The 5 challenges of data driven manufacturing

Based on the applications for big data and analytics, the Internet of Things (IoT) is all set to create the next generation of manufacturing. IoT can reduce costs and improve productivity across an entire supply chain, optimize distribution, and bring about new types of after-sales service. A data-driven organization will not only streamline its manufacturing operations, but it can forecast or quickly resolve any issues affecting plant performance or customer satisfaction.

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To become a data-driven manufacturing business, an organization must understand some underlying challenges. The five major challenges manufacturers may need to overcome are:

#1 Lack of understanding of the benefits

Today, small companies make up a majority of the manufacturing sector. In companies with less than a thousand employees, one might find a small and overworked IT department. Its staff may have limited knowledge of business information, software, and database technology. Such companies will not have the technical staff available to help them understand the various benefits associated with data-driven technology.

#2 Limited budget

There is always a limited budget allocated to invest in information technology in smaller companies. Any type of digital transformation is expensive. The end users will need to be trained on the new systems as well as data cleanup and maintenance tasks. This endeavor presents a different challenge for small and big companies alike: regardless of budget, it takes a few years to evaluate and fully deploy any new technology.

#3 Inaccurate or incomplete data

Data is at the core of deploying any business-information solution. This means a lot of time is spent preparing and ensuring that the data accumulated is complete and accurate. If the data is inaccurate or incomplete, the decisions made can affect productivity. Due to a lack of sufficient data collection resources, smaller companies may find themselves relying on spreadsheets for most data analysis and discovery. This system can be both tedious and time consuming to maintain. In big companies with technically qualified internal resources, there can still be extensive backlog to get through before deployment can be realized.

#4 The integration of legacy systems

Introducing new technologies is always exciting, but finding a way to make them work with well-established and proven legacy systems is difficult. A typical factory can possess multiple levels of technology beginning with programmable logic controllers on machines, and extending to distributed control systems and supervisory control and data acquisition systems. Starting from scratch can be easy, but integrating efficiently with an established design and manufacturing environment requires a lot of time and effort.

#5 Security challenges

Industrial control systems were designed and installed at a time when cyber security was not much of a concern. Today’s distributed control systems are connected to the Internet, exposing them to the risk of unauthorized access by attackers who could sabotage the system. Moreover, IoT devices often provide remote system access and control capability, making data security an even greater concern than ever before.

Are there any other challenges with data-driven manufacturing? We want to hear from you.

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