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Digital Transformation 4.0 

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Let’s just say it: industry 4.0 is a powerful technological weapon, and as with all weapons, it is often easier to hurt yourself than to use it correctly, especially if you do not have a full understanding of its operation.  As many companies jump feet first into industry 4.0 and invest lots of resources (material, financial and human), hoping for a return on their investment, it is important to bust some of the myths that could significantly compromise this investment. Not taking these issues into account could seriously undermine the positive benefits of your move to 4.0, and ignoring them could wipe them out completely.

Finally, whether you are investing thousands or hundreds of thousands of dollars, one fact remains: in technology as in many things, it is wishful thinking to expect everything to simply fall into place once you have spent enough money.

Here are 3 myths surrounding industry 4.0 that every manager should debunk to ensure their return on investment:

Openmind technologies - virage numerique 4.0-03

Myth #1

After shifting to industry 4.0, it will be easy to determine my business objectives with the data generated

Some managers believe that by going through their digital transformation, new and improved business objectives will appear magically out of their copious new data.

That would be like saying that if you are building a cabin and need a specific tool, you just need to walk into the hardware store and the right tool will present itself to you.

We all know, naturally, that the best way to proceed is to first determine which specific tool we need to complete our task, and then go to the hardware store to buy it.  As many would say, “It ain’t rocket science.”

So why would anything change if we replace “tools” with IT data, and “building a cabin” with business objectives?  Why change what makes so much sense, just because now we’re in IT?  Why deny the most basic logic, taking IT data as all-powerful, but with no specific purpose?

These questions answer themselves—it’s clear that you must first develop your business objectives before even beginning your shift to industry 4.0, which is a means and not an end in itself.

In concrete terms,

  1. You set a precise objective, such as determining the real production capacity for each team in Department A within the company on a daily basis;
  2. You identify the data you need to develop this objective, and how the data will interact;
  3. Finally, the digital transformation 4.0 will enable you to meet this objective (sometimes by finishing an incomplete 3.0 transformation), but above all, through the interaction of the previously determined strategic data.

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Myth #2

Industry 4.0 will make it easy for me to make strategic business decisions

So you’ve developed your strategic business objectives, and have completed your shift to industry 4.0 to make them happen. Great!  Now, your manual or semi-manual processes have been fully automated, and along with them comes an influx of all sorts of relevant data. Concretely, you now have an automated process A generating data, and another process B generating just as much.

For this demonstration, we are only using two processes, but keep in mind that a real case would probably involve dozens of distinct processes: payroll entry, entry of hours worked, classification of billable vs. internal hours, etc.

Openmind Technologies

The previous diagram exposes the second myth—that by simply using the data generated, it will be easy to make any necessary business decisions.  Why is that not true, if the data is now available?  It is because, with a few exceptions, the processes, which collaborate on the same task, are not each in a vacuum: they interact with each other, just as people are constantly communicating between themselves when working together.

Now it is clear where the structural weaknesses lie in the previous diagram.  Let’s say that task X involves identifying a company’s real manufacturing capacity.  Process A generates the number of hours available from each employee, including their leave, vacation and other planned work stoppages. Process B, on the other hand, documents the level of skill and experience that each employee has within the production line for each of the products sold by the company (machine operator, day labourer, etc.).

Now, let’s say that we are trying to establish a correlation between the processes involved in completing an order:

  • Estimation of hours necessary for the manufacturing company to produce order X;
  • The company’s production capacity based on the skill level available from the production team;
  • The customer’s required schedule for the order.

When the data sets for each process are handled in silos, it is difficult for a manufacturing company to make the right decisions regarding scheduling and the ability to respect the delivery date for any products that have been ordered.

Openmind Technologies

We can see here that by using only the data set from process A or process B independently, it would be impossible to accept or reject the offer with certainty.  However, if you bring together the data from processes A and B, the right answer becomes apparent.

By combining data from processes A and B, we could determine that the joint production capacity of machine operators and day labourers working on the order is 1,260 hours up until delivery.  It seems obvious that we should reject the customer’s offer, or at least review the conditions attached.

The previous example shows that by not combining data from different data sets, it is difficult to make an informed decision.  But things can go even more wrong!  In some cases, you might be led to make a bad decision if you are using the data incorrectly.

Openmind Technologies

This concrete example illustrates the idea of using multiple pieces of data in a business decision-making context. Depending on the case, the end decision to go 70 km/hr or 100 km/hr comes down to combining the data involved in the problem resolution process.

Myth #3

Industry 4.0 will have little impact on my human capital

Now that your strategic business objectives have been clearly defined, your processes fully automated and your data combined, you can follow up with your strategic business decisions!  Just one little detail…

Combining data was a crucial step in your transition to industry 4.0, and the same goes for presenting, evolving and recording data as you go along.  For example, it isn’t enough to simply have strategic information.  It has to be presented in a way that makes it easy to understand (reports, dashboards, archives, trends, etc.), updated automatically and regularly to remain relevant, and be accessible and organized for simple viewing and sharing.

Some of these tasks should be handled by a data-modelling expert, while others should be done internally (training, folder hierarchy structure on the server, archiving, etc.).

Since any change within an organization will lead to its share of disruptions, it would be unwise to assume that such an important change—the shift to industry 4.0—is any different.

Beyond the training required for employees who will work on generating data sets, you will also need to account for some people’s natural reluctance toward change; a drop in productivity can be expected when shifting to industry 4.0.

Should this be seen as an impediment to moving forward with industry 4.0?  Absolutely not!  Human impact is inherent to any project that involves… humans! So, nothing new here.  If recently moving the office coffee machine led to a company-wide outcry, a technological transformation on this scale will certainly have a few repercussions of its own.  The most important thing you can do is adequately prepare for the situation, and not minimize it.

Be prepared, not disappointed!

In conclusion, shifting to industry 4.0 is a necessary step for any company that wants to stay at the top of its industry, or get there by making strategic business decisions using streamlined, reliable and comprehensive data.  In spite of everything, some myths surrounding 4.0 remain, and when viewed as certainties, they can easily turn your investment into a nightmare from a technological, human resource and financial perspective.

Be wary if your supplier uses words like “easy,” “quick,” “risk-free” or “inexpensive” when presenting their 4.0 audit, the first step in your shift to industry 4.0.  A professional audit should present the real implications of such a change to your organization, and above all, take the time to confirm the projected return on investment in order to determine if the shift is worth it, based on your business situation.

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