A new vehicle program can take up to five years, involving thousands of parts and hundreds of contributors in multiple disciplines and countries. New sources of information along the development process add to the project size and complexity. At the same time, legacy systems and a lack of relevant, accessible engineering data is hindering process optimization. Companies need a comprehensive view of the vehicle program’s evolution, leveraging real-time data analytics for enhanced decision-making.

Information Overload
Both automakers and suppliers need a clear understanding of the status of the projects and programs they manage — as they evolve in real-time. However, users face a significant hurdle in distributing and navigating the volumes of data required for business intelligence (BI) due to information overload from all the supporting IT systems.

The truth is that most big data deployments are built in silos to address specific business needs, so it is typical to see multiple CAD, PDM, PLM, ERP, CRM, content management and collaboration systems from completely different vendors within the same organization. This is often complicated by the fact that the automotive sector has seen a number of joint ventures and mergers in the past two decades. Because of the cost of previous IT investments, and the additional expense associated with implementing new enterprise solutions, maintaining legacy and other business critical systems will likely remain an ongoing trend.

But as globalization and vehicle complexity increases, it’s clear that enterprise software systems need to communicate with each other in today’s intricate engineering and manufacturing environments. If a company is to function efficiently, ERP data should not exist in isolation from CRM, PLM and other data. And, viewing multiple spreadsheets or individual dashboards full of key performance indicators (KPI) does little to help create a comprehensive view into the inter-dependencies between all the program pieces.

IT systems have typically broken down complex information into data structures, which are reported statistically and retrospectively. However, much of the information required to gain insight into managing a program is unstructured and therefore not accessible via a traditional BI approach.

Unstructured and non-quantitative information has to be processed differently, yet in tandem with the structured content, which requires strong semantic techniques that put the information in context. Only then is it possible to use this information for proactive decision making.

Transforming Data into Insight
Key performance indicators and reporting do play a critical role within product development processes. With so much going on, it is important to be able to visualize progress and summarize the health of the business, programs and projects in a number of ways. However, once a KPI flashes red or a graph takes a nosedive, hindsight can’t help.

While the KPI can tell you how you are doing, it can’t tell you what to do next. What is required is insight based upon real-time data analyics. The next action involves getting answers to questions, generating further related questions and including directions/stakeholders to drive the search for answers.

To gain an accurate view of the true current state requires dynamically uniting all of this data being generated from these disparate sources – both structured and unstructured.  The ability to retrieve, link and display this growing volume of data in a user-friendly format can deliver the insight needed to enable businesses to make informed decisions on demand. This ability helps to both avoid and solve problems, anticipate customer demand, and exceed customer expectations – and offers manufacturers a competitive advantage.

There are a range of technologies that can be used to facilitate data connectivity. But, the reality is that there no single data access point or a comprehensive data-warehouse featuring fully cleansed and interrelated data – but instead distributed silos, data errors, omissions, mismatches, multiple formats, multiple versions and duplications.

So, to accomplish a sophisticated level of data cleansing, integration and reporting is extremely challenging — it just isn’t practical to integrate all of the silos. Ideally, a solution would provide real-time analytics on top of the multiple information systems, and have the flexibility to draw and share content from multiple silos. And, it should also allow for the rapid introduction and manipulation of new information sources.

Bringing it All Together
Enabled by the ENOVIA PLM Analytics solution, Dassault Systèmes’ “Vehicle Program Intelligence” industry solution experience can sit of top of individual IT systems while remaining flexible. It draws content from multiple silos, brings together multi-format content by finding links between information types, and displays the results in customized dashboards based upon real-time data analytics. This enables program executives and managers to see the dynamic relationships between various types of information from the part level, to the product to engineering processes, project planning and operations.

Data proliferation is on the rise. But it’s not the volume of data; rather the ability to manage and exploit it that is required in order for companies to accelerate and enhance their business decisions.