The Covid-19 pandemic has changed how many companies view the necessity for digital transformation and their use of data. Innovation has led many organisations to become more software-oriented.
The collection of data can help organisations optimise their operations, lower costs, and improve efficiencies. Inefficiencies can be identified within an organisation and corrected, streamlining the entire process and subsequently improving the overall ROI of the manufacturing line.
With the increasing sophistication of sensors, and the availability of real-time data for analysis, manufacturers can adapt quickly and remain agile in an increasingly complex and volatile external environment.
During a global pandemic such as Covid-19, it is critical for manufacturers and organisations to make informed decisions. Data analytics helps tremendously with this effort.
With significant data being collected from smart enterprises, it is essential that this data is understandable and the correct questions are asked of this data so action can be taken.
“It is very crucial manufacturers make the right decision,” said Randy Goh, country manager Singapore, SAS, speaking exclusively to Industrial Automation Asia (IAA).
“We can combine internal and external data to help organisations better understand the possibility of what is going to happen in the future. With that, the company can make more informed decisions.”
“During this crucial period, you want to know what is happening, you also want to predict what is going to happen,” said Goh, “and then to optimise it at the same time.”
“Because of Covid, there has been, at least for the short term, a more reactive response, because you know manufacturers are faced with reduced demand, curtailed distribution, worker safety issues, raw material sourcing, and so on.”
“And what is also very important is the obsolescence of their products,” said Goh. “The risk of that has gone up dramatically.”
“It is a case of re-prioritisation,” added Goh.
Ultimately, manufacturers need to refocus their efforts and understand what is important during this time. This is where analytics can help, alluded Goh.
“There are short term pains,” said Goh. “We need to move from a ‘how do we help them’ to think about how to take proactive steps in terms of making the shift from data to decision.”
Goh said that this is where the technology piece comes in and in a broader sense changing mindsets and the overall culture.
“What we have observed is that the analytics maturity will really have an impact,” said Goh. “And defines a company’s valuation, one way or another, at least in the near future.”
Of course, the challenge for organisations is to figure out how to incentivize those efforts and to cascade that down the value chain.
“There is the short term impact of Covid,” said Goh. “I think in the long term we see a lot of potentials to bounce back and help organisations realise some of their reprioritized goals in analytics.”
It is hard to imagine an industry that has not been impacted by this pandemic. Companies such as SAS are no different in that sense, and like every crisis, there are opportunities and challenges.
However, as Goh explained, while Covid-19 has changed the organisation, it has not altered the company’s product roadmap, at least not in the short term.
“We have additional things given the current situation like for instance contact tracing becomes an important part of it. In the past there has not been such a big emphasis on that,” said Goh. “There is also a greater emphasis on visualisation of trends, given the current situation.”
“In terms of our solutions, it is still the same, but in terms of the usage, it has been applied quite differently in many cases in this case for the requirements of Covid-19,” added Goh. “Such as contact tracing, hospital bill optimisation, or even looking at the different trends.”
Furthermore, prior to Covid, said Goh, a lot of organisations, and this applies to not just manufacturing but also the other ancillary industries as well, is that there is always the concept of a building inside-out approach.
From the point of view of an operations first approach and trying to build out something towards the needs of the market.
“What we have observed is from a technology perspective, there is now a shift from building with first an outside consideration, then inwards. An outside-in perspective,” said Goh.
“What has changed is it has become more of a consideration for ecosystem and interoperability,” said Goh.
Putting that in the text of has SAS changed its roadmap, “I would say not in a big way.” In fact, it lends well to the roadmap that we have always had.”
The two key things that we stress a lot in our products and offerings is first openness of infrastructure and for SAS we know that there is a busy ecosystem of vendors and SAS usually wants to be a part of any organisation knowing that we are there to augment a technology need.
“Oftentimes there are legacy environments and data that we have to work with, so we are very integrable from an openness of infrastructure perspective.”
“The second point is really in terms of strength of orchestration. A number of examples have been highlighted that cut across industries, and that goes to show in terms of our ability to take data into the decision from an orchestration of data. It is a big deal for organisations that have not started doing it before.”
Future Of Analytics
A lot of companies today implement descriptive or reactive analytics. Eventually, this will move up the ladder towards predictive and eventually prescriptive technologies. To achieve this, artificial intelligence (AI) will play a significant role, in addition to the Internet of Things (IoT) and streaming analytics.
“A lot of companies, as they come out of Covid, are going to start reimagining their enterprise,” said Goh. “They are going to think about how their organisation can handle the next disruption of this nature or any nature.”
This will focus on discussions around intelligent automation. “The intelligence is going to come out of the analytics, and AI, and operating on data that is streaming in from the edge.”
This data could be data from machines on the manufacturing floor or production lines. It could also be data that is flowing in from the supply chain, from logistics, from transactional systems, and that could be data coming in from the market, from customers, from social media.
As such, companies are going to be looking at bringing all of these data streams together, operating on them to derive actionable intelligence.
This could derive results such as ‘what are customers sentiments about products,’ for instance.
“We are going to see analytics really become ubiquitous and AI will become more ubiquitous over the next few years in manufacturing,” said Goh. “We are going to see more companies leverage streaming data and streaming analytics and especially AI and IoT.”
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