An Introduction to Ontology
A Data Ontology is a representation, formal naming, and specification of the categories, attributes, and relationships between data. In summation, an ontology uniquely identifies a set of concepts and categories that represent data, illustrate its qualities, and describe the relationship between portions of data.
How to best make the knowledge contained in a specific formal ontology more easily available for automated information processing is an effort many computer scientists study and debate continuously. It's necessary to establish this framework to investigate linked data and how it relates to other information.
You can apply philosophical ontologies and their concepts to investigate any type of data and its relationship to other data. For instance,
- In Medicine ontologies constructed from symptoms help healthcare professionals diagnose patients.
- Data ontologies are the backbone of Sabermetrics for baseball and other sports analytics applications such as gambling.
- In the KatalystDI platform each Package has an ontology relating it to the data it represents (the structured BOM, associated suppliers, reference design, installation standards, etc.).
Arguably, the most famous application of ontology to industrial production is the Toyota Production System (TPS). Toyota determined that designing processes that give desired results effortlessly and eliminating inconsistency would have the most impact on their process value delivery. The TPS organized manufacturing, logistics, and communication with Toyota's suppliers while making sure their processes were as flexible as required without stress or overburden (this would result in waste). These principles have been incorporated by many other manufacturers in their lean manufacturing strategy.
Data Ontology & Construction
Let's start with a close-to-home example. Say you decide to build a house. The first step isn't to walk out and break ground or even order supplies. Even in the smallest projects, there is always planning and permitting, then comes ordering materials, finding labor, followed by actual breaking ground.
Most home builders use some type of software such as a dedicated system or even a spreadsheet to collect all this information and manage it throughout the process from ideation to completion of your new house. Behind the scenes, there are formulas built into those spreadsheets or even a relational database of some type.
Ontology in construction describes the mapping of relationships, attributes of the data, knowledge sets, and other concepts such as workflows. That specifically means linking all the goods that will be purchased, when they are coming, how they will get there, who will install them, what they need to commission the equipment, and information on what those goods cost and when and how they should be paid for. It begins to sound challenging for even a small project like the house example.
The Industry Paradigm
Construction today is both one of the most forward-looking industries in terms of utilizing sophisticated technologies, and the most antiquated. On one side of the spectrum, you may have 2D project documentation, minimal permitting, a lengthy build process utilizing manual labor, and a project timeline that could be from months to years. On the other end, organizations who build large projects may take a holistic look at their projects and utilize an ontological process to break scope into work packages so that work is structured under precisely controlled conditions. For instance parts of a project may be built to precise 3D specifications in a factory environment, then delivered to the site, installed, and ready to commission within days.
Well constructed ontology allows deep analysis of cost or labor saving opportunities.
With a single-family residential project, the stakes for cost overruns and delays are relatively low. Depending on where you live, the average house will be somewhere between 1500-2500 square feet in size. Materials can often be purchased off the shelf and the labor skill needed is low.
Consider the implications when projects are scaled. If a multi-family unit that has 100 apartments that are an average of 1000 square feet it's approximately 50 times the size of a single-family home. A small amount of improvement is thus multiplied by 50. If we think about large industrial facilities this grows exponentially when you think about all of the packages involved. factor in costly long-lead equipment purchases and more complex technical requirements. Ensuring materials are ordered on-time and arrive when they are supposed to is critical to predictable construction.If you are a contractor, you may also face financial penalties for late delivery as your customer depends on delivery at a precise date to fulfill their obligations to theirs.
Why ignore ontology?
Those who build by utilizing more mature technological processes are better able to minimize waste and optimize labor costs through software tools. You see this in the fact that contractors who are responsible for large projects typically utilize dozens of software tools to ensure the precise coordination of all parts of the project. Procurement systems can analyze thousands of prices and help source materials with optimal cost-effectiveness. Logistics tools help track deliveries to ensure materials are on-site on time.
I'll pose a theoretical question then, why do many companies ignore the ontology of their extended construction supply chain and manage this information in multiple systems?
You'll hear many answers including:
- We don't have software to support it
- It is difficult to accurately map beyond 1-2 tiers of our supply chain
- Vendors are reluctant to provide information when they will be delayed
The key to success is defining your packages and their associated supply chain.
The challenges listed were difficult to solve 2-3 years ago and less relevant than they are in a world of routine supply chain disruptions. Throughout the pandemic, construction boomed. With closed borders, this has created a perfect storm of increasing demand, but dwindling supply for critical materials. In our world of increasingly disrupted supply chains, it's more important than ever to address this problem and why we built KatalystDI.
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