SUPPLY CHAIN ALLOCATION PLANNING
SOLUTIONS DESIGNED TO DETERMINE CONSTRAINED SUPPLY PLAN
Get realistic estimate of the extent to which consensus demand can be serviced considering production, material and logistics constraints with prioritisation given to different orders / customers and different objective functions
Description
SCAP uses Mixed Integer Linear Programming (MILP) technique to decide the optimal allocation of demand / inventory to the various sources and distribution point. The tool can be configured for cost minimization or contribution maximization or mixed objective functions.
SCAP considers parameters such as logistic cost, production costs, handling costs, taxation, market price etc. in the MILP model run different scenarios of Cost Minimization / Contribution Maximization or any other Mixed Objectives. The MILP is run with the constraints such as Supply Constraints, Mode Capacity Constraints, Demand Constraints, Norms Constraints. Allocation Planning can be used in various situations of Distribution Allocation. Production Allocation, Inventory Allocation, Product / Customer Mix depending on the industry and the specific Use Case
Benefits
Features
Multi-Echelon Configuration
Incorporates multiple plants, warehouses, hubs, and depots across various locations to optimize supply chain efficiency and responsiveness.
Diverse Product and Mode Handling
Manages multiple products and transportation modes, enabling flexibility and adaptability to different market demands and transportation requirements.
Cost Minimization and Contribution Maximization
Implements constraints such as service levels, mode availability, and capacity utilization to minimize costs while maximizing overall contribution to profitability.
Capacity Optimization Across Multiple Dimensions
Considers product-wise, line-wise, and aggregate capacity constraints to ensure optimal resource allocation and utilization throughout the supply chain network.
Multi-Market Adaptability
Tailors supply chain operations to accommodate diverse market demands and geographical variations, ensuring efficient distribution and customer satisfaction across various regions.
Seasonality Analysis and Forecasting
Incorporates build-ahead strategies for seasonality analysis, enabling anticipation and management of variance or deviation in demand across multiple time periods, thus enhancing responsiveness and efficiency
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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