Data, beginning with its quality, processes and systems, must be the first consideration in any enterprise analysis or decision—technical or otherwise. Managers at every level of an organization have likely seen the ramifications when data goes awry. It often goes unnoticed until harm is done to products, marketing, engineering, production, distribution or services.
Business models can also take a hit based on data validity or the expectations on which they are based. This commands the attention of the executive suite, whose top-level managers are measured, in large part, by the soundness of their business models and the quality and speed of their associated execution.
The executive suite continually grapples with risks that come with implementing every new process-enabling technology, and allocating the necessary resources. These risks are the biggest for managers when they try to fathom a business solution's details and specifics without first grasping the solution's data and process enabling requirements, as well as its scope and possible impact on the business.
This brings us to product lifecycle management (PLM). As defined by CIMdata, PLM is a strategic business approach that spans the full lifecycle, from idea through life. As such there is an increasingly urgent need for the executive suite to align its value propositions to their business models. If that synchronization is missing, business models will not reflect marketplace realities and opportunities.
In this article, I address how PLM strategies and resulting implementations offer insights and remedies for these unsound business models.
‘In Sync’ is Far More Than ‘Linked’
Getting business models and the value propositions of PLM truly ‘in sync’ requires integration, not just ‘linking’ them. I find such linkages are rarely reliable. When integration is achieved at the enterprise level, the value propositions of PLM enhance and reinforce business models. This is because everyone in the organization can use all relevant data in the forms required, when required—even as business models evolve.
Being in sync means that top-level managers who formulate business models and those who manage data and processes within PLM solutions talk to each other in mutually intelligible ways. Getting in sync requires them to use the same verbiage, syntax, definitions, terminology and business jargon. Only in this way can they build a shared appreciation for each other. The reality is that they both have the same goal — maximizing the organization’s ability to deliver right-to-market each and every time. But what might differ is what they need from each other, which goes to the core of communication.
Getting in sync is vital because of the continuous evolution of the raw data, analyses and decisions that underlie every business model. These nonstop changes impact business models in unpredictable ways. The consequences of this continuous evolution make effective lifecycle process and data management an imperative for decisions and analyses. This is because data is an organization’s lifeblood — it is what runs the business and ensures business models succeed.
To achieve this, executive suites must begin with accepting PLM strategies, methodologies and enabling solutions as enterprise opportunities—not just something cobbled together in engineering. As a strategic business approach from concept through life, PLM is an enabler and driver of right-to-market, ensuring and maximizing the organization’s return on investment.
The Role of Data in PLM and Business Models
In many organizations, financial executives, for example, control data through bills of material (BOMs) which track the components and supplies a product or service requires and their costs. PLM solutions are commonly used to generate corresponding Bills of Information (BOIs) containing much more data than parts and their relationships.
Business models are strategic and need to evolve like everything else at the enterprise level. For example, revenue and profit projections (i.e., maximization of their return on investment) are the executive suite’s constructs of corporate aspirations. Business models zero in on viable markets with products and services to maximize profit. Business models also dictate resource allocation and pinpoint changing costs.
Business models have another data role: determining the contexts in which data (e.g., revenue and profit numbers and other key performance indicators) and insights and results are shared with the shareholders, board of directors, business partners, distributors, bankers, union leadership and the trade media. In publicly traded companies, business models also establish expectations of shareholders, financial analysts and regulators.
The dangers of incomplete or poorly defined business models are manifold and obvious.
The value propositions of PLM solutions rest on the reality that data in many forms (i.e., metadata, files, folders, algorithms, apps, images, videos and more) continually surges through the enterprise via three primary data structures:
- Information Technology (IT): primarily data processing.
- Engineering Technology (ET): especially product development data.
- Operational Technology (OT): which is production, delivery and service data.
Whether IT, ET or OT, data and tools are implemented on the enterprise’s computers, in the Cloud (e.g., Software as a Service [SaaS]), or in some hybrid arrangement that does not affect the validity of messages.
PLM’s Importance to Business Models
IT, ET and OT constantly change, as do their data repositories, which may be scattered throughout the extended enterprise. Data is gathered, secured, managed and kept current in business models in countless ways—usually with periodic updates when changes in underlying data are noticed.
To get past this, the executive suite needs an appreciation of PLM solutions, its capabilities, and the benefits from its enabled digital twins, digital threads (webs) and end-to-end connectivity. Even a basic grasp of these will show how PLM tools helps the enterprise achieve, enable and protect its data, and how easily data is mismanaged, mangled or lost.
