5 Steps to Building A High Value Digital Ecosystem for Manufacturing

5 Steps to Building A High Value Digital Ecosystem

As has been written previously, McKinsey has been giving heavy emphasis to the need to forming a digital B2B ecosystem in MRO and manufacturing (see prior article). One of the points McKinsey stressed was the importance and value of creating a digital ecosystem that spans the entire supply chain. 

As an Aerospace & Defense (A&D) manufacturer, here are 5 steps you can take today to build a digital ecosystem for manufacturing and make this strategy a reality.


1. Have a Shift in Viewpoint

Instead of considering other OEMs, service providers, or competing suppliers as adversaries, industry experts now suggest that the winning strategy is through the creation of a network of peers. This collaborative framework can help to secure each other’s place in the market. The network then makes everyone stronger against external competitive threats, especially during a time of transformation. 

There are already several early examples of such ecosystems. The aviation sector has AVIATAR, a platform that can help airlines avoid delays and cancellations by using data to better organize and schedule maintenance. The oil and gas sector’s OpenEarth® Community is a shared software platform for accelerated technology innovation. Booksellers and publishers in Germany have developed Tolino as a joint digital reading ecosystem for e-readers.

2. Be Committed and Be “All In”

It will do you no good to be half-hearted. One business unit or division is not a digital B2B ecosystem. Commit to building an end-to-end platform. This requires a definition of all functions across the full extent of the value chain. Competitive animosity will need to be laid aside to create a community based on complementary strengths.

3. Generate Shared Value

The ecosystem must clearly lay out what each partner is to provide and gain in return. To be sustainable in the face of relentless competition, each party must benefit materially from cooperation. In turn, these relationships must result in high customer value. By teaming up effectively, the know-how of the sector can prevail against a new threat from low-cost competitors. Then, this knowledge can be used to develop an omnichannel infrastructure spanning customer self-service, field service, and contact centers to deliver superior value to your consumers.

4. Choose a Flexible, Modern Platform

Any successful digital ecosystem must be established on a technology framework that can work seamlessly in the cloud with standardized APIs. By its very nature, these ecosystems are comprised of many solution providers and vendors. A seamless infrastructure and easy-to-use user experience are essential elements. As well as the usual compute, storage and networking portions, it must have at its core existing expertise in ERP, MES, PLM, automation and other mission-critical systems.

5. Establish a Strong Management and Governance Framework

The success of your digital ecosystem/platform will be closely tied to how well change management is achieved. This means having the right agreements and understanding in place to assign owners, whether through licensing, joint ventures, or other vehicles. As the expression goes, the devil is in the details. 

In the long term, it may not matter who your partners are within this digital ecosystem. Five years from now there likely will be a few new “bedfellows” that were never anticipated. What matters most is that plans are put in place now for your systems (IT and OT) infrastructure to digitally interoperate both internally across functions and departments and externally with outside systems, partners and solution providers. The better Aerospace and Defense manufacturers are prepared, the better they will fare when the dust settles in the digital marketplace where a high volume of business will be conducted in the foreseeable future. 

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When Searching for Higher Quality, Start with Better Data

When Searching for Higher Quality, Start with Better Data

A paper by Greg Cline of Aberdeen Group, “IoT and Analytics,” makes a compelling case for prescriptive analytics. The author’s point is that the more data that is available, such as from Internet of Things (IIoT) devices or other organizational systems, will ultimately drive better analytics performance. Those operating in complex discrete manufacturing industries are aware of the importance of collecting the right data at the right time. This capability is so important, it has a huge impact on elevating quality across production and sustainment activities. It leads to improved decision support based on better data.

The Search for Better Data

Best-in-class firms are eagerly searching to gain access to more data. The rationale is simple. Greater, more accurate data delivered faster contributes to improved decision support, greater efficiency, and improvement to the bottom line. 

All the investment in Operational Technology (OT), Information Technology (IT), and Industrial IoT data have been part of this push to perform greater, in-depth analyses. What results is the ability to make better decisions about assets, products, processes, and operations. The benefits are numerous. As one example, by paying attention to parameters about machinery and component functionality, non-optimal operations can be detected and remedied before any unscheduled downtime occurs. 

MES is Key to the Digital Transition

Aberdeen Group noted that Manufacturing Execution Systems (MES) on the factory floor form an important part in the hunt for better data. This isn’t a big leap. MES systems have been collecting a wealth of information from the shop floor for many years. When MES is fully integrated with Quality and Sustainment operations, you have a flexible and reliable foundation to drive better data collection. This knowledge then becomes a critical part of your Enterprise Quality Management and Quality Assurance programs. 

Ironically, MES systems have been around for decades. Yet they continue to be a critical part of how to improve manufacturing efficiency and performance – now more than ever – as we become ever-more immersed in the digital age of manufacturing operations. 

The Quest for Greater Quality

Just as importantly, harnessing IT, OT, and IIoT data with advanced analytics can result in a sharp rise in product quality. When data is more readily available and aggregated together quickly in real-time, it can drive greater value. The conclusions reached by analytics applications make it possible to optimize production, quality and sustainment processes. What results is a reduction in waste and an increase in quality. As well as forming the cornerstone of excellent customer relationships, this has everything to do with cost containment and greater profitability. 

The systems of complex discrete manufacturers must be nimble enough to provide pre-configured products when applicable, yet also be capable of dealing with frequent Engineering Change Orders that are complex and often difficult to accurately track. Further, change must be managed quickly to maintain fast go-to-market objectives.  

Incorporating IT, OT and IIoT Data 

In order to capture the greatest success, analytics applications need to better utilize IT, OT, and IIoT data. This data can be collected from assets, components, sensors, and systems to develop a complete, real-time picture. Manufacturers require status updates on all critical assets. This includes visibility to streaming data about key performance indicators from a huge volume of data, and much more. 

