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A Traditional Organization + a Modern Data Science Practice: The Oshkosh Story

Learn about the culture of data science at scale that Oshkosh Corporation has developed using modern software tools like Dataiku — a culture that has enabled significant cost savings and performance improvements over a variety of solutions for the business and their customers.

Founded over 100 years ago in the heart of the industrial Midwest, Oshkosh Corporation has become a leading global manufacturing organization that is now in the midst of an AI transformation, recognizing the importance of data and analytics to their continued longevity and success.

Oshkosh Corporation builds some of the world’s toughest specialty trucks and access equipment (e.g., concrete mixer trucks, military vehicles, fire trucks, and boom and scissor lifts). These heavy-duty machinery vehicles and service equipment are highly sophisticated and specialized as they are designed to move people and products around the world safely and effectively in any situation. Therefore, it comes as no surprise that Oshkosh requires equally advanced data science and machine learning (ML) capabilities. 

We spoke with Dr. Michael Schuh, Chief Data Scientist for Corporate Engineering at Oshkosh, to hear how his team is building the foundations of a data-driven culture within the organization. We discussed how they are using Dataiku to enable actionable insights from data through close collaboration with both business leaders and subject matter experts alike to drive efficiencies and generate new business through product innovation across the enterprise.

About Oshkosh Corporation

  • Oshkosh’s mission is to design and build innovative products that power global progress, such as specialty trucks and access equipment
  • Founded in Oshkosh, Wis. in 1917
  • 15,000+ team members working across five continents and 22 countries
  • 850+ active patented technologies and 147 facilities worldwide

new oshkosh logo

Blurring the Intersection Between Traditional Data Science and Engineering Practices

The Data Science and Analytics team in Corporate Engineering at Oshkosh has been built completely from the ground up since its genesis in 2017. As AI maturity and adoption increases around the organization, so does the size and scope of their team, which currently has over 10 full-time data workers, including a mix of data engineers, scientists, analysts, and a supporting cast of DevOps and MLOps engineers, and citizen (line of business) data scientists/analysts. 

Today, the team functions as a center of excellence for Oshkosh Engineering and its subsidiary teams.  While the subsidiaries operate mostly independently, the corporate team aims to bring synergy across the enterprise by providing big data infrastructure and services, software tools (like Dataiku) and educational upskilling/training activities, and advanced R&D support to guide new and innovative data solutions.

But it wasn’t always that way. At first, the team had just a few members who worked mostly on ad-hoc projects with messy data in unwieldy spreadsheets. Their analytics efforts were more siloed and scattered and there was very limited project accessibility and visibility from non-technical team members, such as business users and beginner analysts. Those variables, coupled with the fact that technical and non-technical stakeholders couldn’t collaborate on projects, including data understanding, day-to-day updates, and results/impact assessments, were preventing the team from scaling out their data science practice to the next level and gaining more widespread adoption.

To drive a sustainable and scalable data science and analytics practice in the modern era, they knew they needed an all-in-one solution that covered their key criteria: It had to be seamlessly collaborative between coders and non-coders, future-proof to be able to integrate new technologies, and governable to maintain security and trustworthy solutions. In the fall of 2020, this is where Dataiku came into the picture. 

Excelion Partners, a data science and analytics consulting organization that leverages Dataiku for its end-to-end analytical capabilities and collaboration-driven features, was critical in the ramp-up process. The Excelion team, also based in Wisconsin, provided generalized and tailored training, best practices, and helpful technical knowledge sharing on Dataiku. The relatively quick ramp-up period enabled the Oshkosh team members to start designing new projects and producing useful results within a matter of weeks.

For the corporate engineering team at Oshkosh, Dataiku has been transformative for many reasons. First up: governance. The team has audited project access and visibility, so users can easily see other teammates’ data flows and who is using what datasets for what models and dashboards. Accessibility is also customized with permissions management to give specific users/groups the ability to read or write project content to connected data sources, export and share datasets, and make general project modifications. Dataiku also helps the team maintain enterprise-level security with data governance features like documentation, task organization, change management, rollback, monitoring, and more.

Next, the team uses Dataiku to facilitate and enable project collaboration. At first, it used to be that the technical experts and data scientists had the code and communicated outward on it. Now, analysts, management, and customers can see exactly what the team is doing, which not only highlights the full spectrum of how Dataiku caters to different user profiles and responsibilities, but also has been pivotal to upskill and empower others across the business and build trust internally for current and future projects.

Dataiku is a full solution, helping us build a purpose-driven data practice in the modern era.”

Dr. Michael Schuh, Chief Data Scientist, Oshkosh Engineering

Primary Use Cases

1. Moving From Calendar-Based Intervals to Real-Time Predictive Maintenance

At Oshkosh Corporation, engineering teams use Dataiku to create predictive maintenance analytics that save their customers time and money by keeping vehicles on the job and out of the shop. A great example of this is a routine oil change. Similar to a car, most heavy-duty vehicles typically use calendar-based scheduled intervals (e.g., every six months), without considering the historical operations and current oil quality. Through vehicle telematics data and condition-based maintenance (CBM) methods, oil degradation can be monitored and modeled to accurately predict a remaining useful life and prescribe appropriate actions. By triggering an oil change only when needed, customers avoid unnecessary maintenance actions that result in inflated materials costs and reduced vehicle availability. 

