Industrial Information Portals & Decision Support Trends Drive Needs for More than Spreadsheets

5 min read

Jun 7, 2018 2:00:00 PM

It is well known and accepted that, to manage a process, we need to measure the results of that process.  If we start measuring the production output of a machine, the people responsible for running and maintaining that machine are more likely to pay attention and take the necessary decisions to improve its performance.

Every business with any focus on continuous improvement has some system that works for them and has evolved as they have traveled their journey. Some use paper, some use paper and spreadsheets, others use custom solutions, and others use some form of off-the-shelf software.

For over 20 years we’ve helped users get the digital data they have into these various systems, but in the process of doing that, we’ve heard some things from them and seen some patterns we will share in this blog post.

Market Demands Increase Need for Scalability in Continuous Improvement

Decision Support Need to be Scalable for the FutureOne of the things we’ve heard is that accelerating demands from our users’ markets are forcing the Plan-Do-Check-Act cycle to go around faster.  What happens to their system when they have to move faster, or scale up to more machines?
What happens when they decide to monitor all 53 machines on the shop floor?  What about the quantity of rework or rejected product on each machine?  What about the electricity or water used by each machine?
Very quickly, a measurement-focused organization will need a system to handle the volumes of data being collected, calculated and used to support decisions being made in near real time.
A system of this nature is known as a Decision Support System: an information system that supports business and organizational decision making activities.  Which leads us to the most common system we all have on our computers.

Spreadsheets, a Common Approach

Beyond Spreadsheet for Decision SupportIt’s not uncommon for many organizations to turn to a set of spreadsheets.  However, as tempting as it is to fall back on a spreadsheet-based “Decision Support System”, there are a number of reasons most users of spreadsheets eventually feel limited by the tool every professional loves and knows.

Multi-User Challenges

Only one person can be in the workbook at a time, even when you store the workbook on the network.  The other person receives the dreaded “This workbook is in use by Joe Smith, would you like to open a read only copy”.  You call Joe and he’s gone to a meeting, leaving the workbook open by accident.  Now you have to come back to do your work.

Accuracy of Data

Data in the spreadsheet is usually manually entered, or imported from a CSV, or copy/pasted from some other digital reporting system.   The risk of human error is high.  The people usually doing this work have multiple other demands on their time, and all it takes is one untimely distraction, and the potential for loss of validity of information is high.


Although you can restrict usage, and ask your users to mark when they make changes, or fill out a revision log, there is no clean, easy way to know who did what in a large spreadsheet workbook.
Comments on cells are nice, but what if you need to easily see the history of changes made to a data point over time and how made them, and any comments they made when making the change?  You are dependent on an over-worked human to insure they always get it right.

System Breakage

Everyone knows how to edit a spreadsheet.  Problem is, everyone thinks they are a spreadsheet expert.  Reality is, the more complex the number of tabs, interlocking formulas in a spreadsheet (or worse, between workbooks), the higher the chance of problems.
Without any accountability for who made changes, one accidental or well-meaning formula change results in “#REF” errors in cells all over the system.  Although you can lock the workbook from editing, the chance still exists, and without an audit-trail, you are left with the risk of a mess.

The Multiple Data Source Bowl of Noodles

Another challenge we’ve heard about is the varied and multiple data sources.  Because we focus on digital data so much, it’s easy to forget that in many operations, digital data sources such as PLCs and Process Historians are a small part of what they have.  Other data sources we hear about are:
  • Databases
  • Other spreadsheets
  • Manual data entry
  • ERP systems
  • Lab systems
  • Energy monitoring systems
  • Facility management systems

Most conversations we have involve stories of the pain users go through in order to gather up the required data.  We’ve even heard of people having to export data from a digital system as a CSV file and then load that into Excel so that they can then relate that data to information they pulled out of a process historian as a CSV file.

A characteristic a solid decision support system should have is the ability to pull data from multiple data sources, and support manual data entry like a spreadsheet, and treat all the data equally.

Cost & Time Realities Often Interfere with Progress

Measuring Cost of EntryMany companies have big dreams, and big visions, but when they look at their options to move past spreadsheets, they realize the costs to receive any value quickly run into the 10’s or 100’s of thousands of dollars, and they will spend at least 6 months in implementation.  With competing demands for capital and staffing challenges due to generational change, they often quickly just decide to deal with the limitations of spreadsheets and keep using them.

A key overall characteristic of a leading Industrial Decision Support system for manufacturers who are ready to move off of spreadsheets is reasonable cost of entry with license prices in the thousands of dollars and time to implement, resulting in time to value, measured in days or weeks, not months.

The Ten Trends

So we’ve talked about some of the key things we’re seeing and hearing.  We’ve put together a paper that covers these 10 trends in industrial decision support systems and information portals:

  1. Systematization
  2. Promoting Self-Service
  3. Employing software robots to gather the data
  4. An understanding that industrial data and business data are different but have to work together
  5. Automated calculation bridges matter
  6. Perspective and context without custom code
  7. Embracing manual data entry
  8. Seeing and understanding for all
  9. Encouraging sharing and collaboration
  10. Enabling the bigger picture

Download 10 Trends Whitepaper Now!

John Weber
Written by John Weber

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