How AI Enhanced, Democratized Data Helps Boost Productivity - Industry Today - Leader in Manufacturing & Industry News
 

April 13, 2023 How AI Enhanced, Democratized Data Helps Boost Productivity

As AI, ML and process automation (PA) move into the mainstream manufacturers are seeking practical applications to leverage data insights.

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By: Mike Guay, VP Cloud ERP Expert at Infor

Manufacturers are increasingly turning to smart manufacturing principles with the aim of turning the shop floor into highly efficient, data-driven operations humming with synchronized precision. Unfortunately, line-of-business managers, crew leaders, and shift supervisors—the shop floor heroes—often lack easy access to the relevant data needed to keep production on track.

Deskless manufacturing workers play a vital role in operational efficiency. Democratizing accurate relevant data— making data consumable for a wider population — gives these front-line users valuable decision-making insights.

Countless critical decisions are made on the shop floor every shift. The setting – often notoriously noisy and fast moving – is not conducive to careful, prolonged evaluation of new information. Fast-moving production lines require fast decisions and expedient action. On the shop floor, errors can be costly, eating away at profitability, wasting resources, and jeopardizing as-promised delivery times. Decisions based on guesses, a random exception, or anecdotal information can waste time and money.

Empowering Front-Line Users

The answer is to put AI-enhanced relevant data in the hands of every decision-maker involved in production. Modern digital platforms and ERP systems provide tools so users can apply advanced functionality to everyday situations. Manufacturing and shop floor functions include embedded artificial intelligence (AI) algorithms so front-line users do not need advanced data analysis skills. Low-code and no-code capabilities provide tailored role-based dashboards on mobile devices with up to the minute trends and predictive analysis, putting dynamic insights in the hands of machine operators, maintenance technicians, material handling engineers, scheduling clerks, and shift leaders.

Practical Applications

As artificial intelligence (AI), machine learning (ML) and process automation (PA) have moved into the mainstream, manufacturers increasingly seek practical applications leveraging data insights. Proof of concept projects are then rapidly scaled to capabilities which provide a timely, measurable return on investment. The shop floor offers many such practical applications for AI-driven insights. Routine processes are automated and streamlined. Only anomalies or exceptions are routed to decision makers for individual attention making those roles far more productive. Data flow is streamlined across functions keeping various teams apprised of real-time updates and needs. No one is out of the information loop and operations keep pace with evolving expectations.

Here are nine examples of how relevant democratized data helps boost productivity:

  1. Custom quotes and bill of materials. In the last decade, ERP systems have greatly improved their ability to manage make-to-order, engineer-to-order, and configured products.  Yet these types of orders consist of complex processes including automated systems for generating rules-based quotes and matching bills of materials. Changes to customer configurations and engineering changes must be incorporated as they occur.  Accurate up-to-the minute change/configuration information greatly reduces rework and/or customer returns.
  2. Projecting raw resources needed. Synchronization of production planning and availability of raw materials and inventory requires access to data and AI-driven predictive capabilities to prevent stock-outs. Accurate data helps procurement managers make sure the warehouse is stocked with necessary components. Too much inventory negatively impacts profitability and increases the risk of obsolescence.
  3. Accurate scheduling. Synchronizing production runs to fulfill customer orders depends on accurate production status as well as sales, delivery promises, inventory of raw materials, and machine capacity. Working with co-manufacturers or subcontractors also requires access to information which may reside outside the enterprise. Collaboration portals can help share information while protecting security.
  4. Strategic scheduling of the workforce. Data insights allow managers to evaluate and measure the performance of shifts and crews, identifying essential staffing requirements and tracking expenses. With the acute labor shortage manufacturers face today, careful scheduling of right-skilled workers is especially important.
  5. Workflows. Accurate data keeps operations running smoothly with no gaps, delays, or roadblocks.  Democratized data improves coordination among team. Sharing data on job status, equipment performance, and scheduling improves workflow and reduces interruptions. Reporting identifies trends and variables, allowing managers to delve deeper into influencing factors that can be improved. Better informed decisions are made, changes executed, and results monitored. Continuous improvement of workflows become a natural part of the system.
  6. Compliance and quality control. Managers need to track, monitor, and evaluate quality standards with a continuous feedback loop in place. As new products are introduced, specifications need to be easily updated and accessible to relevant teams. Regulation compliance, too, is critical in many industries and demands accurate reporting. Democratized access to data helps keep the details in view when and where they are needed most.
  7. Waste reduction. As manufacturers strive to be more sustainable, they place a high priority on reducing waste, including energy, water, and raw resources. Reducing scrap is essential. By improving consistency and quality control, fewer units will need to be scrapped or reworked. Access to data will help crews verify proper machine settings, consult knowledge banks for typical resolutions of issues, and verify proper specifications and variables.
  8. The call center. The aftermarket service operation needs real-time access to account and product details to answer customer questions about deliveries, service agreements, warranty status, and scheduled preventive maintenance. Service dispatch needs to access the location and availability of technicians to dispatch the right person to the right job based on geography, service level agreements, and urgency.
  9. First-call resolution. Field technicians at the job site need remote access to details on the unit, as-serviced history, inventory status of parts, and availability of replacement or upgrade units. A technician with the right data is seen as a trusted advisor and can often make sales in the field.

Rewriting Your Operations Playbook

Manufacturers considering deploying their first SaaS ERP solution or upgrading a legacy solution often compare various vendors and the functionality of their solutions.  A factor often overlooked is the  importance of the underlying technology platform which must include AI/ML capabilities, process analysis, low code / no-code capabilities, and easy to use data access and analysis across the organization.

When seeking modern ERP solutions, usability and easy access to data is critical. Furthermore, the democratization of data within and beyond an organization’s walls provides the opportunity to drastically increase operational efficiency to ensure customers are providing with the best possible experience or service.

mike guay infor
Mike Guay

About the Author:
Mike Guay started working with business systems decades ago – before they were called ERP. His career has spanned over 35 years as a user, a vendor, and system integrator. His expertise and experience with ERP systems crosses HR, Finance, Supply Management and technology. He has worked with customers in many verticals including Public Sector, K-12, Higher Ed and others. Prior to joining Infor in 2020 Guay spent over 7 years as a senior ERP Analyst at Gartner. He was the lead author of the first Cloud ERP Magic Quadrant in 2018. He has expertise in best practices for adopting MT SaaS ERP and how customers can maximize value from the investments Infor is making in products and technology.

 

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