Mathematica Solution for Industrial Engineering

Optimize your processes, 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

Key Capabilities

Why Choose Mathematica

Ways to Use

Mathematica includes thousands of built-in functions for computation, modeling, visualization, development, and deployment »
Built-in statistical functions such as ANOVA, standard deviation, and more for implementing Six Sigma and other quality management methodologies
Estimation of distribution parameters from data and testing of goodness of fit of data to distributions
Powerful optimization routines for maximizing process efficiencies using Just-in-time inventory management, Lean manufacturing, and other practices »
Built-in linear, nonlinear, logit, probit, generalized linear, and other regression models »
More statistical distributions than any other system, with the ability to define new distributions from data, formulas, or other distributions »
Efficient random number generation for developing fast, robust simulations of assembly lines, queuing systems, and other applications in manufacturing, material handling, logistics, and other industries »
Automatic interface construction for real-time analysis of working prototypes »
Instant deployment of your prototypes using Wolfram CDF Player or webMathematica
Generation of dynamic reports containing graphics, text, and interactive applications »

Key Capabilities

Why Choose Mathematica

Ways to Use

Compare Mathematica to your current tools. Do they have these advantages?
Free-form linguistic input produces immediate results without the need for syntax »
Competitor note: Unique to Mathematica
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
Instant, interactive interface construction using a single command to visualize your simulations, examine prototype sensitivity to parameter changes, and more »
Competitor note: Unique to Mathematica
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
Automated precision control and arbitrary-precision numerics produce accurate results for large-scale finite element analysis and other problems »
Competitor note: Matlab relies on finite-precision numerics, which can cause serious errors due to lack of precision

Highly optimized superfunctions analyze your equations and automatically select the right algorithms to get you accurate results quickly—sometimes switching mid-calculation for further optimization »
Competitor note: Non-Mathematica computation systems make you analyze your equations manually to determine which function to apply—e.g., where in Mathematica you use NDSolve, in Matlab you must correctly choose among ode45, ode23, ode113, ode15s, bvp4c, pdepe, and so on or risk wrong answers
Complete workflow, from simulation to analysis to typeset document or interactive slide show, in a single document
Competitor note: Unique to Mathematica
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Ways to Use

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
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Key Capabilities

INDUSTRIAL ENGINEERING CASE STUDIES

Case Studies

Some Major Organizations Using Mathematica

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User Stories

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—Markus van Almsick

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