The actionable insights produced through CBM analytics projects in Dataiku have shown tremendous potential to disrupt the current maintenance paradigm for vehicle fleets in many markets. Early pilot program results show a potential 2-3x improvement in required maintenance intervals, which translates to more than a 50% reduction in downtime and support costs over the life of the vehicle. If extrapolated over the fleets of similar Oshkosh vehicles in operation around the world, this could result in thousands of labor hours and millions of dollars saved for their customers.

There are additional benefits to these analytical solutions, too. A clear win is the increased environmental sustainability via reduced use of hazardous materials such as oil — literally saving thousands of barrels of oil in the previous example. A more subtle benefit comes from reducing additional vehicle faults and failures that are often accidentally induced by performing maintenance actions, which would mean additional rework, time, and costs. Lastly, indicators from CBM models, operationalized through Dataiku, keep operators from running their vehicles to critical failures while in use, preventing additional incidents that could be potentially dangerous and much more costly to repair. 

oshkosh vehicle

2. Enhancing Product Intelligence With Edge Computing

Another way that Oshkosh is building out a forward-thinking data science practice is through its product intelligence initiative. With edge computing, ML is embedded directly into the vehicle to generate advanced analytics and model insights locally in real time without the need for (and connection to) a centralized cloud. This is best seen in their commercial vehicle markets where new technologies offer competitive advantages for both the manufacturer and the owner/operator through value-added innovations and increased product efficiencies. 

For example, consider the world of concrete. An average business might have a fleet of vehicles and several cement plants around a general service area. Over a typical workday, a single driver/truck will be dispatched many times to various locations, and each delivery contains many routine operations, such as loading and pouring product, cleaning equipment, driving around, and so on. However, since every dispatch can vary greatly, the timing of these operations and total delivery are often hard to estimate ahead of time, which can make logistical fleet coordination difficult and inefficient. 

Using Dataiku, Oshkosh rapidly developed efficient edge solutions to automatically identify and report a vehicle’s current operational status. Not only does this give the dispatcher more time to utilize more accurate information for better decisions, it also frees the driver from tedious additional duties and possible safety distractions. 

Altogether, these small savings compound over time and have a multiplier effect when scaled over the entire fleet. A few minutes per trip becomes many hours over the fleet. This is significant time that directly benefits the bottom line, either through increased capacity and revenue potential or by way of decreased downtime and costs through more ability to service vehicles without impacting operations and further mitigating more costly breakdowns that can cause extended downtime and service interruptions in the future.

3. Freeing Up Manpower and Transforming Product-Specific Analytics

In addition to customer-facing data science and ML initiatives, the team also performs a variety of data work for internal customers, such as dashboarding and automated reporting. At the heart of these tasks is reliable data engineering and robust descriptive analytics — both of which are facilitated by low-code solutions in Dataiku that empower less experienced analysts to create high quality, reproducible, production-ready results. 

For example, Oshkosh must meet strict performance requirements when testing new vehicles, and the data is carefully analyzed internally and routinely audited for compliance. Before the team’s involvement, this was an extremely manual process involving multiple data transfers between disparate systems and siloed workers. 

By creating a single unified workflow in a Dataiku project, all parts of the process were linked together through an observable data lineage with embedded documentation that made collaboration and process improvement efficient and effective. This included:

  • Manual data prep (cleaning, enrichment, etc.) was replaced with visual prep recipes, and Excel/VBA macros were recoded as Python script recipes. 
  • The deliverable report to management (Excel charts in PowerPoint slides) was automated using Python to seamlessly generate the charts and slides exactly as expected. 
  • The data product was then synced to their Hadoop environment where self-serve BI dashboards automatically update for end users to quickly identify and investigate issues and trends that may have otherwise gone unnoticed. 

In all, this entire process that used to take hours can now be done automatically in a matter of minutes whenever the customer desires.

This project highlights some of the major recurring strengths of Dataiku for Oshkosh: transparency, repeatability, and maintainability across the team — all of which minimize single-point failures and promote collaboration and innovation. These types of projects are relatively low-hanging fruit across the organization, but their benefits are truly game-changing. 

In just the first year, these operationalized solutions are already saving tens of hours of employee time per week that can be better spent on more meaningful parts of their job. In addition, these are excellent opportunities to promote a culture of data literacy and data stewardship, as well as identify potential citizen analysts from within that already have strong domain expertise and a vested interest in improving their work. Dataiku allows the team to quickly onboard new data workers, leverage existing projects/results, and scale data-driven analytics across the enterprise.

You need collaboration to do data science effectively in the first place. Dataiku facilitates and enables collaboration much better than anything else I’ve seen. It used to just be technical experts and data scientists, but now analysts and management can be involved side-by-side in the process.”

Dr. Michael Schuh, Chief Data Scientist, Oshkosh Engineering

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Looking Ahead

By leveraging Dataiku, Oshkosh has seen a significant increase in ROI of analytics solutions through new product sales, enhanced software services, and cutting-edge R&D support. While many specifics are confidential, the future of data science and ML is bright for Oshkosh Corporation and its customers. There are major initiatives to modernize military and defense around the world and the novel CBM solutions created by Oshkosh lay the foundations for these future efforts. The commercial market is also extremely interested in product intelligence and Oshkosh is leading the market by delivering built-in telematics and analytics to support the changing needs of modern, data-driven business.

Looking ahead, Oshkosh Corporation is always seeking potential new business. They were recently awarded several major government contracts, and the data team is excited to scale and grow their solutions to new markets and big opportunities. This means more growth for engineering and product-focused teams while bringing more non-technical contributors into the fold and evangelizing data-centric processes to additional business units. Their journey continues to enhance their AI maturity by identifying innovative projects that will have a lasting impact on the business for years to come.

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