The Big Data Revolution in Advanced Manufacturing
The importance of Big Data in Advanced Manufacturingis critical to the advancement of the industry, and for UK manufacturers toremain competitive with international manufacturers in Germany, and Japan.However, it is clear that there’s a lot of confusion around the subject, andthat’s why we (Valuechain) teamed up with ADS, and a number of other partnersto help demystify Big Data, and unlock the value it can have to businesses.
The Big Data Opportunity
While the benefits that can be brought to advanced manufacturing supply chains, through the analysis of big data are clear, there are also some challenges, and inhibitors, that must be overcome to unlock the potential of big data. That is why I put together this post, to help you navigate Big Data, and ensure you are able to use it to your full advantage.
I will begin by describing some of the drivers for change and the role big data can play. I will explain what we mean by supply chain big data in this context, the enablers for big data and the inhibitors that have prevented supply chains from exploiting technologies that already exist. I will offer some solutions to overcome these obstacles and propose opportunities that companies here today can benefit from.
Drivers for Change - 5Cs
1. Complexity: Rationalisation & TieringFirst of all, Aerospace and Defence supply chains are inherently complex. This has been compounded by supplier rationalisation programmes, and the development of multi-tier supply chains. Multi-tier supply chain optimisation is a complex problem, and the approach has been to abdicate responsibility to the next tier down, rather than use internal resources to improve multi-tier performance. This has led to an incredibly fragmented knowledge of the supply chain, which impacts on network agility and overall performance.
2. Commercial: Global Offset & Low CostSecondly, Commercial drivers for aerospace and defence OEMs are creating new global challenges. Initially OEMs were seeking advantages from low cost labour countries such as India, China, Mexico and Eastern Europe. However, few companies truly benefited from the total cost of acquisition reductions they were seeking, and a lot of this comes down to a lack of data and communication. From supplier selection, supplier development, collaborative NPI and work package management; the cultural and logistical challenges exacerbated supply chain inefficiencies.
More recently, OEMs have not only sought lower cost from emerging supply chains, but are now seeking new customers from growing economies in Brazil, Russia, India and China for example. However, OEMs must establish supply chains in these emerging economies as part of their offset obligations, and Big Data is crucial to their success.
To establish supply chains overseas, OEMs may commit to moving large work packages to emerging economies. However, the multi-tier complexity of their existing supply chains makes it challenging to understand exactly what technical capabilities and quality approvals are required, to successfully establish a sustainable supply chain.
Furthermore, it is really complex and challenging to map existing supply chains onto an unknown supply chain in an emerging economy where the supply chain capability and structure is completely different.
3. Capabilities: Emerging TechnologiesA third driver in the Aerospace and Defence supply chain, is that manufacturing technologies and materials are changing. The most prominent emerging technologies, relevant to this sector, include composites, which is growing rapidly, and additive manufacturing, which is still relatively nascent. It is thought that by implementing these technologies, OEMs will be able to gain a competitive advantage, either in fuel efficiency, or responsive, aftermarket service.
However, as OEMs seek to establish these technologies in their supply chains, they will need to find brand new suppliers. OEM procurement and supplier development teams only know what they know. Big Data can help them to identify what they don’t know or, more specifically, which suppliers they should know.
4. Capabilities: Growing IndustryThe significant growth in build rates has meant that global demand is outstripping supply. Therefore, organic growth of existing supply chains is constraining growth. This means that OEMs must identify new suppliers to increase capacity. However, the current process for OEMs to select new suppliers is incredibly risky, with minimal performance related information available. There is currently no way of know if the potential suppliers are strategically aligned, and have the right technical capability and performance levels. Big data can, therefore, play an important role to reduce supply chain selection risk.
5. Competition: Cost SensitiveThe fifth driver, is the competitive pressures that OEMs are under to reduce supply chain costs, whilst improving customer service levels. There is only so much that can be achieved through price negotiation with suppliers, and sustainable cost competitiveness can only be achieved through waste reduction.
While lean and agile improvement programmes have been deployed at many tiers in the supply chain to reduce waste; most activity has been focussed within the four walls of factories. The problem, is that the root cause of most waste and inefficiencies come from volatile customer demand and unreliable sub-tier deliveries. Big Data can enable multi-tier supply chains to streamline communication so that they can improve planning, increase resource utilisation and reduce inventory levels, amongst other things.
How Can Big Data Help?
- Improving multi-tier supply chain transparency to reduce complexity;
- Mapping existing and future supply chains, to reduce difficulties expanding overseas to low-cost markets;
- Mapping new technologies onto new and existing supply chains, to ensure easy uptake of new technologies;
- Provide dynamic rate readiness to identify constraints and simplify new supplier selection; and
- Streamlining communication, so that companies can collaborate to reduce waste throughout the supply chain.
What is Supply Chain Big Data?
For example, OEMs will often have disparate databases which monitor supplier development or supplier auditing. They will also have data regarding supplier collaboration activities; including new work packages, NPI and collaborative problem solving. Then there is external 3rd party data such as financial risk assessments, environmental risks, geo political risks and quality accreditations such as AS9100 and NADCAP.
Suddenly there is vast amounts of data being captured across 4 or 5 different platforms, which is very fragmented. With no uniformity to the data that is collected, it’s not surprising that most organisations struggle to make good use of this data.
This is because: Capturing this data is only the beginning.
The key thing for organisations to figure out, is how this data can be applied to their business to optimise supply chain performance, and inform strategic and tactical decisions. Also, how can supply chain big data underpin continuous learning so that each tier of the supply chain can improve overall supply chain performance.
Enablers for Supply Chain Big Data
Big data analytics is rife, everywhere we look from financial market analysis to your Tesco Clubcard. The algorithms and simulation models exist to provide predictive analytics, which can be visualised intuitively to support decision making.
However, big data analytics is still not being applied effectively to advanced manufacturing supply chains.
So, What are the Main Inhibitors?Multi-tier supply chain data is inaccurate, and suppliers are not engaged to openly collaborate and share data.
This autocratic “win-lose” style of supply chain management has created a lack of trust in the supply chain. Suppliers are reluctant to share data with their customers or partners, in fear that it may be used against them. There are also cyber security concerns; with many companies unwilling to trust cloud computing or the internet. This results in limited engagement, which is impacts on the ability to optimise supply chain performance.
Our approach to address data integrity issues has been to develop ERP systems that are configured to the DNA of SMEs, so that they can be effectively deployed at low disruption and cost. We provide continuous training, customer support and data integrity monitoring to drive data accuracy. Many companies do not want to change their ERP system due to the disruption costs and risks, so we have also developed several bolt-on applications to improve data accuracy, transparency and sharing.
To address the cultural challenges, it is critical to identify win-win opportunities, and answer the question ‘what’s in it for me?’ for every tier of the supply chain. As companies start to engage, and generate tangible benefits from securely sharing data, with selected supply chain partners, then it will encourage them to collaborate more openly with their partners.
Of our bolt-on applications, there are currently 3 dedicated to improving the management of big data in supply chains.
- OpenBook monitors customer performance and identifies schedule change exceptions;
- VMI integrates with customer and supplier ERP systems to streamline stock replenishment and invoicing; and
- NPI enables companies to collaborate with their customers and suppliers to share technical and operational data.