Volume 5 Issue 11 November 2023

In this Issue

Welcome to Industree 4.0 for November, 2023, exclusively sponsored by SAP.



Join the SAP packaging and paper community in Vienna!

June 17-19, 2024 – Vienna, Austria

The International SAP Conference for Paper and Packaging is back with a new look for 2024. Join SAP, their partners and customers in Vienna, Austria for the SAP for Process Industries and Natural Resources Conference, June 17-19, 2024.

Are you in the paper and packaging industry? Perhaps you make cardboard, paperboard, shipping sacks, or paper bags? Are you trying to find out how technology can make your mill more efficient and flexible? Are looking to achieve real-time insights into sales and customer demands? Is supply chain resiliency a priority for your chemicals business? Looking for guidance on green and sustainable mining? Whatever your focus, join SAP in Vienna to hear directly from established customers already tackling these challenges.

This is the only face-to-face community meeting that provides practical content specific to your sector and the opportunity to hear lessons learned from other industries with operational and process synergies.

“This is the premier global SAP event focused on the building materials, chemicals, metals, mining, paper and packing industries. Join 450+ attendees across 5 industries and 30+ countries for three days of strategic thinking and tactical planning” - Stefan Weisenberger, Global VP, Process and Natural Resources Industries, SAP

What to Expect?

  • Dedicated industry tracks focusing on the topics most important to you

  • The opportunity to meet with and hear from other businesses with similar operational and process synergies

  • A case study-led agenda. Learn how to improve your operations by seeing exactly how someone else has tackled the challenges you face

  • Organise one-to-one meetings with SAP and established users you are interested in learning from

  •  Vet a wide range of SAP partners with specific expertise in your field

  • Fun! Dedicated time to socialise and have those important conversations

At this industry event - you will hear how the industry is leveraging innovative technologies, how your peers are challenging the status quo, and connect with SAP leadership, customers, and partner ecosystem. Also designed for the building materials, chemicals, metals, and mining industries, you can expect dedicated content specifically for packaging and paper companies and the added bonus of hearing lessons learned from other industries with operational and process synergies.

Hot Topics for 2024

  • Supply chain resilience: From frequent supply shocks to changing regulations, explore how you can meet demand and mitigate risk while controlling costs.

  • Customer centricity: Learn how you can be more responsive to customers from individualized order promise through manufacturing and delivery – all while considering costs and margins

  • Sustainability: discover how to comply with the latest financial and ESG regulations and improve your sustainability to reduce risk, attract investors and drive new opportunities.

  • Market and price volatility: Manage rising costs and changing market demands with better visibility across your business operations, financials and assets.

  • Assets and operational excellence: We’ll discuss how maximising asset health and performance with real-time insights and mobility can drive efficiency and margin.

  • Innovation and automation: Hoping to better predict equipment failure or demand forecasting? Hear how AI and other technologies can support you, your team and processes.

Did you miss the SAP conference for Paper and Packaging in Madrid 2022? check-out the session recordings from our keynote and track sessions. See the recordings here: 2022 content. Check out a short video summary.

Registration is Now Open

Mark the dates in your diary. Register to get the latest information and updates on the agenda and speakers.

We look forward to welcoming you to Vienna.  

Building I4: Level 2: Data Analytics

By Pat Dixon, PE, PMP

Vice President of Automation, Pulmac Systems International (pulmac.com)

In the last article I mentioned the digital landfill, which is the process data historian where we save the overwhelming volume of data generated. Why are we saving this data? What do we plan to do with it? 

An August 2022 article in the Wall Street Journal “International Paper’s CFO on Leveraging Disruptive Finance” interviewed Tim Nicholls, CFO. In this article, Nicholls stated:

"Technology is helping us improve our business model, our processes, our customer interactions, and much more. Access to large amounts of data has increased our capabilities for decision-making at every level of the organization. At IP, we have built cross-functional teams focused on using advanced analytics to identify patterns and streamline our operations. Our teams take the opportunities presented to us through advanced analytics and find ways to create unique value, with the ultimate goal of bringing scalable solutions to the enterprise."

They are not alone in recognizing the value of data. Throughout industry, investments to turn data into gold proliferate.

The term for this application is Data Analytics.

As with many terms in industry today, there are differing interpretations. Many vendors sell solutions labeled as data analytics, yet they can have very different features and functions.


To flesh this out further, some data analytics solutions on the market fill a particular niche or deliver a subset of the full scope of data analytics. A full data analytics solution includes the following:


Data originates in many sources. Most commonly in industry, data is stored as time series in a historian. A single historian may consolidate data from several control systems or digital devices. In our industry, we also have QCS systems which may be an independent data source. We also have lab data that could end up in SQL tables. We may need to load data from Excel. We could also have multiples of the above in a facility, and certainly across an enterprise.

A data analytics solution must make connection to these data sources possible. Ideally, we want the data in realtime so that we can see changes as they occur. 


