IDC Interview with InterSystems

IDC EUROPEAN MANUFACTURING EXECUTIVE DIGITAL FORUM 2020

We had the great pleasure to exchange views on the topic with the Keynote Speakers and Breakout Session Leaders of the IDC European Manufacturing Executive Digital Forum 2020. The following interview was conducted by Lorenzo Veronesi, Research Manager, Manufacturing Insights, IDC EMEA and Jochen Boldt, Director, Data Platforms, InterSystems.

Lorenzo Veronesi: AI has significant potential to support and drive a broad range of industrial applications and processes. From your perspective, what are some of the key use cases that are driven or enabled by AI?

Jochen Boldt: Operational inefficiencies plague many manufacturers, logistics providers, and suppliers, with the culprit often being disconnected processes that are siloed both in design and technology. These processes are often unaware of external data and workflows that could have a substantial impact on the business.

This is because many processes and the supporting technologies have been designed to work in silos. The systems generate data independently, then produce reports independently, and so it should be no surprise that decisions are made independently.

As well as silos, most organizations do not trust the data they are seeing within these processes and lack the technology and expertise to extract the intelligence they need. Without an overarching and accurate view of the business, it’s difficult for manufacturers to plan for growth and practically impossible to respond to any disruptions – in the last year alone, many suffered from large periods of downtime following the Covid-19 pandemic.

To address these shortcomings, businesses should consider new technologies that integrate disconnected existing processes and applications. Organizations are leveraging advances in data management technology, complimented by AI and machine learning (ML) models, and new API-driven development approaches to connect and automate processes that cross existing system boundaries in a non-disruptive manner. They can continue to take advantage of their existing legacy systems without requiring “rip and replace” by exposing, connecting, and orchestrating services and microservices. The result is a comprehensive and overarching perspective that enables frictionless interactions between functional areas and delivers greater flexibility and efficiency, and better insights led by AI.

Leveraging advanced analytics technologies like AI and machine learning can help in a number of use cases. Arguably, one of the most significant is demand management. In this scenario, AI can enable manufacturers to better predict and model demand to manage situations proactively, rather than just reacting to them. While some organizations focus on aggregated demand, those excelling in the field of AI have started to break down planning into more specific levels, for example, they may look at it on a regional basis or even down to individual customer requirements or product. By conducting more detailed and accurate forecasting processes, manufacturers can yield impactful improvements to overall performance and profitability.

Leveraging advanced analytics technologies like AI and machine learning also offers manufacturers the potential to automate predictable and repeatable situations. In this way, the smart decision is embedded into the process and the system takes care of exceptions – with or without human intervention. Not only is this likely to reduce incidents of errors, but it also frees up users to move away from the more mundane aspects of their roles, manage more pressing issues, and adding value to their organization. Sometimes these are tactical issues but other times they may involve more complicated issues, such as ordering of raw materials from external suppliers for example.

Other use cases include sales and operations planning (S&OP) processes, which AI and analytics can transform by bringing together stakeholders and data from across sales, production, procurement, and other departments. This cross-departmental infusion of data can make a big difference to businesses and help them make more informed decisions moving forward.

Lorenzo Veronesi: What benefits can be achieved from applying AI and how to they address thebarriers and challenges?

Jochen Boldt: Through data-capturing devices (including IoT), manufacturers have access to real-time data that provides valuable details regarding orders, shipments, location, and more. Unfortunately, this has also yielded more data than any human being (and many existing systems) can manage, making it difficult for companies to truly get the most out of these assets. As many manufacturers lack the technology to extract the intelligence they need from their data, technologies that make use of AI can help them with this. Applying AI also allows manufacturers to do more with their data and gain greater visibility of their end-to-end supply chain, including everything from inventory levels to production output. Other benefits include enabling them to identify and reduce operational inefficiencies, while incorporating analytics into automated processes can help them enhance decision making, prescriptively drive the business, and gain valuable diagnostic and predictive insights for strategic planning.

Without the visibility AI can help them achieve, manufacturers will continue to make less than optimal decisions and actions based on incomplete and inaccurate data.

Companies looking for a solution to these challenges should first and foremost ensure they can unify data silos and integrate and access all their data, including real-time event data. Though this can be challenging, it’s possible to simplify and streamline the process by implementing a data platform that augments and organization’s existing legacy technology assets to collect, harmonize, normalize, and integrate data and processes from across their organizations and their partner ecosystem.

Ultimately, any challenges manufacturers encounter in this journey will be outweighed by the significant benefits they will see from being able to obtain an accurate and trusted view of their enterprise, and apply AI to their data to guide decision making and deliver automated, prescriptive actions. One of the most significant impacts will be that the business becomes smarter and decisions are guided by data, instead of gut feel. AI can also empower manufacturers to evolve from using their data to report what has happened to predicting what is likely to happen and proactively performing intelligent data-driven actions based on the analyses. This will help them to keep up with changing demand and enable them to make data-driven decisions to move their organization forward.

Lorenzo Veronesi: What are your recommendations to manufacturing organizations about how to start engaging in data-driven and AI-enabled projects?

Jochen Boldt: To truly begin engaging in data-driven and AI-enabled projects, manufacturers must make enterprise data initiatives a key component of their digital transformation strategy.  But as they do so, one thing they need to remember is that they don’t need to remove their existing technology infrastructure and start again, but rather they can connect their existing systems and data, leveraging and preserving their previous investments in technology. They can then complement their existing infrastructure with new technologies that integrate disconnected processes and applications to progressively connect the gaps, eliminate blind spots, and provide their business with the information that it needs, generating quantifiable benefit with each sprint.

In today’s business context, agility and flexibility are more critical than ever, and an overarching architecture that connects data and silos across departments provides the visibility, intelligence, and automation that manufacturers crave. At the heart of this architecture is a modern data platform that can augment existing data and application infrastructure to enable better decision-making; create intelligent, streamlined, end-to-end processes; and deliver accurate real-time visibility for their mission-critical supply chain initiatives. Better data drives better insights that drive the business forward, and for most companies, that translates into growing revenue, managing margins, becoming more resilient, and keeping their customers happy.

Transitioning from islands of automation to streamlined end-to-end supply chain processes is a daunting task but, fortunately, it can be tackled in manageable steps. Here we should revert to the experts, many of whom recommend starting with small incremental steps that deliver meaningful business value. For example, identify a specific business process that can be improved by integration and automation and start there. From there, it’s just a case of moving one process at a time, reviewing the success of each implementation and learning as much as possible.

Lorenzo Veronesi: Thank you for the interview, Jochen.

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