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Spreadsheets can’t tell if you’re having a good day

spreadsheet
(Image credit: Image Credit: Pexels / Pixabay)

Most of us would rather be asked if we’re having a good day by a friend than our boss. Positive or negative experiences in our personal lives are usually fairly obvious, but events in the workplace often take more analysis, with many different factors at play. When manufacturers calculate factory shop floor metrics like Overall Equipment Efficiency (OEE), they need multiple pieces of information. If the data is not readily available, it’s difficult to say whether they’re having a good day or not.

Throughout the pandemic, manufacturers have needed to react rapidly to changes –  from sourcing new suppliers when factories were closed, to finding new ways to move goods during lockdowns to enabling remote work. Many could ill-afford spending days or weeks collating the data needed on which to base important business decisions, but they were hindered by a lack of what we’ll call “data agility.”

It’s hard to excel with manual spreadsheets

It is 2021, yet many manufacturers still collate data using spreadsheets. These documents are no longer fit for purpose as the volume of organizational data continues to grow. Manufacturers should not be wasting time and energy cutting and pasting data from different programs, stitching together reports by hand, and attempting to manually track subtle nuances in vast data sets. Many businesses talk about removing data silos but continuing to rely on spreadsheets all but guarantees silos will remain, making joined-up business strategies a distant dream. 

Manual spreadsheets also leave the door open for mistakes, whether that’s an incorrect formula, data being entered incorrectly, or a filtering problem. Even if they don’t cause errors, they still waste time and effort, with reports taking time to compile then often being out of date before they are read.

Five ingredients for a successful shift to MES 

The age of the spreadsheet is over. Manufacturers are quickly realizing that to remain agile and relevant, they must have data agility, or ready access to data that is current and accurate. A great way to achieve this objective is when operations are underpinned by a Manufacturing Execution System (MES). 

Here are key factors to consider that will help your MES implementation be a 

success. 

1. Create a single source of the truth

If quality processes, incoming parts and supplies, and worker assignments are managed by three separate systems, there’s a big risk the resulting pieces of data will be incompatible or report inconsistent results. Adopting an MES does not automatically address this issue on its own, but it creates a common interface and source to run and review manufacturing and quality performance. Manufacturers must use a data warehouse to create a single source of truth. By aggregating this data from various sources, the data can then be handled with greater consistency and presented to users in a standardized format. This way, no matter who is using the data, everyone will start from the same accurate, standardized base.

2. Ensure real-time visibility doesn’t slow things down 

An MES provides up-to-the-minute information, making it possible to know how a factory is performing at any given moment. However, manufacturers with large user bases need to be careful that user access does not slow critical operations down. Separating out the data utilized for reporting from what is required to run shop floor operations is a good start. Performing careful testing at the MES implementation stage is crucial in making sure that any performance issues can be addressed early on, giving a clear path to real-time visibility.

3. Ensure reporting tools are easy to access

MES adoption will be slow if a platform is not easy to use. Manufacturers must demonstrate that an MES gives employees a better way to prepare and distribute intelligence, saving them time in the process. It must be available on any device, easy for people to navigate and primarily run through clear, graphical screens. These should give people the KPIs and reports that can enable them to act quickly and decisively.

4. Fuel it with quality data

An MES is only as good as the data it runs on. Gathering, cleaning and contextualizing your operations data is crucial. A data warehouse shared by all operations will make data as accurate and standardized as possible, setting you up to gain access to accurate analytics, AI, and machine learning technologies to reveal insights, digging deep to find correlations hidden within complex systems. These insights can be priceless but won’t be possible without making the first upfront investment of implementing an MES as a starting foundation.

5. Make it open to as many people as possible

The more people who pull insights from a MES, the more valuable it becomes. It needs to distinguish between different sets of users or roles, and what their needs are – whether they’re a line worker or the CEO. Manufacturing Intelligence applications must be able to handle these different user profiles, giving them access to the relevant data and widgets they need, to get personalized insights.

Answering with confidence 

Ultimately an MES can provide manufacturers the foundation to start building a data-sharing strategy that can quickly identify whether you are having a successful day. The growth of Industry 4.0 means that data volumes will continue to grow at exponential rates. Manufacturers are considering “when” rather than “if” they move to an MES. Through greater data intelligence, they will then be able to honestly answer if they are having a good day or not. 

Mark Hooper, Technical Director, iBASEt

Mark is Technical Director at iBASEt and is experienced in defining, implementing and communicating all aspects of large enterprise IT transformation, modernization and integration programs.