To be greater than the sum of your parts, you must first sum your parts.
The success of any organization is determined by how effectively it combines all the different things it does. For an engineering company, this might include product design and development, production and delivery processes, supply chains for process inputs and distribution networks, sales and marketing, finance and human resources. It involves physical and non-physical processes and equipment and the people who make them work.
All these elements are combined according to an organization’s operating model: a system of principles and practices that govern process design, people management, and an organization’s culture and performance.
Organizations often don’t explicitly define their operating model. Instead, they evolve over time and establish the basis for the decisions the organization makes, large or small. But there are several well-defined models that have been embraced for different reasons—and understanding where your organization lies on the spectrum is crucial to succeed in digital transformation.
The three main operating models
Until relatively recently, the main characteristics of the operating models used by most organizations in manufacturing and elsewhere were the same—only one model existed which everyone used. Often referred to as Scientific Management (and also Fordism or Taylorism) it sought to apply “science” to the management of organizations.
Emerging in the 1920s, this model features careful definition and control of working practices, minimization of costs, division of work between many low-skilled workers and a small number of managers and professionals, and minimal employee participation in improvement or innovation.
Radar charts are a fantastic tool for illustrating the main characteristics of an operating model. This chart illustrates the priorities of Scientific Management:
In 1990 researchers Womack, Jones and Roos studied the operating model that was used in Japan, one which had brought success to the country’s manufacturing sector since the end of the Second World War. The result was “The Machine That Changed the World,” a book which introduced the Lean operating model to the wider world.
Compared to Scientific Management, the Lean model featured higher levels of employee skill and flexibility, lower levels of inventory, greater focus on the customer, a new “just in time” approach to workflow and employee participation in continuous improvement and innovation.
Many organizations outside of Japan have made efforts to implement the Lean model, in all industrial sectors, with varying degrees of success. The model’s emergence introduced organizations to the notion that there were choices to be made about the model they would use, which would significantly influence their management activity and their organizational performance. Operating models were now a competitive factor.
The third main operating model is Agile. This model emerged as globalization and technological change contributed to intensified competition for many organizations. They sought to make changes more quickly to the volumes they produced, the products and services they offered and the processes they used.
The Agile model emphasizes processes, equipment and work practices that enable flexibility, creativity and innovation.
While explicit commitment to a specific operating model is not always present, most organizations are oriented more towards one of these three models, and all are in common use. In recent years a fourth model has been discussed: a Lean/Agile hybrid, which some organizations have pursued.
The radar charts show that these operating system models differ significantly from each other. Practices that are essential for the success of one model may be fatal for another. For example, empowering employees is essential for Lean and Agile but very undesirable in Scientific Management.
The technology implications of your operating model
Operating models are extremely important, but often overlooked, in digital transformation initiatives. Consider the following hypothetical example.
Amir Kaur is the CEO of a consumer electronics manufacturing company. Competition in his industry is intense as competitors battle to develop and introduce new products quickly and produce and deliver them cost effectively. Information technology has the potential to positively impact most areas of the company.
Amir’s company uses a Lean operating model. They emphasize low inventories, pull systems of workflow, and have empowered employees who participate regularly in continuous improvement activities. At his weekly executive team meeting, Amir asked his direct reports for their ideas on how the company should apply technology over the next couple of years—what should it spend money on today?
The responses covered a range of areas. For example, one suggestion was that Operations should install an automated warehouse system that would enable more inventory to be effectively managed, reducing stock outs. Human Resources suggested tools that would more closely monitor employee behavior, while others argued that the product development process could be accelerated with more advanced online collaboration tools.
Amir considered all the suggestions and was concerned that while some were consistent with their Lean operating model, others would clash with it and damage company performance. The automated warehouse would increase inventories and eliminate many benefits of Lean. Technologies that closely monitored employees would negatively impact empowerment and motivation. On the other hand, collaboration tools in product development could enable better understanding of customers and more rapid integration of knowledge within the company and throughout the supply chain—consistent with the Lean approach.
As technology has been introduced in organizations today, their impact on and consistency with operating models has often not been considered. This is an important cause of digital transformation failure. A technology application, which may have benefits in one part of the organization and on one performance objective, could conflict with the corporate operating model and produce negative consequences overall.
When all organizations used the same Scientific Management operating model, new technologies were usually (though not always) applied in ways which were consistent with that model. Objectives were straightforward: reduce costs, increase output and simplify work. As new operating models became more widely understood, performance objectives multiplied and reflected their varying emphasis. It has therefore become very important that the introduction of new technology be consistent with the model being used. Often it is not.
Digital transformation is accelerating, and this makes operating model consistency increasingly important. Multiple technology changes over a short time, if inconsistent with the operating model, have the potential to rapidly create fatal chaos.
Further, as organizations consider their investment in technology, they should be considering the operating model that will be appropriate for their organization in the future. The model today may not enable them to compete effectively in the future. Implementing technology in an outdated model today will make it harder to change later. For some, this will mean adopting a new model while transforming their technology.