Management of Change vs Change of Management

By Des Evans MBE
Honorary Professor - Aston Business School

This article was first provided by Des to support his session at the Service Leaders Summit in March 2023

Until 2014 Des Evans was CEO of MAN Truck & Bus UK, seeing the business grow from £55 million toover £550 million over 20 years on the back of a services-led growth strategy. Since retiring, Des has stayed actively involved with the transport industry looking at green technologies such as hydrogen, as well as an Honorary Professor at Aston Business School as part of the Servitization practice, where his experiences of Service led business growth are well documented. Des has experienced two significant periods of uncertainty. In the late 90s, MAN faced a declining Truck market and a product that had become commoditised. It was this challenge that led to the transformation of their business model. They then had to weather the storms of the 2008 financial crisis and the resulting squeeze on operating margins.

Management of Change vs Change of Management

The rise in globalisation, combined with increased technological innovation, has revolutionised business. Increased ease of access to data and information and pressures brought about by the social media revolution has challenged not only traditional organisation structures, but also traditional management practices.

Change management or management of change is now top of many organisations agendas as they adapt to new market conditions and competitive threats from a global, connected world.

The level of change varies according to the maturity of organisations but the more mature operations have arguably the biggest challenge due to the culture, routines and current structures that are difficult to change if not approached correctly.

There are many theories with regard to how any change process is handled but what is clear is there is always resistance to radical change if the communication to key stakeholders is not managed with extreme care. Regardless of the many types of organisational change, the critical aspect is a company’s ability to win the buy-in of their organisation’s employees on the change.

Effectively managing organisational change is a four-step process:

1. Recognizing the changes in the broader business environment

2. Winning the support of the employees with the persuasiveness of the appropriate adjustments

3. Developing the necessary adjustments for company needs

4. Training employees on the appropriate changes

My experience has shown that an authoritative, top down approach is not the best way to bring about the necessary change.

Before any general communication to the wider stakeholder community is made i.e. customers, suppliers, staff members; it is important that first line management is informed and that their buy-in is obtained.

The first line management team need to act in unison and their management and leadership competence will be tested along with their commitment to any change.

It will be the responsibility of the first line management to be a consistent source of communication to all stakeholders. Without this first line commitment very little effective change will be made as staff members will soon see through any lack of resolve to the new change.

In addition to obtaining first line management commitment it is important to set clear, realistic goals that need to be agreed.

Goal agreement as opposed to enforcement is critical to any change implementation as no staff member or member of the management team will sign up for radical change based on unrealistic goals or targets that are imposed.

Once goal agreement and commitment has been achieved, it is important to devise a measurement system that will track performance and set out a sequence of steps that need to be performed in away that will appear logical to any doubters in the camp.

Initial change will come from a series of adjustments to current practice and routines.

Alongside these adjustments it will be necessary to ensure that all relevant training is undertaken and if necessary repeated. Change management is not something to be undertaken lightly. A state of organisational readiness needs to be assessed before any major change.

Management commitment and their ability to communicate will be crucial and at the end of the day, if management of change cannot beachieved, then change of management may needto be considered!

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Service Innovation for value-driven opportunities:

Facilitated by Professor Mairi McIntyre from the University of Warwick, the workshop explored service innovation processes that help us understand what makes our customers successful.

In particular, the Customer Value Iceberg principle goes beyond the typical Total Cost of Ownership view of the equipment world and explores how that equipment impacts the success of the business. It forces us to consider not only direct costs associated with usage of the equipment such but also indirect costs such as working capital and risks.

As an example, we looked at how MAN Truck UK used this method to develop services that went beyond the prevailing repairs, parts and maintenance to methods (through telematics and clever analytics) to monitor and improve the performance and  fuel consumption of their trucks. This approach helped grow their business by an order of magnitude over a number of years.

Mining Service Management Data to improve performance

We then took a deep dive into how Endress + Hauser have developed applications that can mine Service Management data to improve service performance:  

Thomas Fricke (Service Manager) and Enrico De Stasio (Head of Corporate Quality & Lean) facilitated a 3 hour discussion on their journey from idea to a real working application integrated into their Service processes. These were the key learning points that emerged:

Leadership

In 2018 the Senior leadership concluded that to stay competitive they needed to do far more to consolidate their global service data into a “data lake’ that could be used to improve their own service processes and bring more value to customers. As a company they had already seen the value of organising data as over the past 20 years for every new system they already had a “digital twin” which held electronically all the data for that system in an organised fashion. Initially, it was basic Bill of Material data, but has since grown in sophistication. So a good start but they needed to go further, and the leadership team committed resources to do this.

  • The first try: The project initially focused on collecting and organising data from its global service operations into a data lake.  This first phase required the development of infrastructure, processes and applications that could analyse service report data and turn it into actionable intelligence. The initial goal was to make internal processes more efficient, and so improve the customer experience. E+H looked for patterns in the reports of service engineers that could:
    • Be used to improve the performance of Service through processes and individuals
    • Be used by other groups such as engineering to improve and enhance product quality.
  • Outcome: Eventhough progress was made in many areas, nevertheless, even using advanced statistical methods, they could not extract or deliver the value they had hoped   for from the data. They needed to look at something different.
  • Leveraging AI technologies: The Endress+Hauser team knew they needed to look for patterns in large data sets. They had the knowledge that self-learning technologies that are frequently termed as AI, could potentially help solve this problem. They teamed up with a local university and created a project to develop a ‘Proof of Concept’. This helped the project gain traction as the potential of the application they had created started to emerge. It was not an easy journey and required “courage to trust the outcomes, see them fail and then learn from the process”. However after about 18 months they were able to integrate the application into their normal working processes where every day they scan the service reports from around the world in different languages to identify common patterns in product problems, or anomalies in the local service team activities. This information is fed back to the appropriate service teams for action. The application also acts as a central hub where anyone in the organisation can access and interrogate service report data to improve performance and develop new value propositions.
  • Improvement:  The project does not stop there. It is now embedded in the service operations and used as a basic tool for continuous improvement. In effect, this has shifted the whole organization to be more aware of the value of their data.

Utilizing AI in B2B services

Regarding AI, our task was to uncover some of the myths and benefits for service businesses and the first task was to agree on what we really mean by AI among the participants. It took time, but we discovered that there are really two interpretations which makes the term rather confusing. The first is a generic term used by visionaries and AI professionals to describe a world of intelligent machines and applications. Important at a social & macroeconomic level, but perhaps not so useful for business operations -at least at a practical level. The second is an umbrella term for a group of technologies that are good at finding patterns in large data sets (machine learning, neural networks, big data, computer vision), that can interface with human beings (Natural Language Processing) and that mimic human intelligence through being based on self-learning algorithms. Understanding this second definition and how these technologies can be used to overcome real business challenges is where the immediate value of AI sits for today’s businesses. It was also clear that the implication of integrating these technologies into business processes will require leaders to look at the change management challenges for their teams and customers.

To understand options for moving ahead at a practical level we first looked briefly at Husky through an interview with CIO Jean-Christophe Wiltz to CIOnet where we learned that i) real business needs should tailored drive technology implementation, and ii) that before getting to AI technologies, there is a need to build the appropriate infrastructure in terms of database and data collection, and, most importantly, the need to be prepared to continually adapt this infrastructure as the business needs change.