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“We’re bringing manufacturers new volume, we’re bringing them new customers, and we’re increasing that funnel on the front end.”

Chris Schron, Director of Data Science at Dealer Tire

What are the challenges of data science in manufacturing?

The manufacturing industry faces huge opportunities and challenges in the industry 4.0 transformation. Successful data science work helps manufacturers take advantage of the proliferation of data and stay ahead in the digitization and automation movement.

  1. Data science is research-based and experimental in nature. Manufacturing process has well established methodologies for tracking progress such as Kanban. Thus, managers can predict and control the process by using clearly defined metrics.

    Data Science is different as research is more exploratory in nature. Data science projects have goals such as building a model that predicts something, but like a research process, the desired end state isn’t known up front.

    This means data science projects do not progress linearly through a process like manufacturing does. The inherent uncertainty of research makes it hard to track progress and predict the completion of data science projects -- something manufacturers are not used to.

  2. Deploying models into production. Putting models into production is never easy. It’s even more challenging for manufacturers due to the complexibility of the operations and the network of people, machinery, regulations and logistics involved.

  3. Leverage and adopt the latest and coolest packages and product innovation. Manufacturing is very process-driven and involves a great deal of human intervention. It is not easy to deploy new technologies right away and make them part of the day-to-day operations of manufacturing units compared to some other industries. Manufacturers can’t afford frequent process-breaks. Hence, business down-time resulting from software installation and upgrades or bug-fixing makes adopting the latest technologies challenging.

How does the Domino data science platform help manufacturers?

  • Reproducibility and collaboration. Domino enables data science teams to conduct collaborative, reproducible research. Data scientists gain automated reproducibility for code, data and environment configurations. They can discover, reproduce, and iterate on prior work, experiment with new techniques and make them repeatable for manufacturing decision making. Different team members can share, comment and collaborate on projects at every stage of the model development lifecycle.

  • Fast and flexible model deployment. With Domino, data scientists can deliver model products to manufacturing stakeholders in a variety of ways, including scheduled reports, Flask and Shiny apps, user-friendly web forms, enterprise grade batch or real-time APIs for integration into downstream systems. Domino helps data science managers understand the usage and performance of models by tracking engagement and key statistics over time.

  • Easy access to open and flexible tooling options. Domino provides the flexibility, agility, and scalability data science team needs and supports dynamic tooling environment, diverse skill sets and preferences. Data scientists can do exploratory data analysis and model development without configuring and using their own compute resources. They can spin up high-powered workspaces with a single click, without needing help from engineering.

Trusted throughout the manufacturing industry

Domino provides data scientists with easy access to the tools they prefer and the necessary compute power. Data scientists can collaborate using Domino, deploy and share models with manufacturing managers, and reproduce past experiments.