The executive suite should insist that data managers at all levels incorporate these tools—and demand that business managers, analysts and decision-makers carefully watch and fine-tune their inputs to business models.
Once the executive suite is comfortable with PLM and its fundamental value propositions, they should also insist that managers regularly verify that no business model is floundering for lack of timely updates.
I do not believe the executive suite needs a deep understanding of the innerworkings of PLM solutions. Deeper dives should be left to project leaders, middle management and technical staff with the necessary skill sets, training and education.
Executive suites have many issues demanding their time and attention, but proven resources are available to get business models and value propositions in sync. To fix business models, there are a few worth noting:
- Data Governance and Configuration Management ensures that all of the enterprise’s data and all its digital systems are what they are purported to be. For example, that every change has been properly authorized and that all changes are tracked.
- The Theory of Constraints requires the identification and removal of bottlenecks leading to big gains that show up in all PLM processes, especially in those that support the end-to-end optimization of the product lifecycle.
PLM Value Propositions
To see how this works, let’s look at the most cited PLM solution value propositions:
- End-to-End Connectivity spans the enterprise and reaches deeply into both product lifecycle data repositories and their business-model integrations. Ideally, connectivity should start at product or service inception and reach at least the end of production (if not to the end of useful life, recycling and more). Ditto for the systems and applications that make up IT, ET and OT.
- Digital twins (or virtual twins) represent, as fully as practical, a product or service (or even a process) and every change made to its initial definition in engineering, production, operations and in the hands of users. This includes warranty claims, returns and regulatory compliance. Digital twins embed each product or service in its business model, which provides context for decisions and use, and facilitates the sharing of any changes.
- Digital thread is a web of data that describes the product, service or process, and all the decisions made through the lifecycle. It ties each Digital Twin to its physical, real-world product, service or process and coordinates their changes. A reliably synchronized business model reflects the extent and capabilities of its digital threads and digital twins.
To these, we must add digital transformation, which is both a parallel initiative and a key element of an enterprise’s data management enablement. Digital transformation renders formatted data in every kind of data repository into the 1s and 0s of raw data. While not always in sync with business models or PLM solutions’ value propositions, with every passing day digital transformation frees more data from cumbersome and outdated formats.
Why Synchronization is So Complicated
A big challenge to the synchronization of business models with PLM strategies and enabling solutions is what I call forcing factors. These are innovations that generate new data in new forms while impacting the relevance and quality of older data; entirely new business models emerge from forcing factors.
CIMdata’s list of forcing factors include:
- The growing electronics content and software in physical products.
- New manufacturing techniques and support processes.
- New materials that are lighter, stronger and greener.
- Mass customization, or the squeezing of every new technological feature into every new product or service offering.
- Shorter product lifecycles.
- Nonstop innovation.
- IIoT/IoT, which enables continuous manufacturing and marketplace feedback.
Another big challenge is the disruptions to data and business models triggered by new technologies. CIMdata’s list of new technologies and related approaches that fit this criteria include:
- Generative design.
- Additive manufacturing / 3D printing.
- Artificial intelligence and machine learning.
- Topology data analysis.
- Predictive analytics.
- Graphical databases.
- Agile software development.
- Virtual/augmented reality.
No doubt, every enterprise has its own list, and I have seen dozens. My point is that the upheavals of forcing factors, and new technologies and their related approaches should be understood by the executive suite, and that business leaders and decision-makers should be required to use them in updating and fine-tuning business models. All of this is critical when considering that a company is in business to create and use data that is ultimately transformed into its products and/or services.
Predicting the Future with PLM
Amid the usual business disruptions too familiar to list here, PLM solutions can be invaluable in predicting the impacts of what can be called’ business model shock.’
Business model shock is a sudden realization of trouble tied to unpredictable events such as a customer default, implosion of a key business partner, an internal organization upheaval, supply chain disruptions or a hostile takeover bid. To mitigate business model shocks, PLM strategies, methodologies, and enabling technologies must be supplemented by management vigilance.
This is why CIMdata believes that PLM should be regarded—and accepted—by executive leadership as the business model for data, the foundation upon which the business operates and ultimately maximizes its returns on investment. In turn, business models deepen and broaden the enterprise reach of PLM’s value propositions.