Optimize your processes and implement quality management measures, rapidly prototype and deploy interactive applications, and generate live reports automatically—all in one system, with one integrated workflow.
Underlying the Mathematica industrial engineering solution are state-of-the-art algorithms, sophisticated statistical analysis tools, self-checking, high-precision arithmetic, and the world's most accurate symbolic and numeric engine.
Interactively optimizing processes using built-in functions
A capacity planning model for short life cycle products that optimizes order quantity and cost in the face of uncertain demand
Solving high-performance optimization problems involving nonlinearity and thousands of variables
Optimizing flow in a pipe network
Rapidly designing and simulating non-standard applications
A custom face gear designed for a Proctor & Gamble consumer appliance; a simulation of a cold forging process, and the deflections in the workpiece-die-press system
Compare Mathematica to your current tools. Do they have these advantages?
Built-in functionality for constrained and unconstrained optimization, statistical analysis and computation, simulation, curve fitting, and a range of other application areas Competitor note: Matlab requires the purchase of multiple toolboxes
Easy-to-use parallel computing capabilities for solving computation- or data-intensive problems on multicore computers or grids Competitor note: Extensive programming is required to parallelize processes in all other systems Competitor note: Matlab requires an extra-cost toolbox
Complete workflow, from simulation to analysis to typeset document or interactive slide show, in a single document Competitor note: Unique to Mathematica
KEY CAPABILITIES
WHY CHOOSE MATHEMATICA
WAYS TO USE
Designing manufacturing processes and developing statistical process control measures
Developing production schedules and establishing inventory levels
Designing and analyzing supply chains
Designing custom engineering components that adhere to tight tolerances
Developing computer simulations of industrial engineering systems
Improving manufacturing process efficiencies using Lean manufacturing techniques
Improving product quality by applying quality management methodologies, such as Six Sigma
"This capability of not having to compile things and not having to stick to a certain technology, mathematically speaking, helps quite a bit—the freedom of choice [for] what mathematical tools to use."