When tethered to advanced analytics, asset performance and product quality improves. Alerts can trigger an equipment inspection sooner than previously considered. Contextualized alerts can offer suggestions to managers and shop floor supervisors on what to do next. Analytics can also be used to make suggestions on how to improve Maintenance, Repair, and Overhaul (MRO) operations. This knowledge can then optimize staffing schedules, MRO visits, and other workflows. 

Where to Begin?

In the field of MRO, it might be as simple as developing a system to first classify assets as over-maintained, under-maintained or well-maintained. The application of analytics continues to get better over time. Indicators of maintenance status on dashboards can then deliver real value to shop floor personnel. 

Operators could then shift setpoints to better mirror actual real-world results. They could then quickly understand if too much time was spent maintaining certain assets at the expense of other assets that might be in danger of failure. From this perspective could come a wealth of new insights that could then be applied to your overall MRO program – letting the cycle begin once again!

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7 Ways Manufacturing Execution System Data Drives Faster, Better Decision Making

Data Drives Better Decision Making

In the context of Industry 4.0, rapid access to and sharing of data — regardless of source, format, or location — is essential. Manufacturers need to consolidate heterogeneous data into actionable intelligence for resilient operational decision-making. This knowledge provides more consistent processes, a better-informed staff, and more open lines of communication along the value chain that leads to improved productivity, greater output, and higher customer satisfaction. 

Companies need to provide their employees with a real-time, collaborative decision-making environment that puts in-context, timely information in the hands of everyone who might need it, at that time. This need, which has driven much investment in the Industrial Internet of Things (IIoT), drives faster and better decision-making that supports changes in markets like increased service requirements, connected products, and mass customization.

Data Must be Trusted

Creating a data-driven decision-making environment isn’t accomplished by just creating a data lake. It requires more, including an understanding of a fundamental point: People need not only to receive data and information, not only to understand it, but they must trust it. So many times, when talking with decision-makers in the line of business, the IDC team has heard complaints about the fact that clients must rely on data coming from too many disparate silos and systems, resulting in inconsistencies and poor decision support. 

When you do not trust your report 100%, you must double-check and verify the data every single time. As such, it is critical to be able to share information, that is typically distributed and scattered across several systems and data warehouses, with the organization’s decision-makers to enable them to achieve real-time monitoring of business performance.

This is of utmost importance because when it comes to IT / OT integration, companies are gradually moving their organizational structures from segregated to coordinated to integrated. According to the most recent WW IDC research on this topic, in 85% of global organizations, IT oversees the operational processes as well. And more than 60% of companies are now in the process or have already set up an integrated organizational structure between IT and OT, where control systems and execution systems investment decisions are made through a shared services organization, a centre of excellence, or a corporate function. 

In this vision, ongoing business as usual collaboration exists between IT and operational technology and decision making about investment and priorities for operations – IT and OT are managed as a single unit. 

Implementing an MES is More than Just Getting Rid of Paper

Modern Manufacturing Execution Systems provide obvious value beyond the elimination of paper and streamlining of production processes. Further value can be attained from systems and data integration – MES can bring context to the information. MES fills the data gap with adapters and the capability to collect, add context, analyse, and immediately provide the derived insights. 

In other words, the MES brings value to the whole process – standardizing data, unifying semantics, defining metadata, and creating meaningful insights by associating the raw data to product, process and resource dimensions. 

Among the key business challenges and opportunities that a modern MES can address – where the value of the data that is provided is truly recognized – we identified:

  1. Harmonization of global manufacturing operations: creating a uniform environment for all plants to globally automate decision making.
  2. Manufacturing intelligence: gaining higher visibility of global manufacturing operations to have more centrally managed control over manufacturing capabilities across multiple sites. This allows the launching of value-added initiatives (e.g., “virtual” centers of excellence to reconcile manufacturing performance across the plant floor network and distribute best practices).
  3. Seamless integration with corporate business applications: integrating manufacturing operations processes and data with corporate-wide ERP system and other applications to drive greater consistency with process execution – as a foundation to becoming a digital enterprise.
  4. Connecting design and manufacturing operations: lowering documentation costs and empowering R&D efficiency through analysis of data associated to product structure.
  5. Improved fixed assets utilization: advancing maintenance management intelligence that is available to increase plant availability, reduce operational costs, and minimize capital expenditures.
  6. Compliance and environmental footprint reduction: reinforcing regulations and lessening manufacturing operations environmental footprint.
  7. Enabling rapidly changing, highly complex product lines: producing products either in markets where mass customization is replacing large production batches and limited product configurations or in industries with high complexity and low production volumes with better management and governance of production and process changes.

Key Takeaways 

Going forward, MES implementations will have to be considered key enablers of gathering and sharing data – including through the use of the IIoT – in order to drive new value. It is not possible to benefit from the plant automation without considering how this is information is going to be handed over to the MES layer   

At the same time, manufacturers need to realize that while there is certainly a massive value to the integration, at the same time Lean principles must not be forgotten. In other words, keep it simple and focus on the business benefits without losing track by trying to integrate “everything with everything else.” Instead, focus on high-value integrations, for example, from machines that do automated inspection and automated parts placement. 

It is now clear, that an MES project is not just a technology project. It is an operational improvement project enabled by technology that can become a key component of business transformation. New technology applied to old processes simply creates expensive “paper-on-glass” old processes. The increasing interdependencies and collaboration between IIoT and MES provides an opportunity to re-engineer and optimize processes for a data-rich factory ready for an Industry 4.0 future.

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