In fact, companies in the mill products industries, like steel, paper, textile or cement, are leading the adoption of in-memory technology to handle big data issues. Surprised? You shouldn’t be. If you know the industry you can better understand why these companies are so keen on tackling IT challenges with big data.

First of all, this industry is already familiar with big data issues. In fact any company with a half way sophisticated manufacturing process knows how data can pile up very quickly. In fact, the manufacturing IT consultancy ARC Advisory Group estimates that machine generated data has increased at least 10 fold in the last decade.  And here is the big one, ARC estimates that just in the last 10 years, product and quality information has increased 1000 fold. There are many reasons for this, but the fact is the big data challenge is present in the mill products industries already.

“So what?” you may ask. If you have big data and are familiar handling it, then you also know what a gold mine it can be. Companies I have dealt with often employ teams just focused on gleaming insight out of this big data in production, maintenance, logistics, and financial areas. The problem was that the tools they were given often didn’t live up to the challenge. Like being asked to sew a t-shirt and being given a hammer, the tools are often not precise or performant enough for the job. Put the right tool in the hands of these teams and you will see serious business improvement.

What kind of effect can you expect from dealing with big data challenges? First you need to look at the corporate priorities. KPMG Capital published a survey earlier this year to determine where companies use data and analytics.

KPMG areas of business.JPG

This reflects nicely the priorities of the typical paper or metals company. Large investment in production machinery and equipment means a lot of capital is in the plant. This investment needs to work efficiently for a high return on capital invested or ROCI. But how good is the visibility into the efficiency of the machinery. Leading mill products companies have identified the plant as a target for improvement. Scenarios that improve visibility into machinery condition and performance allow them to do benchmarking and initiate cycles of continuous improvement. Even better than just visibility are scenarios using predictive algorithms.

Predictive maintenance is a very popular topic to investigate. But what good does it do to know when a piece of machinery will fail? The major benefit is that it gives you flexibility to schedule maintenance activities based on the current market situation and company strategy. What I mean is the longer you wait to maintain equipment the costlier it will be. That does not mean every maintenance activity needs to be done immediately. Often completing a high priority customer order needs to be prioritized over maintaining production efficiency. Or sometimes it is the other way around. Predictive maintenance allows you to bring more business context into your decisions about the plant. McKinsey recently looked into the impact of advanced analytics like predictive maintenance on operations.

In the McKinsey Quarterly article “When big data goes lean”, the authors identified that metals was the industry with the highest potential benefit from applying advance analytics. Companies who have applied complex algorithms to business processes have experienced improvements in metrics like profit margin, productivity, EBITDA, and overall equipment efficiency.

Big Data goes Lean Exhibit.JPG

Questions you have never been able to answer before.

Answering seemingly simple business questions is often not so simple.

  • Why is it that deliveries are always late when scheduled for Friday afternoons?
  • What is the quality of the paper rolls being produced today, and which customer orders can be best satisfied with this batch?
  • Do I have visibility into all inventory, even in transit or at different DCs or plants?
·      Questions like these often are not answered in standardized corporate reporting platforms. Often until I look and play with the data, I don’t even know what I am looking for. What is the source of declining production? Answering such a question will begin an avalanche of follow-on questions and
that is why performance is so important.
Only a fast system will allow for iterative ad-hoc reporting and analysis to gain an understanding of the root-causes of business issues. Submitting a new view of data and having to wait more than a few seconds is a drain on productivity and frustrating for users. Just imagine using an internet search engine today that takes more than 10 seconds to respond and you will understand the issue.

There are also many interesting areas where big data plays a role and companies ask us about.

  • Productivity of fraud mitigation activities
  • Analyzing and segmenting of customers by profitability
  • Identifying reasons for glitches in the supply chain in real-time
  • Segment dealers to achieve best margin and revenue for both companies
  • Braking down my COGS to identify opportunities for improvement
  • Analyze which projects are within budget and predict completion date and cost overruns
  • Improve my forecasting for commodity prices
  • Stronger adherence to strategic targets with role based interfaces

You are probably thinking that these use cases are for large enterprises. Our experience is that about 1/3 of projects are being done in small and medium size enterprises. Modern tools to handle big data are easy to install and easy to use even for occasional users.

So after looking at it a bit more closely you can see why companies in the mill products industries like steel, paper or cement producers, are leading in the adoption of in-memory technology to support big data challenges.

For more real live use cases on the ust of analytics and big data check out hows HBIS Tangshan Iron and Steel saved 600 million from improved equipment management and an 80% reduction in inventory. You and also view presentations from the SAP Metals and Mining Forum 2014 here.

Sources and Links:

ARC Advisory Service

“Going beyond data analytics: achieving actionalbe insights with data and analytics,” KPMG Capital, January 2014

“When Big Data Goes Lean,” Rajat Dhawan, Kunwar Singh, and Ashish Tuteja; McKinsey Quarterly; Q2, 2014

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