Industrial IoT makes machine tools smarter

Shams Hasan, infrastructure solutions manager, META region at Dell EMC looks at how IIoT is giving manufacturers the edge

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Industrial IoT makes machine tools smarter Hasan: "An ideal connected machine solution should be flexible enough to access data from any industry protocol."
By  Shams Hasan Published  January 15, 2018

IDC predicts that by 2018, a third of industry leaders will be disrupted by “digitally enabled competitors.” To remain competitive, original equipment manufacturer (OEM) machine builders need to digitally transform by embracing the industrial internet of things (IIoT) and Big Data predictive analytics.

On that note, manufacturing companies are increasingly utilising IoT and Big Data to address and improve their top operational challenges, including, reduce unplanned downtime; improve overall equipment effectiveness; reduce maintenance costs, and; increase return on assets.

As IIoT continues to evolve, there is an increasing opportunity for machine builders to gain a competitive advantage to generate new revenue streams and improve their product development processes through the availability of real-time data. The vital focus is on using this data to provide better service and support to the customer, and improving equipment availability through remote monitoring and predictive maintenance models.

Through innovative OEMs embedding IIoT technology into machines, remote personnel can troubleshoot issues, change operating parameters and oversee machine operation with supervisory control to avoid possible problems. OEMs can now advise on-site engineers and operators on how best to remedy a problem or improve performance. This type of expertise combined with multitenant real-time visibility into daily operating conditions can extend the lifetime of machinery and process equipment.

To achieve these significant outcomes, organisations should follow these six steps when planning their connected machines implementation:

Define the business case for machine connectivity

Focus first on establishing clear business objectives for the way new data will be used to digitally transform the business. It’s important to understand customers’ key performance metrics to create a competitive advantage. Clearly defining these unique metrics will have significant influence on customers’ ability to optimise operations while managing risk.


Determine what data is valuable to gather

Large amounts of real-time data can be generated by connected machines. Managing high volumes of machine data will require appropriate provisioning to accommodate secure network transport and storage. Subsequently, it is important to first determine what data is valuable to gather based on the business objectives. Begin by picking a specific business objective and let that determine the data that is captured.


Decide the best way to capture data

Depending on the machines and the connectivity standards there may be multiple data protocols being utilised. An ideal connected machine solution should be flexible enough to access data from any industry protocol and scalable enough to interface with a broad variety of industry protocols and data sources.


Develop a security strategy for connectivity

Security is a key consideration for any IIoT deployment, and it is important to have a security strategy from the start. The first step is to ensure that the data that is being moved is most critical to achieving their business objectives. The next step is defining comprehensive security policies that determine how IoT-connected devices will communicate. Best practice deployment safeguards may include the following: block all inbound wireless traffic to the gateway, lock all physical ports on gateways, partition your network of industrial machines for isolation from all other networks and establish authentication/authorisation access controls.


Give your customer the flexibility to distribute analytics

As the OEM, it is important to help customers establish an advanced analytics foundation based on their specific operation. Take action immediately by detecting and responding to local events at the edge as they happen. A distributed approach enables simultaneous integration of additional data sources in the cloud, enabling remote access to critical data.


Digitally transform by acting on the analytics

Turn insights into action by integrating connected machine data into the business and customer. Use newly available data insights to improve operator visibility and move from reactive or fixed schedule maintenance models to predictive maintenance. Build contextually relevant user experiences for the people who know the machines best through web, mobile, and embedded applications that scale gracefully from smart phone to desktop.

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