This is the meaning of the term Big Data. Big Data refers to a variety of data sources, such as corporate research connecting to data historians at each mill. This is enabled by the internet connectivity of the 4th industrial era. A data analytics solution needs connectivity to big data.


It is hard to analyze data that you can’t see. The volume of data we generate in industry cannot be comprehended by looking at it point by point. The desktop tools that we commonly use are not meant to handle the volume of data we generate. Excel has a maximum of 1,048,576 rows by 16,384 columns. If you stored 6 months of data for a single sensor in a row at 1 second frequency, that would require 15,552,000 rows. Visualizing all of that data requires a full toolbox of techniques. We are all accustomed to seeing time series trends, but that alone is not sufficient for discerning what all of the data together is telling you. It requires tools that can organize and relate data in a presentation that reveals answers.


Not all data is good data. To be useful, there needs to be tools to identify and remove bad or irrelevant data. Doing this point by point is practically impossible. Data analytics solutions need a rich set of filtering and condition detection tools, and these tools ideally should allow applying data cleansing in a comprehensive way regardless of the timeframe of the data.


Once we have pre-processed the data to yield a valid dataset, analysis tools can find correlations that yield answers. Prediction models can be built to show what will happen under different conditions. Statistical techniques show whether there is a valid cause and effect relationship. There are a multitude of techniques for analyzing data. A data analytics solution should offer a big toolbox for analysis.


The results of data analytics aren’t useful unless they can be shared with the right people. Often, it takes a team to conduct the pre-processing and analysis. Having a collaborative environment for doing the work and sharing the results ensures everyone has the current version, instead of hunting through emails and hard drives for a file. Having enterprise wide visibility in a common environment means realtime information with minimal additional effort. Internet connectivity in the 4th industrial era facilitates this collaboration.

In the 4th industrial era, data analytics goes beyond what can be seen in a single control loop. It is a platform for integrating Big Data, meaning a variety of sources, with a rich toolset and collaboration, so that the digital landfill can yield nuggets of gold. 

Supply Shortages and AI

Jan is bemoaning a lack of tomatoes in the lead article this month. This is representative of a problem I have seen elsewhere.

Since 2020, construction projects have been beset by lack of critical items. In another column I write in another one of our publications, I commented recently that I never saw "Force Majeure" in any place but contract language up until a couple of years ago. Now it has become the lingua franca.

I remember a little over forty years ago, I had joined a company and was receiving orientation in several departments. In the purchasing department they told me how they could now track late shipments. The purchasing manager showed me on his computer screen that it now showed late shipments. For a very brief moment, I thought they had some devine way to track materials. About three questions led me to understand that a human had to call the provider, ask the status of the shipment, and then manually enter it in to the computer.

I could do the same thing with a pencil and paper, it just wouldn't be printed on "green bar" (does anyone even know what "green bar" is any longer?).

However today, with today's computers, software, Internet, and AI, we can do that which was my brief flash four decades ago. We are able to know before it is a problem where the delivery problems are.

Now, you might say, "Hey, Jim, you just threw AI in there because it is the buzzword of the day." No, you are wrong, for if I build a database of suppliers' performance, AI will be able to alert me way ahead of time of potential delivery problems. Build the database out further, and AI will be able to offer alternative suppliers in time to not affect my needs.

This is powerful.

Why AI Is Not Your Enemy

By Damien Pacaud

There has been quite the amount of chatter within the Supply Chain and Quality Control spaces about the implementation of Artificial Intelligence (AI) to support and streamline manufacturing processes. However, in working to drive efficiency, transparency and compliance, the argument for the indispensable human touch of on-the-ground inspectors still weighs in strong.

Read the full article here

The Ethical Implications of the Internet of Things (IoT)

By IoT Business News

In the landscape of modern technology, the Internet of Things (IoT) stands out as a revolutionary paradigm, embodying a network of interconnected devices that communicate and exchange data seamlessly. This burgeoning web of devices, ranging from smart thermostats to autonomous vehicles, has the potential to reshape our daily lives, enhance efficiency, and open new avenues for innovation. However, the swift advancement and integration of IoT into various sectors also raise profound ethical questions that warrant careful consideration.

Read the full article here

Modernising warehousing in 2024

By Allan Tan

If you ever watched the Indiana Jones: Raiders of the Lost Ark movie, towards the end, the supposed Ark of the Covenant was placed inside a wooden crate and then rolled into a warehouse to disappear. The movie was set in 1936. Fast forward to today, if someone in the US government were to requisition the crate containing the Ark, I wonder if it is at all feasible to do so - at least promptly.

Read the full article here

Understanding the Difference Between MLOps vs ModelOps

By Nikolai Schiller

In the ever-evolving landscape of artificial intelligence (AI) and machine learning (ML), two terms often come up: MLOps and ModelOps. While they may sound similar and are related to managing the lifecycle of machine learning models, they focus on different aspects of the process. 

Read the full article here
Industree 4.0 is exclusively sponsored by SAP