Why Spashta ?
A need for better supply chain decision making solutions. Our expertise spans all aspects of supply chains and our portfolio includes projects for some of the world’s largest organisations.
Optimizing network performance
A comprehensive range of supply chain services
The Problem
Traditional solutions for supply chain decision making are siloed in approach, inflexible, and with long implementations resulting in low adoption and ROI Users create their own 'Excel clusters' for day-to-day decision making despite having expensive implementations
Root Cause
Optimal SCM solution architecture requires Three Critical Dimensions: Supply Chain Domain, Mathematics & Software Technology Traditional solution providers have evolved organically and are good in one or two of these dimensions
Companies are getting low ROI from SCM software
Supply Chain Insights LLC, Supply Chain Planning Software Study May-July 2018
Spashta's Goal: Enable supply chain agility with an interactive, intelligent, decision-making platform
Companies are getting low ROI from SCM software
Supply Chain Insights LLC, Supply Chain Planning Software Study May-July 2018
Address global, full scale supply chain problems
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Respond rapidly to business challenges
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Enable dynamic & boundary less planning
• 5-15% improvement in cost, service level & inventory
• Manage risk & business cycles build supply chain agility
Spashta's edge in Solution Technology
Specific Solution Approaches
Supply Chain Network Design
Inventory Optimization
MILP Optimization
Stochastic Algorithms
Demand Planning
Production Planning & Scheduling
Statistical Algorithms
MILP Optimization & Heuristics
Distribution Planning
Container Load Optimization
Linear Optimization & Heuristics
Combinatorial Optimization
Dispatch Planning
Master Planning / Supply Planning
Combinatorial Optimization & Heuristics
Linear Optimization & Heuristics
E.g. Dimensions of a Master Planning Problem
- Size & Scale ... no of demand points, SKUs, mfg plants, DCs, etc.
- Time Horizon ... no of time buckets being planned into the future
- Complexity ... tiers of supply chain, capacity constraints, shared resources
- Business Rules ... shelf life, lot sizes, attribute based planning, etc.
- Real time ... open orders, stock levels, in-transits, etc.
Spashta's Solver Performance
Efficacy … consider all dimensions of the problem: self learning solvers
Speed … solve large & complex problems in minutes
Agility … perform rapid “what-if scenarios”, fine tune business rules, perform “anchored planning”
Sample Solver Run Statistics
| # of Supply Chain Tiers | 4 |
| # of Mfg Plants | 48 |
| # of Stages of Capacity | 5 |
| # of DCs | 93 |
| # of Resource-Lines | 130 |
| # of SKU-Locations | 124,333 |
| # of Time Buckets | 60 |
| # of Demands | 5,856,000 |
| Shelf Life, Lot Size, Multi-modal Lead Times | |
| Machine | T2.2XL on AWS |
| Run Time | 6 min 29 sec |
Specific Solution Approaches
Supply Chain Network Design
Inventory Optimization
MILP Optimization
Stochastic Algorithms
Demand Planning
Production Planning & Scheduling
Statistical Algorithms
MILP Optimization & Heuristics
Distribution Planning
Container Load Optimization
Linear Optimization & Heuristics
Combinatorial Optimization
Dispatch Planning
Master Planning / Supply Planning
Combinatorial Optimization & Heuristics
Linear Optimization & Heuristics
E.g. Dimensions of a Master Planning Problem
- Size & Scale ... no of demand points, SKUs, mfg plants, DCs, etc.
- Time Horizon ... no of time buckets being planned into the future
- Complexity ... tiers of supply chain, capacity constraints, shared resources
- Business Rules ... shelf life, lot sizes, attribute based planning, etc.
- Real time ... open orders, stock levels, in-transits, etc.
Spashta's Solver Performance
Efficacy … consider all dimensions of the problem: self learning solvers
Speed … solve large & complex problems in minutes
Agility … perform rapid “what-if scenarios”, fine tune business rules, perform “anchored planning”
Sample Solver Run Statistics
| # of Supply Chain Tiers | 4 |
| # of Mfg Plants | 48 |
| # of Stages of Capacity | 5 |
| # of DCs | 93 |
| # of Resource-Lines | 130 |
| # of SKU-Locations | 124,333 |
| # of Time Buckets | 60 |
| # of Demands | 5,856,000 |
| Shelf Life, Lot Size, Multi-modal Lead Times | |
| Machine | T2.2XL on AWS |
| Run Time | 6 min 29 sec |