning

The Company
A renowned high-tech American multinational technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence and consumer electronics.

  • ~$1B Revenues (Yearly)
  • 60 Plants
  • ~1k+ SKU's
  • 4 Business Lines
  • Spreadsheet data-entry errors and lack of coordination between departments
  • A high cost of Inventory
  • Consistent stock outs
  • A low rate of Inventory turnover
  • A high amount of obsolete inventory
  • A high amount of working capital
  • A high cost of storage
  • Inadequate forecasting
  • Neglected Trends
  • Overstocking Discounted Products
  • Shipping the wrong items to customers
  • Lost customers
  • Imbalanced lead times

  • Budgeting and forecasting

    Forecasting to predict future trends and changes in KPIs, and budgeting to get a roadmap for allocating resources to achieve financial goals.

  • CAPEX & OPEX Planning

    Planning for physical assets, such as buildings, equipment, machinery, and vehicles and planning of employee salaries, rent, utilities, and property taxes etc.

  • Scenario planning

    Proactive planning for uncertainties, enhance resilience, and adapt swiftly to changing circumstances.

  • Management reporting

    Internal reports used to run the organization, make business decisions, and monitor progress to make more accurate, data-driven decisions.

  • Data Integration across organization
  • Product & Geographical hierarchies
  • Sales & Inventory Transactions
  • High Accuracy Forecasting Model
  • First cut forecast
  • Test against constraints
  • Re-cut forecast
  • Machine Learning Model
  • MVR Forecast
  • Inventory Norms Analysis
  • Data Warehouse setup
  • Automatic capacity adjustment (Optimal Qty & Timing at dept. level)
  • Integration of Demand Planning with Supply Planning
  • Integration with PnL
  • Multiple Plan Simulation based on Inventory, finance etc.
  • Multiple What-if Scenario
  • Dashboard Design
  • Multiple Algorithms (Statistical baseline)
  • Marketing forecast
  • CRM Integration
  • BOM Explosion (Demand Planner Forecast)
  • FP&A Integration (Finance Forecast)
  • Production Planning (Multiple What - IF’s)
  • Inventory Plan(RCCP/Safety stock Plan)
  • Sourcing Plan (Make/Buy Decision)
  • Allocations Plan (By RM/Region)
  • Capacity Planning ( By Constraints)
  • Supplier Plan (3rd Party Decisions)
  • Improved forecast accuracy from 30% to 70% at SKU Level

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