One of Microsoft’s hardware manufacturing plants in Suzhou, China, was able to identify inventory that was on the verge of obsolescence (thanks to AI & Machine Learning) and saved the company nearly $5 million in one year, according to a McKinsey report. This reduced the inventory costs by $200 million. All it needed was a five-person data analytics team that had access to real-time data from the shop floor, thanks to IIoT devices. In another instance, BMW Group’s IoT platform created a connected digital toolbox that was able to boost productivity as well as empower employees to share best practices quickly with other key stakeholders.
These are some typical use cases of how data and analytics can help the manufacturing industry. The idea of a smart factory is no more a mere utopian dream. The rapid rate of adoption of embedded systems and connected devices has given rise to massive data that can help manufacturing industries improve their efficiency.
IIoT at the core of the smart factory
The market for industrial IoT products is expected to grow to touch $263.4 billion by 2027, growing at a CAGR of 16.7% from 2019. IIoT refers to a network of interconnected devices that provide real-time insights from the entire production cycle, be it designing, maintaining, optimising, monitoring and analysing industrial operations for data-backed decision-making. Rapid digitalisation across industries has encouraged the use of IIoT devices as –
It provides access to a large volume of data from multiple systems, such as PLCs, SCADA systems, ERPs and other sources, from multiple locations or production units
It enables preventative & predictive maintenance
It provides data for advanced analytics & actionable insights
It facilitates a centralised view of all KPIs in a unified manner
Self-serve reports and interactive dashboards allow for advanced analytical capabilities that empower manufacturers to use their data interactively for drawing deeper insights.
The key benefits of IIoT for the manufacturing industries include:
Data science – The second pillar
IIoT is one half of the digitisation story. The second and just as crucial part is data science. Data generated by IIoT devices alone cannot help businesses achieve their business goals. Access to Big Data broadens the vision and provides the kind of insights needed to achieve long-term goals. This needs to be properly stored, processed and analysed to get the kind of insights that can power strategies and boost performance. It needs a complete understanding of how the business works & creates value, what its challenges are and what kind of data it needs to draw insights that can plug these gaps.
Data and analytics play a major role in not just making the smart factory a reality but also in enabling the manufacturing enterprise understand market trends, customer needs, regulatory requirements, competition as well as capture other related data that will impact one’s business. Even news of war & weather can create a surge in demand or a drop and will impact the operations. Therefore, manufacturing businesses aiming to leverage digitalisation technologies should take a step-by-step approach towards embracing them.
The first step is in performing a gap analysis and prioritising the infrastructure needed since the possibilities of Industry 4.0 are endless but funding is limited. Therefore, investing in the right infrastructure is important for collecting the right kind of data for the specific needs of the organisation.
The data thus generated needs to be stored in a scalable infrastructure. Cloud is the ideal solution that provides the scalability and flexibility manufacturers need to store, process & analyse data. It could be in a public cloud or a private one. However, a business may also feel the need for increased levels of security and may feel on-prem infrastructure is more secure. They can look at a hybrid infrastructure to make the most of both worlds.
A business needs access to both structured and unstructured data as well as real-time and historical data. The database & data processing capabilities should be able to provide a unified view and extract relevant information even from legacy systems & unstructured data.
Data security is crucial to protect employee, customer and enterprise data and is also a regulatory requirement. Data governance policies & security solutions to translate them into data privacy, prevention of unauthorised access, protection from breaches and so on are very essential. Any breach can also affect the business’s brand image and reputation.
Invest in reporting and analytic tools for creating interactive dashboards with real-time streaming data in order to provide accurate & actionable insights as events unfold. Different functions need different kinds of dashboards and the tool should be able to provide them appropriately. It should also enable self-service as any dependence on the IT team can cause delays and loss of opportunities.
An enterprise-wide integration with other enterprise systems, such as ERP and CRM, can help trigger alerts & notifications in a timely fashion for appropriate addressal of any issues and challenges.
Benefits and impact of data analytics in manufacturing
The benefits of IIoT devices and data analytics are manifold, impacting almost all aspects of the manufacturing process. Some of the benefits include:
Product development: Big Data & analytics can help manufacturers understand their customers & their needs better and use this information in developing new products or improving the existing ones.
Predictive analytics: Businesses can forecast trends and devise strategies to improve their performance & growth. It can help with reducing waste, plan as per demand and control costs to maximise profits even in the most challenging times.
Preventive maintenance: Being able to predict possible trouble with equipment can help prevent its breakdown when executing a project and the resultant loss of time & money. Instead, the business can have a scheduled break for maintenance & repair, thereby, reducing downtime and firefighting to execute a project.
Predicting demand: Manufacturing entities can plan their inventory and production better if they can forecast demand. This will reduce their inventory costs as well as meet customer needs in a timely fashion, thereby, improving sales & revenues. In addition to data from the supply chain and the inventory management system or the ERP, external factors, such as the weather, the state of the economy, markets and raw material availability will also have an impact. Using data analytics makes it more manageable and timelier so that businesses can be better prepared for how the events unfold.
Price optimisation: Arriving at the right price is a complex process that factors in all the elements, right from the cost of the raw material to distribution costs. But at the end of it, it must also match customer expectations, which are the hardest to predict. Data analytics can help with price optimisation for increasing profitability by aggregating & analysing pricing and cost data from internal sources as well as comparing it with that of competitors to arrive at the best optimised price variants.
Warranty analysis: Warranty claims are a mine of valuable information about the product quality and can be useful in effecting improvements in the existing products or develop new ones.
Supply chain risk mitigation: On the supply side, manufacturers can manage the complexities, anticipate problems and identify solutions to mitigate risks. The challenges have increased with global outsourcing and distribution. Visibility into manufacturing processes & product delivery can help predict possible delays and other issues to help manufacturers develop contingency plans.
Shorter time-to-market, without compromising on quality and functionality, are essential for a manufacturing business to be able to stay competitive, meet customer needs, be compliant and be profitable. Data analytics technologies enable this by providing insights that can help improve the effectiveness of different business functions – right from designing to distribution – to meet specific business needs & goals.
Key to building the smart factory of the future
However, for an effective implementation, the right infrastructure is essential, and therefore, understanding the gaps & how they impact the goals is critical. This will determine the data management strategy encompassing everything, from ingestion (data sources) to storage (data management), retrieval (data transformation), governance (data security & data governance) and generation of reports & dashboards (data visualisation & BI). The real key to building the smart factory of the future will be at the intersection of four key technologies – IIoT, data engineering, data analytics and data science. Needless to add, one also needs the right human capital to garner insights and make swift & actionable decisions.