The past few years have seen a sharp rise in maturity of new digital capabilities, including cloud computing, big data analytics, sensors and machine-to-machine communication (often called the Internet of Things or IoT). These new tools are impactful to manufacturers as much or more than to other industries. These disruptive technologies have spawned many new and exciting ways to solve problems in manufacturing, often labeled "smart manufacturing."
In a recent blog, Sean Bryson described Hitachi Optimized Factory, the Hitachi best practice approach to smart manufacturing. Hitachi Optimized Factory offers many use-cases and a digital transformation roadmap for manufacturing, but is this indeed valuable to your business? The question manufacturers ought to begin with then is: Why implement smart manufacturing solutions at all?
Value of Optimized Factory
Improved worker safety. The US-based Occupational Health & Safety Administration (OSHA) estimates that 36 out of every 1000 workers will get injured this year, resulting in lost time, personal costs and damage to company reputation. Costs are considerable: OSHA estimates that $1B/weekly is paid in direct compensation to injured workers and OSHA penalties can exceed $12,000/day for non-compliance. Many injuries are preventable however, the top three causes reported are: 1) struck by something; 2) over-exertion; and 3) a fall. Digital tools such as cameras and wearable sensors can be used to monitor workers for abnormal activity or repetitive motion. Big data analysis can identify causal relationships between operating conditions, training, environment and personal injury to avoid putting people in unnecessary dangerous situations.
Increased operational efficiency and productivity. Manufacturers continually seek reduce operating costs, get products to customers when promised and make use of available capacity through improving:
- labor productivity,
- production throughput,
- equipment availability and
- product quality or yields.
Manufacturers employ various means, including upgrading equipment and up-skilling workers, though manufacturers report having reached a limit of continuous improvement through these traditional means. Digital tools offer an avenue to step-change improvement. Cameras and other sensing technology can monitor and measure labor productivity and material flow, identifying poor performers and bottlenecks. Predictive quality and predictive maintenance analysis can forecast downstream material waste and equipment downtimes and allow time for remediation.
Smart Products and Increased Customer Experiences. Product-based companies seek to improve customer satisfaction levels and customer retention. Understanding how products are being used by their customers open a new level of customer interaction, allowing product designers to quickly adjust product design to meet customer demands. Further, IoT-enabled products can tie the customer to the manufacturer in ways that enhances the customer’s usage of the product and affiliation to the seller, including reliability monitoring, promotion awareness, firmware updates, auto-replenishment of consumables, etc. In certain cases, this information can even be employed to establish new service markets, or service-based business models.
A recent blog by Jordy Almgren introduced us to a Hitachi client who used new sensors along with IoT technologies found in Microsoft’s Azure platform, Machine Learning and Power BI to drive improved reliability, productivity and safety of their overhead cranes, in ways unachievable prior to this technology.
With the new data, the manufacturer learned that the cranes were in motion less than 50% of a shift, so they could consider moving from a dedicated operator per crane to one per two cranes, freeing up a worker to provide value-added work elsewhere. They also measured energy spent and distance traveled on each of the three axes of the cranes to better gauge actual wear and tear on the independent equipment driving each of the axes and be able to more effectively plan for maintenance activity and spare parts based on usage rather than calendar time.
With new sensors and big data analysis, the manufacturer analyzed crane velocity, crane acceleration, load on crane, environmental conditions and personnel to identify patterns of safe vs. unsafe operation of crane. Operators are then evaluated against the safe operation baseline. Unsafe operators are then re-trained. Increased data collection also captures near-misses and provides safety management the circumstances and root cause to prevent next time.
In an industry where products look similar to each other with the naked eye and precisions are measured to the micron, the metal manufacturer must assure that customers receive accurate and high-quality metal products on-time. Penalties for returns and expedited shipping are high, and wasted material is very expensive. The manufacturer is considering implementing high resolution machine vision systems to inspect 100% of the surface of the metal and measure the cut size against the customer order, in real-time as it is moves through the high-speed process.
How Microsoft Azure Helps Drive Value with Optimized Factory?
Microsoft’s Azure platform provides essential features and services needed to support global manufacturing needs that few providers can match. Between Microsoft’s 54 global Azure regions, the on-premises Azure Stack solution, and the advanced IoT edge solutions, Azure provides a strong foundation.
Scalability allows digital manufacturing activities to proceed in a crawl, walk, run mode. Industrial IoT (IIOT) use-cases are often best suited to be explored as a proof of concept then scale to a broader pilot implementation before full-scale deployment. The ability of the Azure IoT services to scale seamlessly to support small to large volumes quickly and easily is essential in creating and deploying an IIOT solution. Further, it allows the project to abort and decommission with little overhead cost if the business case being tested does not yield the desired outcome.
Elasticity of the Azure platform allows digital manufacturing projects to be deployed quickly, shorting the time to value. Digital manufacturing projects often seek very short payback periods, and the Azure framework minimizes provisioning and service-to-service integration complexities, allowing the team to focus on building business value-driving solutions.
Azure supports a very extensive set of data storage, integration, analytical and presentation building blocks can adequately address and solve even the most obscure use-case. The Azure toolbox means less risk that the business requirement can be achieved and should provide confidence in the digital manufacturing development team that demands can be met.