Data analytics has been an integral part of manufacturing management for most of its history. However, analytics has undergone both evolutionary and revolutionary changes over the decades with the advent of information technology and digital data gathering and analysis. In Part One of this series, I took a look at the evolution behind data analytics and applications in Manufacturing. Part Two provides insights into implementation of analytics in manufacturing.
Part Two: Implementing Data Analytics in Manufacturing
Acquiring Data
The first step for data analytics in manufacturing should be to implement solutions that connect manufacturing equipment, sensors and controllers to a converged network so data can be captured, moved and stored for analysis in an appropriate manner. While manual data entry is common and will probably continue to some extent, automation is critical to ensure that data is captured in real time, accurately and in the right format to enable analytics and decision making.
The amount of data available on the manufacturing plant floor has increased by many orders of magnitude over the past decade, however analysis and application of such data in decision making has not kept pace. It is this space of analytics that is now driving adoption of 'Internet of Things' (IoT) technologies such that IoT and analytics have now become intricately linked to each other.
Going beyond just analyzing data from IoT and expanding it to include the impact of this data on the people, skills, business processes and linking all of these disparate elements into a single business-focused system is referred to as the 'Internet of Everything' (IoE). Manufacturers are only now just starting to take this wider perspective to analytics and the application of analytics in manufacturing. As manufacturers begin to rely more on data for analysis into business processes, they must also consider some challenges that may arise during implementation.
Virtualizing the Data
Today's manufacturers need the ability to integrate all data from various departments/locations, which has proven to be difficult in the past. The old approach consisted of building a data warehouse where data was extracted from multiple sources, transformed (normalized, processed, condensed) and loaded on a periodic basis into a central data warehouse. Today's manufacturers need data that can be used in real-time to make decisions, not data stored in a warehouse for historical analysis. A steep increase in the use of cloud storage for such data warehouses has led to data being stored across different clouds (mix of public and private) on different platforms. Bringing all of this together to yield meaningful results without moving all the data physically into one data warehouse has been a challenge. Data virtualization solutions now enable accessing data that is physically in different databases and geographic locations as if it were physically in a single data warehouse. This has becomes even more critical with the large volumes of big that are typically unstructured and not easily amenable to traditional data warehousing approaches.
Integrating analytics into business processes
Data analytics cannot be a standalone activity done in a data center by a team of experts. It has to be integrated into the key business processes such that analytics are focused only in areas that provide business value and are available to decision makers at the right time in the right place. Important questions to be considered when implementing analytics solutions are:
Responses to these questions will define your strategy and dictate how analytics are integrated into the business. Implementation models could include
Implementation challenges
Implementation of data analytics should consider the following:
How can Cisco help your manufacturing organization improve efficiencies and gain valuable insight through data? Visit our solutions page to find out more and share your thoughts with us in the comments section below. Stayed tuned for Part Three of this series where I will share experiences in implementation and detail how analytics and IoT are working together to bring results in manufacturing.