Manufacturing orgs must deal with the typical data that most organizations have to deal with - sales, marketing, human resources. On top of that, however, manufacturers must deal some specific data challenges absent from other segments of the economy:
Sensor Data - Many manufacturing orgs have sensitive equipment to monitor delicate and potentially dangerous processes. Failure to capture data from these sensors might mean, at best, a bad batch or defect in the manufactured product. At worst a failure might mean a safety or environmental catastrophe. The historical tracking of this data might also be important for regulatory compliance.
Large Image Data - Modern manufacturing processes often involve taking regular images of products (sometimes microscopic images) during production and QA/testing. These images have to be stored somewhere and referenced later and require a data architecture that can be used to store large numbers of extremely large files.
Employee safety data - Manufacturing is not just about machines and assembly lines - it is about real people working in sometimes hazardous conditions. Compliance with regulations such as OSHA require retention of a great deal of employee safety data without breaching employee privacy.
The flip side of the data challenges facing manufacturers is that good use of data has the potential to help manufacturing orgs significantly more than many other types of orgs because manufacturing orgs are strongly rooted in the physical world and thus are "hard data" organizations. Here are a few ways that data can uniquely propel manufacturing orgs:
Supply Chain Optimization - One thing the world learned from COVID and is relearning again in the wake of port closures and flooding is that supply chains are delicate. Data-driven models of supply chains can help manufacturers to weather short-term disruptions.
Shipping optimization - Manufacturers often spend large amounts of money moving physical objects to and from factories from around the world over highways, rails, and overseas. A careful look at logistics data can help manufacturers find optimizations and significantly reduce costs.
Inventory Management - Having access to real-time inventory and sales trends can help organizations to identify profitable and unprofitable products and plan production to meet demand in an efficient and cost-effective way.
Capital Expenditure Planning - Having good estimates on the lifetime of key equipment can help manufacturers to make intelligent decisions on when they need to spend money on upgrades and replacements.
With all these challenges and benefits to proper data management, how can manufacturers get started on using data to grow and optimize their organizations?
You have to walk before you can fly, and the first step in data strategy is to understand and secure the data you already have and use regularly.
Catalog all the systems where data is being entered. Some are obvious - ERP systems, accounting/finance packages, HR systems. However, some are not so obvious. Just like the universe is said to be filled with "dark matter", organizations can be described as being filled with "dark data" - that is, spreadsheets and databases that hold critical information that only a few people are aware of.
Have a backup and restore strategy for all data systems. For ERP systems that are hosted locally, this at minimum means having backups of databases, preferably backups that are saved off-network. For hosted systems this might mean requesting a data dump regularly of all key system data (work with your vendor on that one). Second, it is even better if you do RESTORES of your data to a separate server for backup testing and failover purposes. A backup that is never tested and restored does not really count as a backup. Countless organizations have been burned when they THOUGHT they had backups and then in a critical moment realized that the backups were corrupted and unusable.
Key spreadsheets should not reside on an individual's hard drive. Key data should be moved at minimum to shared network drives or to online document sharing systems like SharePoint.
Many organizations get "stuck" because their processes become very tied to a particular system that they use, usually their ERP system. This works fine for a while, but eventually the need to change ERP system version, or to change vendor entirely, invalidates a large chunk of existing work.
The ERP system might be most important system, but usually organizations have multiple other data systems that also house important organizational data. By building an independent data architecture you allow yourself flexibility in changing systems over time to better suit your needs.
Manufacturing organizations do well to build both a data lake AND a data warehouse, either in the cloud or on local storage. What is a data lake? A data lake is a more flexible long term data storage solution that provides storage for all sorts of raw data. It serves as the foundation for further data warehouses, data marts, and data visualizations.
Many data analytics projects seem to be successful at first because it is somewhat easy to produce a one-time data analysis or visualization. What inevitably ends up happening, however, is that there is later the requirement to keep the data fresh, to produce the same reports weekly or monthly or quarterly. If there is no serious automation effort this means that resources have to be spent frequently to recreate the same analysis. Instead, begin your data analytics journey with automation at the forefront.
Your data store should contain two sets of data - "fact" data - that is, tables or files that are as close to the raw data as possible. And then, the fact data should be transformed into aggregations and views that will support data visualization.
One of the biggest problems with spreadsheet-based reporting is that it is hard to balance security with the distribution of spreadsheets. Either spreadsheets are kept closely guarded by a small group of people, or they are emailed and forwarded widely until that day that someone types a wrong email address and releases the spreadsheet into the wild outside the organization.
A good data reporting and visualization platform ideally integrates with your existing authentication schemes to allow permission based on usernames or roles.
Data visualizations should not be overwhelming - a user should be able to grasp the key "point", or performance indicator, within a few seconds of looking at a dashboard. Charts and graphs should all support the main point of the dashboard and detail should be available separately for those who wish to dig deeper.
Learn more about developing effective business intelligence visualizations here.
Developing a long term data strategy and architecture is a long process and there is no better time to start than today. If you are looking for help on your particular data challenges, contact us and we would be glad to work with you to make better use of data.
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