Whether they manufacture metals, building materials, paper products, or packaging goods, growth is paramount for midsize mill products companies. And while many are generating growth, how meaningful it is may be questionable. According to research conducted by Oxford Economics in collaboration with SAP, 89% of mill products respondents work at organizations with positive revenue growth, but about half of these (44%) say this growth is less than 5%. And 71% saw profits grow in 2023—with just 22% experiencing profitability over 5%.

Midsize mill products businesses are leaning into digital transformation to address major supply chain complexities, shifting customer expectations, and growing environmental, social, and governance (ESG) concerns. But integrating the wide range of disparate data across technologies they’ve accumulated over time is not an easy task. To achieve their strategic goals, they must focus on three factors:

1. Finance and supply chain connections
2. Expansion into new markets using technologies with built-in localization
3. Adopting technologies and processes that integrate data across lines of business

Mitigating risks to achieve growth objectives

Like most midsize organizations in our survey, the top business objectives for mill products respondents are growth focused: new customers and revenue growth. Many also prioritize innovating with new products, services, and business models. When asked about the service or support capabilities they are focused on to increase competitive advantage, the respondents cited:

• Fulfilling and delivering services efficiently and on-time (65%)
• Profitability of products (72%)
• Expanding their geographic footprint (55%)

Cloud is the springboard for business benefits

The successful rollout of nearly all technologies depends on strong, reliable business data. Today, data integration allows many mill products executives to achieve highly sought after business outcomes like creating innovative business models at scale (77%), delivering more personalized solutions and experiences to customers (74%), sharing knowledge and ideas to drive continuous innovation (74%), and collecting and using employee productivity data to improve efficiency (74%). Having a single source of truth—a reality made possible with well-integrated data and driven by cloud ERP—will be essential for mill product companies going forward. 

However, only 54% of mill product executives say their company has adopted cloud solutions today—far behind the adoption rate of other industries (73% on average). This presents a valuable opportunity for mill products executives, as others in the sector that have made the switch report meaningful benefits. Improved employee experience (48%), improved agility (46%), reduced costs (42%), and optimized priorities (33%) are just some of the improvements milling executives with cloud in place are seeing.

AI adoption is similarly low compared to peers in other industries:

• Only 13% of mill products companies have implemented AI (vs. 25% survey average). 

But with over half expecting to put AI into play in the next 12 months (54%), optimism is understandably high. Executives believe its biggest impact will be on their products and services. However, mill products executives stand apart in their belief that AI will also have significant impact on procurement and networks (81% vs 66% average), supply chain management (75% vs. 64% average), and manufacturing (74% vs. 55% average).

To read the full report register here.

I recently learned of a failed data analytics project. In this project, the objective was to predict strength properties at the reel. When the vendor concluded their work, they told the mill that adding dye would make their sheet stronger. The mill lost confidence in the vendor, for obvious reasons.

I expect that dye addition did indeed correlate with strength, but for the wrong reason. The mill understood that when they made stronger grades at higher basis weight, they use more dye. Therefore, the relationship was not causal. It was coincidental.

A paper maker understands this. A data scientist who is only looking at data and doesn’t know first principles of papermaking doesn’t know this.  

Any data analytics work requires subject matter expertise. Otherwise, the results can have no practical application because of the void in process understanding. What we call first principles are known relationships that cannot be ignored in data analytics. First principle models don’t always yield a precise result that matches the process, but it does have a relationship that explains how the result will move when inputs move. Using data to calibrate the first principle model is the right way to go.

There are also cases when no first principle models exist. Many years ago Dr Bill Scott at Miami University asked me to help with data analytics on wet end chemistry. Wet end chemistry can be mysterious, so data analytics techniques may yield results that help establish relationships. A design of experiments (DOE) can be conducted so that the impact of each input can be isolated. However, this does not happen in a void. Subject matter expertise is required to develop a DOE. Otherwise, the DOE will either use every process input (a practical impossibility) or inputs that are not germane (such as dye).

There are kitchen sink data analytics approaches in which you throw all the data you have into an algorithm and assume artificial intelligence will make sense of it. However, garbage in equals garbage out. First principles need to be incorporated into data analytics to yield useful results.  

I couldn't agree more with the Oxford Economics/SAP lead article above. The growing complexities we are seeing in mill products, including pulp and paper, demand more monitoring, more process control and more reporting than we have ever seen before.

It is easy for customers, the general public and legislators worldwide to demand more of us. Complying with these stakeholders' expections is taking us to levels of information collection and dissimination we have never seen before.

And I am not here to lament about the "good old days." I may even be called agnostic about these requirements. What I do know is that these are the costs of doing business going forward. Companies will comply or shut down.

If you have dragged your feet when you consider information systems of any type, those days are over. I'll be speaking more about these in my SAP Vienna talk. Unfortunately, I will be providing that as a recording, my physican team does not want me traveling right now. However, I am going to provide a way you can ask me questions later. Looking forward to sharing my thoughts with you in Vienna.

We’ve been talking about Industry 4.0 for years, maybe even a decade. Many companies, however, are still just beginning to automate, utilize sensors and IIoT, and collect and analyze manufacturing data. 

This year the IoT market is set to exceed $1.3 trillion, showing the scope of its reach. This growth brings a critical challenge in the form of having to provide secure access to these myriad devices.

The past few years have seen a sharp rise in cyber-attacks targeting vital infrastructure and security products worldwide, focusing on Industrial Internet of Things (IIoT) devices like security cameras. An IoT security program is essential to improving cybersecurity.

I’ve recently written about AWS, IBM, and Microsoft, all giants who’ve announced supply chain product expansions over the last year capitalizing on AI, the Internet of Things (IoT), and other digital technologies.