The Business Case for a Manufacturing Mindset Shift

The Business Case for a Manufacturing Mindset Shift
The Business Case for a Manufacturing Mindset Shift

There’s a common thread to how manufacturing companies are successfully meeting the challenges of the current pandemic and positioning themselves to thrive in the “next normal.”
An August 2020 study from Oxford Economics finds that an elite class of high-achieving manufacturers share a business approach and mindset known as systems thinking. Systems thinking, the study explains, is “a way of seeing the entire web of relationships within and beyond the organizational firewall as a unified entity that operates smoothly, dynamically, and as part of a cohesive strategy.”
That approach evidently translates into results. “Our findings suggest that there is a meaningful connection between systems thinking and business outcomes,” says the analysis, which is based on survey responses from 3,000 senior executives across 10 industries, including 300 from the industrial manufacturing sector. When systems thinking is practically applied in an industrial manufacturing context, it’s about companies treating “all their relationships across internal functions, partners, and suppliers as elements of a unified entity that operates within an overarching strategy. That means using technology to interconnect parts of the business that have traditionally been siloed…then applying those insights to respond dynamically to changes in the market.”
How to get there? And how exactly can an embrace of systems thinking (supported by Industry 4.0 technologies) position a manufacturer to thrive in the next normal? Let’s explore the possibilities.

On the factory floor

Today’s volatile economic environment has dialed up the pressure on manufacturers to find new ways to reduce operational costs and increase productivity. Automation—such as through the use of robots and cobots (human-robot collaboration on processes and tasks)—is among the Industry 4.0 approaches that can enable them to do so.
On the factory floor, the cobot approach, using collaborative robots – intended for direct human robot interaction, appears highly promising. A 2014 study found that teams made of humans and robots collaborating efficiently are more productive than teams made of either humans or robots alone.
With the pandemic having exposed the risk of relying too heavily on far-flung suppliers, manufacturers are moving to reshore production. With its ability to reduce production costs, automation helps to compensate for the higher labor costs associated with reshoring.
Automation represents one facet of the intelligent factory that manufacturers today view as integral to their success. In the Oxford Economics survey, executives identified “operating smart factories” as one of the top two priorities most critical to the success of their business. The digital twin—a digital representation of a physical asset, system or product—can play a key role in a smart factory, enabling the system thinking that leads to greater efficiency. The digital twin allows manufacturers to digitally capture as-designed, as-built and as-operated views of a system, giving them the means to use flexible and adaptive production processes to dynamically react to different demands, setups and workflows.

(Source: SAP)
What’s more, when smart factories are used to build smart industrial products and equipment, data generated from connected factory assets as well as the connected end product and equipment creates a loop for the continuous optimization of production and product performance in the real world.
The cumulative impact of digital twin, automation, IoT-connected assets and other Industry 4.0 capabilities is to deliver substantial and scalable improvements in throughput, efficiency and the capacity to innovate—exactly what industrial manufacturers need to answer near- and long-term competitive challenges.

Supply unchained

The COVID-19 crisis exposed vulnerabilities and risks in even the most rigorously stress-tested supply chains. The Oxford Economics analysis sums up how Industry 4.0-enabled systems thinking can help to address those risks: “Data-sharing and traceability have become even more important in the wake of the pandemic,” it posits, “as the stress put on supply chains requires greater understanding of the availability of specific parts on a daily or even hourly basis—and the ability to respond accordingly.”
Pandemic-related disruptions appear to be hastening the manufacturing sector’s move away from the linear supply chain construct, to a network-focused approach that emphasizes flexibility and resiliency. To maintain a grip on planning and forecasting, and to preserve the overall integrity of supply, manufacturers must have the ability to readily integrate and digitally engage with multiple tiers of suppliers in real time.
The emphasis going forward will be on building digitally connected supply networks in which all the constituents are sharing and making decisions based on the same data. Using a common digital platform, members of the network gain critical real-time visibility into customer demand, supplier capacity and inventory, and transportation logistics so they can collaboratively work through potential disruptions and identify alternative supply sources and logistical pathways, using predictive tools to show them the most efficient options under various contingencies.
As more manufacturers prioritize resilience, reliability and risk-reduction in their supply networks, they need the ability to quickly onboard suppliers, and to plan on a tighter cycle, synthesizing data from across their network to respond to rapidly changing market signals. To avert disruptions and prioritize production, they also need to keep close tabs on the demand side by maintaining real-time communications with customers (about order timing and volumes, etc.). During supply constraints, it’s critical that manufacturers have the ability to segment customers based on lifetime value and other criteria to prioritize and adjust order allocation, thus protecting their most valuable segments and customers.


Now more than ever, customers want their industrial equipment suppliers to deliver positive outcomes and experiences, not just products. Germany’s KUKA AG, a global supplier of automation solutions for a range of industries, is among a wave of equipment manufacturers that are creating customer-friendly, outcome-based business models and revenue streams around Industry 4.0 capabilities. Working through a cross-industry group known as the Open Industry 4.0 Alliance, KUKA is using a manufacturer-independent digital platform to share performance analytics and other valuable data from the IoT sensor-equipped assets it produces with its customers. This newfound level of asset intelligence also is enabling the company to develop new asset-as-a-service and pay-per-use models, so customers can hand responsibility for managing the assets to KUKA if they choose. Here’s an example of how systems thinking is helping a manufacturer to recast itself as a true partner with its customers.

Oftentimes, new business models like these revolve around customized products. Armed with robust experience management tools, a manufacturer can gain data-driven insight into customer expectations and intentions—insight that they then can connect to their product development efforts. With intelligent, connected factory assets supported by strong modeling and analytics capabilities, they can develop cost-effective pathways to mass customization.

Manufacturers can use these same smart factory capabilities to build the sustainable products that customers increasingly demand, showing them efficient pathways for developing products around parameters like recyclability, lower carbon footprint, durability and reuse. With strong digital track-and-trace capabilities, they can verify authenticity and sustainability, from raw material, through production, to finished goods.

About The Author

Ankit Sharma is an industry solution manager with SAP, where he focuses on supporting industry portfolio strategy and driving technology-led innovations for manufacturers in the industrial machinery & components space. Currently he is driving outcome-based business models with industrial machinery companies.

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