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Spare Parts and Logistics

Within broader servitization initiatives, after-sales or “after-market” services constitute a strategic anchor for most manufacturing and engineering companies aiming at a mix of maximizing customer loyalty and retention, generating revenue streams and profits from the installed base and protecting the brand. Within these services spare parts play a crucial role: On the one hand they usually deliver the bulk of profits for suppliers and their potential is often perceived as low hanging fruit; On the other they can be a serious source of contention and dissatisfaction for customers, if they are perceived to be causes of excessive and very expensive downtimes and high, often unjustifiable costs -imposed by vendors from a position of strength.

Situations like these can often cause significant reputational damage and harm the business in a broader sense, often in non-obvious ways, one example being the fact that many customers buy critical spares at the time of purchase of the main equipment at prices significantly lower than after market prices, effectively depriving the vendor of future profits.

In spite of delivering relatively high profits by product standards (a wrong comparison) most spare parts businesses are underperforming. Many companies underinvest in spare parts capabilities, accepting the existing situation as good enough. They fail therefore to capitalize not only on a major profit opportunity, but also deprive themselves of competitive advantage.

Spare parts are a crucial link / intersection between products (source for improving product quality) and services and strong capabilities can play a major role in a company’s ability to design strong service offerings, improve profitability and enhance its competitive position.

We help our clients leverage spare parts for competitive advantage across the whole parts value chain. We work with them to design parts strategies and offerings and improve customer retention while enhancing profitability. Furthermore we help clients rationalize investment in the parts business, strengthen its organization, improve information flows and cooperation with product and service units, upgrade supply chain capabilities and logistics concepts, and optimize pricing.

Our portfolio includes:

  • Demand management and modelling
  • Pricing
  • Supply chain: Concepts, models, strategy and operations
    • Engineering, sourcing and procurement
    • Inventory management
    • Logistics and distribution; Field Service integration
    • Product Lifecycle Management
    • Repair operations
    • Management processes and systems
  • Marketing and Sales
    • Offerings (packages, insurance, stock pools)
    • Contracting
    • New channels to market

Results: We drive higher revenues, improved profitability and better customer satisfaction. Our clients experience on the operational side lower sourcing costs, better inventory turns, less obsolescence, lower logistics costs, higher service levels (improved delivery accuracy, shorter delivery times, …

Service in Industry

Deep dive into the industrial service business.

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Service in Industry

Deep dive into the industrial service business.

Join our community to receive analysis, insight, news and more.
We will never share your data

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:


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.

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