Data Silos - Overcoming Hindrances And Escalation Of Costs
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Data Silos - Overcoming Hindrances And Escalation Of Costs

An overview of data silos. Elaborating with an example from manufacturing.
Data Silos - Overcoming Hindrances And Escalation Of Costs

To uncover any kind of data insights, businesses need to first integrate siloed data from different applications, systems, and warehouses located across the business.

Manufacturers generate massive amounts of data, which has been used in their operations for a long time, some of which would have been collated.

Again, the main difference between OEE, OOE, and TEEP is the time or Availability that is used in each calculation.

In other words, the only changing variable between these three calculations is the maximum time that you define as available for a machine to run. OEE, OOE, and TEEP all take availability, performance, and quality into account. Here is a more detailed look at the calculations for both OOE and TEEP :

Total Effective Equipment Performance (TEEP) considers maximum time to be All Available Time — that is 24 hours, 365 days a year.

Therefore, TEEP = Performance x Quality x Availability (where Availability = Actual Production Time / All Time).

Overall Operations Effectiveness (OOE) takes unscheduled time into account, looking at Total Operations Time as the maximum.

Performance x Quality x Availability (where Availability = Actual Production Time / Operating Time)

Overall Equipment Effectiveness (OEE) only considers scheduled time. If a machine is down due to maintenance, and it’s not scheduled for work, OEE ignores this time.

Performance x Quality x Availability (where Availability = Actual Production Time / Scheduled Time)

With Industry 4.0, there is proven evidence that combining data from existing silos along with new sources and modern AI tools can yield higher ROIs. New tools like Machine Learning and Artificial Intelligence can be of tremendous business value.

With such huge amounts of data, the trick is to find the hidden connections between complex systems that will lead to huge ROIs by increasing throughput and reducing downtime.

Yet, despite their efforts, many organizations have discovered that there are unexpected practical challenges and pitfalls to making this process work. Today it is about how fast you can solve problems with data and how quickly you can put together a clean, unified dataset to get value from it.

This is where the hidden cost of data silos comes from.

Any software or hardware alone cannot solve this issue. But, the right Industrial Internet of Things (IIOT) platform scavenges the data from machines on the floor and passes it on to a data broker and in the process also unifies it with existing data for real-time analytics, i.e. converting raw factory information into valuable data insights.

There are two challenges, which are both costly and time-consuming, to the above mentioned

  1. Combine parts of that data in one place
  2. Clean and structure the relevant data so that it can be fed into data science tools.

The process of combining and cleaning data has been a key impediment to implementing Industry 4.0 projects. One difficulty is that machines report data only to a database built by the same vendor, requiring engineers to spend hours or even days manually downloading information from silos to correlate it with other data to glean the needed information. Hence this process, which is costly and time-consuming, may deter companies from fully exploring data insights to better optimize factories.

Some of these challenges and difficulties in dealing with data silos are addressed by Keito, in its future-ready solution. Here, tools like Keito helps not only to bring information from all channels, but also identify meaningful data, and offer information supply to various other systems for digital transformation.

But today as technology has evolved Artificial Intelligence (AI) can help companies gain insight into effectively improving fab operations and drive lower maintenance costs and higher yield.

The concepts applied to improve speed and yield in operations are the same as those used to enhance data standardization and output.

For either situation, the correct set of tools are critical. A combination of AI and the right set of tools to properly tee up the raw data is an ideal solution for increasing ROI

Hence the earlier management realizes that minimizing the cost of unifying data across silos(by using platforms like Keito) not only has benefits for the entire business but also overcomes most data-related issues, the faster their organizations will be future-ready.