Master Production Planning [MPP]
SOLUTIONS DESIGNED TO SYSTEMATICALLY ESTIMATE MARKET DEMAND
Plan orders across multi plant, multi resource / line to get a optimal production plan balancing conflicting objective function of Capacity Utilisation and OTIF (MTO) or Capacity Utilisation and Inventory Coverage / Service Levels (MTS)
Description
MPP uses Mixed Integer Linear Programming techniques to plan the demand and supply at an aggregate level. MPP does production planning at key resources on a longer time horizon and allows us to plan orders across the plants at an aggregate level to achieve objectives of Profit maximization, Capacity Utilisation, OTIF etc. to arrive at a Production plan at a day / week level. MPP considers constraints such as Lot Size, MOQ, Set Up Time Matrix, etc.
MPP on dataSAVI has two main versions; one for MTO (Made to Order) and another for MTS (Made to Stock
MPP is useful where also be used for tradeoffs like optimisation between capacity utilisation vs OTIF, high volume vs high contribution orders, Product Mix, Capacity Utilisation vs Optimal Inventory Coverage etc.
Features
Multi Plant / Multi Line Optimisation
Streamlines production activities across various plants and production lines, ensuring efficient resource allocation and minimizing costs.
Comprehensive Constraint Management
Manages diverse constraints such as
Scheduled and Unscheduled Maintenance MOQ, Lot / Batch Sizes
Material Availability (WIP, SFG, FG)
Set-Up Time Matrix Optimization
Develops optimized set-up time matrices to minimize changeover durations between production runs, enhancing equipment utilization and reducing downtime.
Configurable Objective Functions
Tailors objective functions to match specific business goals, allowing for flexible optimization and trade-off analysis such as
Set Up / Changeover Time vs OTIF,
Set Up / Changeover Time vs Inventory Coverage, Optimal Product / Order / Customer Mix
Strategic Product/Order/Customer Mix Optimization
Optimizes product, order, and customer mix to maximize profitability, considering factors like demand variability, production capabilities, and customer preferences.
Multi-Time Period Optimization
Plans production activities across multiple time periods to ensure sustained efficiency and alignment with strategic objectives, incorporating demand forecasts and resource constraints for effective decision-making.
Some Customer Cases
On the quantitative side, we have integrated the powerful Statistical and Graphical Modelling libraries of Python. We have also connected to ML / AI libraries of WML / Google / AWS to enables the application of a variety of statistical and ML/AI techniques for different situations
Consumer Electrical Company
Enhanced visibility and transparency via S&OP suite for a consumer electrical manufacturer
Speciality Tyre Company
Implemented Demand Forecasting and Demand Aggregation for the smooth function for the manufacturing unit
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