Manufacturing businesses leverage several systems to manage their operations, starting with the ERP to support financials, resource planning, and other back-office functions. Most companies will have manufacturing execution systems (MES) to control the shop floor, but having other systems for supply chain, inventory, quality management, and customer relationship management depends on the size and technical capabilities of the company.
Regardless of how many systems are in place, the sophistication of the implementation, and the level of integration in place, the reality is that most manufacturing companies have gaps of work fulfilled outside of these systems. These workflow, data, and collaboration gaps are the business’s gray work and are often the source of inefficiencies, quality issues, and production delays. There’s an added risk for smaller manufacturers when gray work also includes tribal knowledge, with only a few people understanding the steps and tools used to complete a critical business operation.
7 signs of gray work impacting manufacturing businesses
How do you spot this gray work? It’s easy – look for:
- Spreadsheets used in workflows, reporting, or collaboration that one person maintains.
- Long email threads to make decisions, resolve issues, or complete recurring tasks.
- Manufacturing incidents, including production, quality, and safety issues, with no system of record tracking them and used to review process improvements.
- Employees entering the same data into multiple systems because they lack integration.
- Complex user interfaces built into enterprise systems that slow down employees and are too expensive or complex to upgrade and simplify.
- Analysts forced to pull data from multiple systems to perform forecasts and present their results days, weeks, and even months later.
- Onboarding new employees, especially in back-office, operations, and sales functions, is slow, complicated, and haphazard because of complex systems and the lack of documentation.
Manufacturing CEOs, CFOs, CIOs, and IT directors have one or more of these pain points and often similar reasons why they haven’t addressed these gaps. It’s too expensive. The vendors for our ERP and other systems are too slow. We’ve tried implementing upgrades and ran into project management issues. We don’t have a dev group to custom-build applications. We can’t get approvals for new licenses or replacement systems.
Employees on the front lines, such as plant managers and floor supervisors, often feel the challenges of gray work but may be unable to communicate the business impact in ways that drive change or investment. Equally problematic are back and front-office employees making decisions with inaccurate information because of data-entry errors or processing delays.
Find efficiencies by transforming from gray work to dynamic work
I will let you in on the biggest not-so-secret solution for manufacturing companies stuck in the gray work abyss. Many of these technology gaps are solvable, at reasonable costs, and without much technical expertise.
Digital Trailblazers have been addressing gray work with no-code solutions for a very long time, and I deployed my first no-code applications as a CTO over two decades ago. No-code platforms have layers of sophistication – apps developed with AI requiring no technical expertise, workflows designed by non-developer builders who are subject matter experts, and more sophisticated integrations that connect workflow and data from enterprise systems.
What can manufacturers accomplish with AI-enabled no-code platforms? Metso, a frontrunner in sustainable mineral processing and metal refining technologies, filled gaps in its order management and inventory management by developing an ecosystem of no-code applications. One global manufacturer automated their supplier management process, where managers can review each order and ensure that engineering, purchasing, and manufacturing activities can be completed in time.
These pioneers recognized the efficiencies of reducing gray work as part of an overall shift into dynamic work management, where workflows are flexible, multi-dimensional, and multi-stakeholder. They can be developed with no-code platforms, machine learning capabilities, automation, and integrations to enterprise systems.
Four dynamic work examples in manufacturing
It’s easy to spot the gray work, but you may be asking what dynamic work looks like and the benefits of developing these capabilities. Let me illustrate with three four examples.
1. Convert checklists to analytical workflows – Manufacturers using forms or spreadsheets to manage checklists can convert them to mobile applications with analytics capabilities. Instead of a safety checklist emailed to a supervisor, a mobile application walks them through the steps and submits compliance evidence. Instead of a worker filling out a PDF form about a manufacturing defect, they report the quality issue from their workstation and upload images highlighting the problem. Managers access dashboards highlighting common problems and alerting them of material issues in real time.
2. Empower plant managers to improve shop flow management – Large-scale manufacturers of homogeneous products will have an easier time procuring and configuring software to manage scheduling, forecast demand, or track the work in progress. Other manufacturers, especially ones providing customized manufacturing services or frequently reconfiguring manufacturing processes, may improve productivity and quality by providing no-code workflow and automation tools to supervisors and enabling them to tailor the shop flow as needed.
3. Optimize equipment maintenance schedules – Small manufacturers conduct periodic inspections of their plants and assess maintenance needs yearly, quarterly, and sometimes monthly. Smarter manufacturers collect IoT data, join it with data from their asset management system, and use machine learning to predict maintenance needs. The key to making this a dynamic workflow is providing supervisors with workflow tools to schedule maintenance more proactively and when it least impacts manufacturing schedules.
4. Expand supply chain scenario planning – Midsize manufacturers can’t always afford sophisticated supply chain management systems. Instead, the work falls into the hands of one person, or a few select people, to manage gargantuan spreadsheets and complex formulas. One slip-up in data entry or formula change and the recommendations can cost the company significantly. In a dynamic workflow, the spreadsheet is converted into a no-code application, integrations enable loading in supplier data, models are parameterized, model changes are versioned and tested, and forms enable the analysts to test different planning scenarios.
Efficiencies in these three scenarios stem from eliminating manual steps, reducing delays, and lowering error rates, but that’s not the complete story of the benefits. Manufacturers undergoing digital transformation to dynamic work capabilities can be more competitive, profitable, and operationally resilient.
The main obstacle is changing the mindset from, no we can’t do it, it’s too expensive, or we don’t have the talent – to approaches that enable more subject matter experts the empowerment, no-code platform, and professional best practices to create dynamic work management capabilities.
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Isaac Sacolick is President of StarCIO, a technology leadership company that guides organizations on mentoring digital trailblazers and building digital transformation core competencies. He is the author of Digital Trailblazer and the Amazon bestseller Driving Digital and speaks about agile planning, devops, data science, product management, and other digital transformation best practices. Sacolick is a recognized top social CIO and a digital transformation influencer, with over 900 articles published on his blog Social, Agile, and Transformation, and other sites. You can find him sharing new insights on the Driving Digital Standup or during his weekly Coffee with Digital Trailblazers.
