Don’t embark on an arduous digital transformation journey unless you have very good reasons to do so. Dangers are lurking everywhere, and the failure rate is high.
That said, history shows that manufacturers who don’t transform will either go out of business or fall behind competition; and on the other hand, the value potential is quite substantial.
In other words, digital transformation has become a necessity for manufacturers aiming to remain competitive and relevant. However, many manufacturers still struggle to navigate the complexities of this transformation. In this article, we will explore five of the most common reasons why manufacturers face challenges in their digital transformation journey and provide insights on how to overcome these obstacles.
1. Lack of clear strategy
One of the primary reasons why manufacturers often falter in their digital transformation efforts is the absence of a well-defined strategy and clear objectives. A sense of panic, driven by the sight of competitors making strides in digital transformation, can cause many to spread their resources too thin across multiple simultaneous projects, or jump ahead to acquire equipment that their IT department is not prepared to help optimize.
Your strategy must be founded on clear business goals. Without well-defined objectives, digital transformation can become an aimless endeavor, leading to wasted resources and frustration.
Lighthouse projects
Identify specific areas where digital technologies can drive efficiency, reduce costs, and enhance product quality. Other areas can follow and benefit from the lessons learned in previous projects.
Quantifiable outcomes are an absolute necessity. Define a lighthouse project that promises significant gains or addresses a pressing challenge.
For example, if supplier onboarding has become a bottleneck hindering your time-to-market, set a key performance indicator (KPI) to reduce onboarding time. Analyze the causes of delays and set a target of success. If the project is launched with a clear baseline and a KPI, it’s easy to follow progress as you bring down the onboarding time.
Furthermore, don't forget to set goals for the end-user experience. Success is not guaranteed if your new technology doesn't align with your business goals.
How NOT to do (case)
A large manufacturer of configurable devices for the utility industry wanted to implement online ordering to reduce the workload on internal sales. However, the project failed because the technical product information, residing in the ERP and now exposed in the ecommerce application, was far from user-friendly. The self-service portal, while technically advanced, created confusion among customers, resulting in incorrect orders, product returns, and increased customer support calls, which was exactly the opposite of the business goal. After 18 months, the portal was closed.
Lesson: Digital transformation requires alignment between those delivering IT solutions and the end-users, including internal line managers who need data and expect business impact. People from the business side should be giving their input not just before project launch but already in the design phase.
INFOGRAPHIC
How mature is your organization’s data?
Assess your data maturity and accelerate your digital transformation journey.
2. Resistance to change
Resistance to change can come from various sources, including employees, managers, or board members. Digital transformation often clashes with organizational inertia and necessitates a cultural shift.
Most employees prefer the status quo, and personal goals may not align with the company's objectives. It's crucial to engage your employees by demonstrating how the change can benefit them personally. Remember the saying: Everybody wants change, but nobody wants to change.
Bad experiences from failed implementations in the past can also cause resistance towards new projects. As mentioned above, the company may have acquired new technology prematurely or neglected stakeholder management. However, failed implementations should rather serve as learning points.
How to do (case)
Leadership communication is crucial in preparing for change and fostering a collaborative environment.
A former CEO of a manufacturing company demanded a 20% efficiency increase from factory workers within five years. Initially met with resistance (because everyone rightfully claimed they were already working at the top of their ability), he showed them that they had in fact achieved a 20% efficiency increase in the past five years through the adoption of new methods and technology. He emphasized their pivotal role in the next phase of digital transformation.
Be transparent about new projects and prioritize people transformation alongside digital transformation. Hire or upskill individuals who can facilitate the transformation and create a data-savvy culture. Invest in training and education programs to equip the workforce with the necessary digital skills. Encourage a culture of innovation where employees feel empowered to suggest and implement digital solutions.
3. Legacy systems and siloed infrastructure
Many manufacturing facilities operate with outdated legacy systems and a siloed infrastructure. These systems have evolved organically to solve specific problems and are maintained by local departments responsible for particular data processes. This was a naturally evolvement of the digitization age, or the third industrial revolution. But as manufacturing is advancing to Industry 4.0, adopting edge-computing, machine learning and Internet of Things, your applications need to be connected. However, silos make it difficult to share data due to the lack of common standards and an inflexible IT infrastructure that does not allow for integrations.
