Digital Transformation Transformation strategies for the manufacturing industry: Think Big. Start Small. Act Fast.

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Digitalization offers the manufacturing industry opportunities for efficiency improvement, quality enhancement, and the development of new revenue sources, but it also requires innovative transformation approaches. Why a pragmatic approach is required now.

In his role as Vice President, Dr. Oliver Becker is responsible for the manufacturing industry customers at Arvato Systems.

Increasing efficiency, optimizing quality, greater customer orientation, a better ecological footprint, more flexible supply chains, counteracting the shortage of skilled workers, or tapping new sources of revenue: Digitization offers a cornucopia of potentials to the manufacturing industry. Moreover, the entire value chain is in motion. The way products are developed, manufactured, monetized, and shipped, how people work together and with new technologies, is undergoing an unstoppable and profound transformation - far beyond cost reductions through automation. Anyone who wants more than just the low-hanging fruit needs to think about future-proof transformation strategies.

The German manufacturing industry needs pioneers.

Despite a myriad of opportunities and already available technologies, many decision-makers are cautiously waiting and thus missing the chance to make their companies fit for the future. Technological boundaries are no longer holding back digital transformation, but rather the reluctance of businesses. The German manufacturing industry risks being left behind by nations demonstrating more courage and a faster implementation pace: According to Bitkom Research, almost two-thirds of German companies see themselves as digital transformation laggards in international comparison. Germany urgently needs a change in mentality, away from perfectionism towards pragmatism. "Done is better than perfect," especially because there is not just one correct solution in the complex context of digitalization.

Digitalization strategies between blueprint and practice.

Many decision-makers feel overwhelmed by the multitude of possibilities. What does Industry 4.0 mean for your own operation? Which digitalization components are relevant? What can, should, must be changed specifically, in what order and at what pace? How detailed and forward-looking must a transformation strategy be today? What budget is required and how can costly dead ends be avoided? So far, there are few inspiring examples and adaptable insights from practice - with the exception of the automotive industry. While many processes are already digitized and automated, they are not yet sufficiently digitally transformed. For example, simply equipping machines with sensors and collecting data is not enough. True transformation means that the digitized machine park is intelligently used to improve processes, save costs, produce better quality, fulfill customer wishes, efficiently employ an ever-scarcer manpower, and in the premium class of transformation, generate new growth.

In addition, the manufacturing industry is not a homogeneous sector. Depending on the company size, division, niche, and business model, the starting point and goals can vary greatly. Therefore, there is no perfect copy-paste concept. Nonetheless, in successful transformations, three phases can be identified that build upon each other and gradually increase the digital maturity level.

The phases of the digital transformation

In Phase 1, technological debts are repaid and a robust digital backbone is established as a reliable base: All basic systems, such as SAP and Microsoft, are integrated, interfaces standardized, and data flows defined. The IT landscape is gradually moved to the cloud - where it makes sense - and secured against internal and external risks with concepts for cyber security. Digital workplaces, from the office to production to the warehouse, make the company fit for efficient collaboration and modern working models.

In Phase 2, the infrastructure is used to optimize the core processes: Physical assets such as machines, workpieces, and components are connected to an internal data source using sensors. Digital twins enrich the sensor data with data from software systems such as ERP, MES, and PIM² to create virtual real-time models that, among other things, enable simulations. The goal is to identify levers for improving productivity, for example through predictive maintenance.

In Phase 3, companies start thinking beyond the core manufacturing processes: Production data is linked with internal company departments, such as sales, and used, for example, for the sustainability transformation. In the final expansion stage of digital transformation, the focus is finally on the monetization of data and the establishment of new business models to tap into new revenue streams, beyond the actual value creation - from the development of innovative services to cooperation with external partners in collaborative data ecosystems.

Application scenarios: A look into practice

Where the individual company starts and which measures are prioritized and combined is highly individual. Important are the scalability of the pilot projects and a technological infrastructure that provides room for visions and ideas that may only be realized later.

Practical Example 1: A mechanical engineering company wants to optimize the maintenance of its machinery and initially relies on condition monitoring, i.e., the continuous recording of the condition of a machine, for example. Sensors on the tool holder of machining machines capture vibrations, forces, and temperatures directly at the site of action and transmit them in real time to a central data platform. If defined tolerances are exceeded, the system reports that the tool needs to be replaced. The next level is predictive maintenance: Intelligent algorithms create precise diagnoses of when the next tool change can be expected, thus enabling production-friendly maintenance planning. In another expansion stage, additional sensors are retrofitted to measure energy consumption and optimize it using artificial intelligence.

Practical Example 2: A manufacturing company decides to introduce digital workplaces. The primary intention is to standardize the software products used, save licensing costs, and reduce update effort. The cloud as a new infrastructure base opens up numerous further opportunities for improvement, which are gradually implemented: For the shop floor, employees receive mobile devices to monitor machine status and thus save time on control rounds. The establishment of a central collaboration platform dissolves information silos and promotes the management of documents and the exchange of knowledge. The uniformly high security standard for all workplaces simplifies certifications and audits. The desire of many employees for hybrid working models can also be implemented in many areas in this way, which in the medium term increases both the number of applicants and employee retention. Paperless processes, lower energy consumption due to reduced office space, and CO₂ savings due to reduced commuting distances also contribute to the ecological footprint.

Practical Example 3: A tire manufacturer starts a pilot project for the Digital Twin in product development: With the help of the Digital Twin, simulations can be performed for a variety of profile variants, material mixtures and weather conditions, without building physical prototypes and testing them in real test environments. This saves money, shortens the time-to-market, and optimizes product quality. The manufacturing plant expands the pilot project, equips every tire with sensors, and collects various usage parameters. After the new tire series has been launched on the market, the quality assurance team found out from the sensor data that the wear in one production batch is significantly increased. Thanks to the digital twin, the tires affected by a faulty material delivery can be located and specifically recalled. In a further step of transformation, the tire manufacturer could enter into data trading and offer real data to manufacturers of digital assistance systems within an ecosystem, which forces act on the tires during steering maneuvers and how the tires behave in critical situations.

From small to large, but quickly.

The art of successful transformation strategies is to combine motivating quick successes with visionary foresight. Starting small, but thinking big from the beginning - and acting quickly. Because digitization will revolutionize the manufacturing industry. Today decides who will be among the winners of the change tomorrow.