The digital revolution will be even greater than the industrial revolution in the 18th century. Since then, rapidly evolving technology, shifting consumption patterns, Brexit, political landscape changes and global market movements are the potential catalysts behind the industries current quandary. The move from an agrarian economy to one which is dominated by machines and industry, particularly with coal, iron and textiles gave Britain a head start on its European counterparts. It was never going to last for long, in fact we are less than a decade from decommissioning all coal fired power plants and replacing these will renewably harvested energy which will drive our digital economy for generations to come. Other economies quickly caught up when the world moved toward the second industrial revolution which gave rise to steel, electricity and petroleum.
Electricity was the great leveller for many companies who manufactured goods and allowed for the third revolution to take a hold with computerisation becoming the status quo. Computer-integrated manufacturing has allowed for almost the entire production process to be automated and controlled remotely. Throughout the development of this phase of industry, one key component grew exponentially, data. It’s this which propelled the world to where it is now; Industry 4.0. Computerisation is now analysing data faster and more effectively to discover and augment actions and activities. The digital horizon has expanded rapidly as new opportunities, on a global basis open up. The maturity of Robotics, machine learning and artificial intelligence are now posed to propel us towards a new manufacturing era.
Data is shaking up the manufacturing process
The fact all this data is at hand does not mean insight or orchestration is easily extracted and implemented from it. Specific questions need to be asked before they can be addressed by the data, mainly through analytical queries. For some, increasing yield whilst reducing waste is a key question, whilst for others understanding smarter and more efficient production or fulfilment is the focus. For instance, real-time analytics of high-street food vendor sales allow for the continuous production and delivery of whatever is in highest demand, and where. Understanding trends and automating the linkage between campaigns and promotions to fulfilment for waste management is fundamental. But fulfilment has to be integrated into logistics to optimise sales success as this is often a determining factor on securing repeat business as well as overall satisfaction.
Data insight and analytics is also reshaping the product and manufacturing thinking and approaches. For those operating with a just-in-time (opens in new tab) (JIT) model, developed in Japan in the 1950’s, the need to keep goods in permanent storage is negated. Real-time click to order and fulfilment are now mainstream. JIT models now fully integrate supply chain management, logistics and CRM promotional interfaces to ensure all aspects of fulfilment are aligned. For this to work, machinery needs to produce goods steadily and with the highest quality but most importantly, inventory has to be front of mind. Many processes are introducing flexibility to support personalisation. This process allows for deeper, longer term customer engagement and connections which can enhance margin through a lifecycle of services and repeat business. Artificial Intelligence (AI) when combined with machine learning and robotics are creating the next generation of production processes which not only seek to find smarter and more optimal ways of buying existing products but to test and identify new and innovative products which automatically become “goods production to order”.
Connected and embedded device functions are improving the manufacturing process, too. The Internet of Things (IoT), Beacon technologies and embedded performance tracking are now reshaping both manufacturing processes as well as optimising proactive supporting services. As the volume of data grows, it becomes inherently harder to understand trends, common issues and prioritising areas of improvement. For manufacturers, this enables them to extend their relationship with the customer beyond the point of purchase and truly interact, analyse, detect and remedy problems in the supply chain or functionality of their machinery that are connected to product data. Most importantly it can automatically improve the customer experience and reduce the volume of potentially problematic or defective components.
Having an eye on the production line and gathering data to allow for a smooth operation is key, but another aspect which is equally important is supply chain optimisation. This is somewhat covered in the JIT model but knowing where goods and services are procured from, and how much they cost, are they sustainably and ethically sourced, keeps operational costs low and organisational commitments front of mind. In an industry where supply and demand changes frequently, personalisation introduces additional cost challenges in make to order as well as enabling deeper customer engagements, having a handle on where the most cost-effective products, optimal production processes and support eco-systems is crucial to delivering margin enhanced, higher customer intimacy products.
To achieve this, and to analyse the aforementioned data, and the most relevant data, robust in-memory databases are offering businesses the ability to deliver increased and integrated platforms which enable the above outcomes. In-memory platforms such as HANA and S4/HANA are delivering value beyond performance through integrated predictive analytics, integrated business processes and real-time insights. Massive amounts of data are created in the manufacturing process every day. From goods used to produce a product, through to connected devices which analyse the production process for defects and output and embedded technologies for track customer usage. Real-time analytics not only enable capture and analysis of this information but automation of an integrated customer experience for repair, renewal, personalisation, service and recommendation, is now a minimum expectation. Think of the Samsung’s Galaxy Note 7. The device had a battery defect which cost the company millions in recall, replacement, brand reputation and customer confidence. This is not easy to overcome.
End users can rest assured
Analysing the entirety of the manufacturing and consumption processes, from acquiring inventory to producing products, throughout the lifecycle of usage will improve the quality of the goods sold and the customer experience. It also allows manufacturers, who are confident in the goods they produce, to offer personalisation through design and service in new ways without incurring significant costs.
Connected devices, even if they are vilified, also play a role in the longevity of goods produced and continuity in our lifestyles. In the car manufacturing sector, new vehicles are installed with both connected devices and experiences which can enable a more seamless transition between our destinations as well as a deeper understanding of our vehicles. As electric and driverless vehicles become mainstream, this will become increasingly important as the complexity of the driver experience changes. This improves customer experience and value added during each journey and throughout ownership of the product. This added value, inclusive of offering tailored insurance, service, renewal and configuration packages dependent on usage data, enables users to make informed decisions and manufacturers to stay ahead of the competition.
Ultimately, the issue is not technology or data but relevant data processed by the technology. We need to focus on the automation of this analysis and associated learning to gain the greatest benefits and value and trust in the changes and optimisation to our manufacturing processes. Just In Time will evolve to Just In Memory where decisions are analysed and assessed and actions implemented with the governance we define to improve customer experience, safety, efficiency and choice.
Matt Lovell, COO, Centiq (opens in new tab)
Image Credit: Alexskopje / Shutterstock