The underlying problem behind an underperforming data foundation is a lack of a data governance policy. So, before investing in new technologies, there is some essential paperwork to be done first:
- Clearly define the scope and purpose of the data policy to support your digital transformation project, explaining which business goals it supports and which data it encompasses.
- Detail how data is collected, including data sources and relevant collection procedures. Address data quality, integrity, and security. Consider data provided by suppliers or third parties, validation processes, and data quality thresholds.
- Define how data will be used and incorporated into decision-making processes. Determine who needs access, who owns the data, and who requires it for operational purposes.
- Explain data processing, including enrichment, validation, and approval processes, ensuring data reaches the desired level of quality. Define necessary business rules and gates.
- Describe how data will be shared, both internally and externally, including format requirements set by internal and external systems.
- Identify legacy systems that are not worth retrofitting. Accumulating technical debt may hinder your ability to scale. Determine which components can be upgraded or replaced to align with digital transformation goals.
Integration is key, so develop strategies to seamlessly integrate new technologies with existing systems or sunset those that can only be integrated at a high cost. Invest in scalable solutions that can adapt as your business grows and evolves.
And make sure you don't feed bad data into your new system.
4. Data and analytics deficiency
A lack of data-driven decision-making can hinder your ability to optimize processes, predict maintenance needs, and improve overall efficiency. This underscores the importance of data governance. Trustworthy data is essential for running a data-driven business. Data must be unified, shareable, and fit for purpose, delivering the right data to the right systems.
Secure data quality
Collecting and storing data is not enough if you cannot trust its validity. Therefore, you need to secure a framework of information before analyzing data. For example, when analyzing supplier performance, it is crucial to ensure that each supplier is represented accurately in your system and that the specific supplier does not have duplicates, either in the same system under different names or in different systems, e.g., in different countries.
360-degree insight with multidomain capabilities
The ability to cross-reference between data domains is equally important. For instance, a change in supplier information can affect a certain product’s or a product category’s compliance status. Gaining insight into different data domains in conjunction can facilitate analytics and data-driven decision making. Governance of product, location and supplier data together can provide you with important insights on who delivers what products to which distributor locations. Governing product and customer data together can help you manage eligibility and customization.
The same applies to IoT implementations. To make IoT effective, you need reliable asset data, as asset data provides context for analytics. IoT data, or time series data, is volatile, while asset data has low volatility. Therefore, you need to secure the foundation of what you are collecting data about before collecting IoT data.
Information collected from assets, such as equipment and vehicles, can support near real-time analytics for performance monitoring and predictive and preventative maintenance planning.
For example, the car manufacturer SGMW uses a single platform to collect all necessary information, serving as a central hub for all automotive parts, factory, BOM, logistic, assembly and supplier data. This approach allows for accurate monitoring of data quality, performance, and product lifecycle processes throughout the supply chain, from suppliers to individual assembly line workstations.
EBOOK
Get the Roadmap for Digital Transformation
5. Cybersecurity concerns
As manufacturers embrace digitalization, they expose themselves to cybersecurity risks, especially within a complex supply chain network. The interconnected nature of digital systems creates vulnerabilities that must be proactively addressed. Manufacturers also act as suppliers and need to assure their customers that they are not susceptible to supply chain attacks.
Common pitfalls include:
- Being overly risk-averse, which can stifle innovation. Combining increased cybersecurity awareness with robust data systems that carefully monitor data streams is essential.
- Insufficient employee training to recognize and mitigate cybersecurity threats. Instead, foster a culture of vigilance.
- Lack of a central office to establish guidelines and maintain training across the enterprise.
- Neglecting to ask vendors tough questions about their data security, such as do they recognize regulations based on location to ensure data security laws are followed per region?
The question is not whether to embark on digital transformation or not, but how to do it right and avoid the pitfalls that cause many projects to fall short of their goals.
