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Data Science can solve many problems that plague industrial manufacturers. Modern machines – and even old ones! – create large amounts of data as they operate. Mathematical algorithms developed by us can look for anomalies in that data to pinpoint irregularities. These can be caused by wear and tear, incorrect machine settings or setups, or operator errors. Many of our clients use our algorithms to continuously optimize their machine operations to increase machine efficiency, reduce cost or waste, reduce energy consumption, detect and avoid operator errors, or implement condition- or predictive maintenance strategies.
Our clients typically approach us with specific business issues that need to be addressed – a pain point. This can be perceived shortfalls in machine efficiency or productivity, higher than needed energy consumption, waste, or product rejects, repeated machine failures, the desire to anticipate and avoid unplanned downtime, or to create new service revenue streams by proactively predicting part failures and providing replenishment.
Based on this pain point our Data Science team will engage with the client to develop a solution approach based on a data-science hypothesis. In other words, we will jointly with the client develop a strategy to solve the issue using the data and tools available from the client.
Before engaging on a full scale project we will usually conduct a short term study to efficiently proof, or dis-proof, that the hypothesis can be substantiated with data. This avoids long and expensive exploratory projects and avoids disappointment if the hypothesis turns out to be incorrect. Our projects deliver success efficiently – or fail very quickly saving you time and resources.
Having a lot of data can be an advantage but is not at all necessary. As part of defining the solution strategy our team will review available data and create strategies to collect all necessary data while the project is in motion.
Any “new” algorithm specifically designed for a client in a full-scale paid engagement (not a pilot) is owned by the client. At the end of the project we will sign over the Intellectual Property rights to the client who is then free to continue to develop the algorithm, extend it, or roll it out to as many machines and plants as they wish, free of charge.
Data cleaning usually starts with the identification of the most important dimensions, which sufficiently describe the processes that should be modeled. Once these dimensions, or features are selected, the second step will be to ensure that the data make sense, by removing any duplicate columns, deleting irrelevant data, removing statistical outliers and filling missing values.
Every algorithm is designed to solve a particular type of problem. At Spur Insights, we start by thoroughly understanding the business challenge and the nature of the data available. Based on this, and drawing from our deep experience across industries, we identify and tailor the most effective algorithmic approach to deliver accurate, actionable results.
Our goal is not just to apply models—but to apply the right ones that align with your objectives and deliver real impact.
Projects are designed for seamless integration into existing systems. Hence the API approach is usually chosen so that the existing systems can send requests to the Data Science systems, which allow those to not necessarily have to be on the same physical systems.
Our clients are free to use their own hosting environments to implement any Data Science algorithm on their own, or existing, IT infrastructure, or directly on their machines.
The Data Science team is always available to re-engage after an algorithm has gone live to provide maintenance or fine-tune the algorithm as this might be required when machines or operating environments change. Having said that, clients own their algorithms and are free to perform their own maintenance as well, as they see fit.
Data Science projects usually start with a Pilot, in which we explore the data for the project. Insights of this phase helps define a timeline of the overall project. Project durations can vary, ranging from 3-4 weeks to several months.
The Data Science team engages through different business models. We offer hours-based consulting and Data Science services, or fixed-price contracts. Talk to us to find out what might be appropriate for your project!
