How to see the forest for the trees

Pulling Visibility into Focus

2022年5月10日 4 最小阅读

How to start driving value from supply chain data

Automotive companies were tentatively hoping that 2022 would see a return to normal supply chain operations. But the ‘new normal’ remains elusive and fiercely debated. The need for enhanced visibility deep into supply-chains is taken as read, but what you see, and how you turn it into actionable insight is far less well understood. Most automotive businesses do not suffer from a lack of data – what they struggle with is the capacity to see the wood for the trees. Manufacturers need to turn granular data from thousands of sources into clear indicators that inform operational decision-making in real-time as well as provide long-term strategic insights.

Visibility into supply chains is crucial. Knowing which factory produces which part and which routes that part travels to get to your assembly line is critical to mitigate and manage supply-chain risk. But with thousands of inputs creating millions of data points it is easy to quickly drown in a sea of data. To avoid this there are some practical steps automotive businesses can take to successfully harness the data they have.

Integrated, granular data that flows freely up and down supply chains and across the business is the essential pre-requisite. Digital threads connect every individual component to every specific vehicle being built, this provides the foundation for analytics to support tactical decision making about those specific components and vehicles. By weaving these threads together, we can elevate our perspective to bring strategic insights focussing on how to improve the overall eco-system. Both of these perspectives are highly valuable to the business, but crucially they can be formed by leveraging the same data, just though different lenses. 

Working with automotive leaders, we typically combine two approaches that help them sharpen focus to create value from supply-chain data. The first is to combine subsets of existing data and analyse them for insights that can identify new ways to improve processes. Data that sits unintegrated in siloed repositories has some intrinsic value, but that value is multiplied when it is combined with another data set. Building new use cases and uncovering new value from data you already have quickly illuminates new opportunities. For example, comparing pricing and order-volume data across two or more sites can spot anomalies and opportunities for better planning and negotiations.

The second approach is to take a specific pain point and then identify what data exists, and what’s needed to examine the issue from multiple perspectives and define new approaches that can help overcome it. For example, working with a leading luxury automotive manufacturer we were able to find, integrate and analyse different data sets to highlight and mitigate the risk associated with specific shipping containers falling behind schedule. Combining current shipping status, factory production rates, on-site stock and other key data sets, predictive analytics ascertain if a specific delay poses a risk to production. Knowing as soon as possible about delays and the impact on production determines the most appropriate and cost-effective action to keep cars rolling off the assembly line.

If the visibility through data is managed correctly it not only delivers ‘real time’ operational benefits and inform tactical decisions, but also delivers strategic insight. For example, consider that late container; looking at the data over a period of time was it a one-off incident, or does analysis show a pattern of delays? If so, does that relate to the route, the shipping provider, specific days of the week or months of the year? Identifying these patterns and understanding true root cause allows automotive companies to implement strategic change such as network redesign, near-shoring vs off-shoring or multiple sourcing vs increased inventory. Cost, service level, resilience and agility can all be factored in. Those strategic changes, underpinned by the visibility the data fabric provides, will reduce the number of tactical incidents to manage and thus improve overall supply chain efficiency.  

Useful visibility requires consistent, shared understanding and trust in the data itself and in its cadence, provenance and timeliness. Real-time data will be needed for some decisions, but not all. However, regularity of data; knowing the cadence at which it updates, is always important so that all parties know they have the most up-to-date information, and when it will refresh in order to have confidence in their decisions. And it goes without saying that the source, quality and completeness of data must be trustable.

This type of visibility is the foundation for the automation of decision making that is needed for the agility, flexibility and responsiveness to thrive in today’s volatile environments. Future supply chains will become self-healing if they can automate decisions based on shared, integrated, trusted and granular data. The increasing complexity of these global systems mean that consequences of any individual decision can no longer be predicted by humans using spreadsheets or bespoke siloed systems. Only a digital fabric that connects multitudes of discrete data points in ways that highlight patterns and drive automated decisions, can hope to manage this complexity. Visibility today means helping humans see the wood from the trees – tomorrow it will enable an auditable, provable, automated decision-making process where the humans focus on optimising the business rules to deliver the desired outcome, not on individual events.

Teradata is working with automotive businesses at all points on the supply chain to help bring this focus to their data. Working with us they are already using existing data to spot current opportunities and overcome immediate challenges, as well as building the platforms and analytic approaches that support next generation analytics and automated decisioning. If you’d like to hear more about how we are pulling supply chain visibility into focus for these companies, or how Teradata can help deploy data for better supply-chain performance, please get in touch with our Automotive Industry consultants. 

关于我们 Robert Widell

Sr. Industry Consultant with a strong focus on the automotive industry. Robert has been with Teradata since August 2017 and joined from the Volvo Group where he spent 13 years in a number of roles, predominantly within Product Strategy and Planning for Trucks, Powertrain and the former Aerospace business unit. Robert was lastly responsible for the Volvo Group long term roadmap for heavy duty trucks as well as leading a team of senior product planners delivering strategic investigations and project pre-requisites. During his time at Volvo Group, he was also deeply involved in formulating strategies and business models related to connectivity, automation and electromobility. He has a wide experience from commercial vehicles, as well as from passenger cars based on spending 9 years within product planning, product development and as a business process manager within General Motors prior to joining the Volvo Group. 查看所有帖子 Robert Widell

关于我们 Paul Taylor

Paul has over 20 years of experience in the aerospace and automotive industries. His career started at Rolls-Royce Aero Engines in manufacturing and engineering, broadening his experience through a series of customer facing, programme management and business orientated roles before moving into supply chain. Most recently Paul has worked with Jaguar Land Rover to manage their Connected Supply Chain programme. This programme aimed to drive a step change in how the OEM collaborates with its suppliers, to optimise inventory levels and to provide visibility, tracking and issue alerting for long distance supply chains. 

Paul has a degree in Aerospace Manufacturing Engineering from the University of the West of England. 
He has a passion for motorsport, in particular MotoGP, and has built a replica AC Cobra kit car which he now enjoys driving whenever time and the weather allow.

查看所有帖子 Paul Taylor


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