Why is my business not already using simulation? This post is a quick guide to some commonplace historical hang-ups and where they’ve left us…
Simulations are exceptionally powerful tools for optimizing systems, guarding against risks, and ultimately for making better-decisions. In spite of this, adoption remains a fraction of what it could be, and many organizations fail to utilize simulation at all.
The lowest-hanging fruit within data science and decision-support has, for a long time, been simple regression analysis and anomaly detection. Neither of these require any form of ‘simulation’, and both may yield relatively high-payoffs (or cost savings) when applied properly.
As companies look to increase the sophistication of their data-driven operations and ask where future value may lie, simulation offers the next-best bet.
Why, though, hasn’t simulation taken off to date?
Historically expensive to build, as well as time-consuming and costly to maintain, “digital twins” of organizations, their people, and their processes have been out of reach for all but the largest of players — and even then they have rarely found their way out of siloed applications.
The most common forms of simulation modeling today are:
- partial-implementations of things like Monte Carlo methods within software such as Excel; and
- highly specialist, domain-specific applications (such as Finite Element Analysis software) which carries with it a high barrier to entry, and typically lacks interoperability with other analytical frameworks.
Tooling around simulation has additionally been expensive, and hard-to-use, all but ruling out simulation modeling for everyday, general-purpose and cross-organizational use.
Our mission at HASH is to eliminate information failure, and as part of that we’re working to democratize access to powerful simulation tooling through a free-to-access, vertically-integrated simulation platform.
Simulation finds itself broadly-speaking where Machine Learning was approximately ~15 years ago. Proven out in academia, utilized by defense departments of advanced nations, and adopted by quant-hedge funds — but beyond the reach of everybody else.
HASH aims to address this by being:
- Fast to build meaningful models with — identifying data in, and transforming data to, an “agent-mapped format” has historically been time-consuming. Through hIndex, and Flows in HASH respectively, we make this easy. hIndex additional contains community-published simulations and behaviors which can be forked and cloned for use in one’s own business, covering a wide range of supply chain, logistics, cloud computing, social distancing, and other key business concerns.
- Free to use in most cases, and low-cost at enterprise-scale (pricing);
- Built atop an open-source core (hEngine);
- Reproducible and verifiable at heart, earning the trust and confidence of users who may not be familiar with, or otherwise open to, simulation-backed decision-making.
- Production-ready: through scheduled runs, background recompute, and programmatic outputs, insights from HASH simulations can be continuously incorporated into real data-science workflows, and algorithms.
This latter point is perhaps most important. Productionizing simulation outputs has long been a manual, time-consuming process. Outputs of simulations have, at best, sometimes been seen as mere “cool visualizations”. With HASH, simulations of all kinds (including system dynamic and agent-based models) can become first-class citizens within data scientists’ toolkits at last.