Manufacturing is still burdened by its past, with practitioners hesitating to cross the chasm from manual to automated processes.
The problems start right at the outset of the manufacturing process. A recent study by analyst Cindy Jutras found that, on average, 26% of manufacturing orders today are manually entered. This hinders productivity as it is a very time-consuming and error-prone. And the inefficiencies continue throughout the manufacturing flow.
A late 2017 story in ZDNet said, “So far, there is precious little connectivity between the older world of ERP and this new frontier of IoT.” Arguably, little if any progress has been made in the last two years.
But why is this so?
The Underlying Problem
Enterprise resource planning (ERP) systems have been on the scene for several decades now, creating and consuming data on a range of functions, from the production-floor to the finance office.
As ERP has evolved, many related functions are still handled without the aid of automation. But, with the rise of IoT, an entirely new frontier opened up. Still, even with the advantages that IoT brings to so many industries and enterprise applications, it has not been embraced widely in manufacturing.
And, in manufacturing, there remains a gap between perception and reality: Despite the increased speeds that IoT provides, only 42% of surveyed manufacturers perceived strong value in the implementation of IoT technology that facilitates the autonomous exchange of data.
While 58% of the most advanced companies can deliver IoT data through their process automation systems, only 19% are able to correlate data with ERP systems. This low adoption rate is a contributor to manufacturing’s position as returning the lowest revenue per employee of all industries.
What Can Be Done?
ERP, when armed with IoT data, helps organizations gain vital business-related insights instantaneously. The continuous stream of data enables enterprises to carry out real-time analysis, which helps them gain actionable insights, make tactical decisions quickly, and increase revenue generation significantly.
It’s also been proven that integrating IoT and ERP can improve data availability, which is the bedrock for improving operations. All of that is easy to say, but let’s see how it’s done.
The first step in ERP-IoT integration is typically machine monitoring, a prime catalyst in helping companies gain greater operational visibility and create an environment for continuous improvement.
Let’s look at phases of IoT integration:
Phase 1: Simple Monitoring
This typically involves connecting an IoT device to a production line or machine so that it can capture cycle data and machine state data (uptime and downtime).
The primary goal during this first stage is to validate the accuracy of the data
The objective is to gain real-time intelligence, which enables manufacturing teams to identify performance issues as they occur and to make adjustments to optimize production
Phase 2: ERP Integration
By integrating machine monitoring processes with a company’s ERP platform, teams bring data collected from IoT sensors into your ERP system to gain greater visibility and operational efficiency.
Note: As manufacturers’ IoT and ERP systems often speak different languages, integrating the systems can be a challenge.
Phase 3: Advanced Monitoring
Advanced monitoring is simply the act of monitoring and capturing a larger set of signals from manufacturing equipment; one aim is to understand which machine or process stopped, causing system downtime.
This phase might also encompass capturing sensor readings, temperatures, and pressures to pinpoint the sources and reasons for downtime and to enable near real-time resolution.
Often in this phase, Continuous Improvement teams gain the data they need to make iterative improvements that translate into cost savings.
Phase 4: Big Data
Gathering data is a starting point, but ultimately you need to move beyond operationalizing it and on to visualizing what it means. Teams need to ask, for example, if they are addressing the most critical issues. They also need to understand if they have the necessary information to make informed decisions
The goal of capturing data is to determine overall equipment effectiveness, or OEE. OEE is the gold standard when it comes to measuring manufacturing productivity by identifying the percentage of manufacturing time that is truly productive.
Measuring OEE is a good start. But measuring downtime and the causes of suboptimal performance can reveal issues that you wouldn’t discover otherwise—for example, the impact of equipment and process interruptions, the contributors to slow speeds, or issues relating to raw materials.
Implementing big data analytics across a framework such as Six Sigma can give your Continuous Improvement team the tools to fuel change. Getting greater insights into how each phase of an improvement program is working—and how efforts for improvement impact all other areas of manufacturing performance—is critical to continued improvement.
Automating Order Processing
For an illustration of IoT-enabled ERP, let’s focus on order processing.
Automating the process of reordering by using IOT sensors to track inventory and materials is one goal. It can let you know, for example, when you’re getting low on a required material. And, if you want to automate the process, you can move beyond “manual IOT” to set a threshold that says, in essence, “If we go below the threshold, order an additional X amount.” With appropriate processes in place, that order will automatically be placed, replenishing the material so that you’ll never be out of stock.
Eventually, with the aid of AI, the AI will alert you even before an IOT sensor alerts you.
Using Complementary Technologies with IoT
IoT will help companies achieve the ideal of a fully automated manufacturing floor. And yet, no one technology is all-encompassing. “Complementary technologies” are critical to automating parts of the process that are beyond the range of IoT.
For example, optical character recognition (OCR) technology can scan incoming emails and communications from customers to fill order forms automatically. OCR, at its best, it’s a means for order processors to “fill in the blanks” on orders.
With analytics in the mix, teams from both manufacturing and sales departments can start to see trends – such as what buyers are ordering or not ordering, or how seasonal changes affect ordering patterns. Essentially, IoT and analytics work hand-in-hand — yielding the greatest insights into what is happening on the shop floor.
Applying IoT in Other Manufacturing Processes
Order processing is just one component of an end-to-end manufacturing process that benefits from the integration of ERP and IoT. Here are two other components in which IoT plays a key role:
By placing one or multiple sensors on a machine, you can gather metrics to track the performance of the machine at the moment or over time. The highly granular views of machine performance you gain are virtually limitless in their ability to indicate a potential failure, or perhaps the need to “end-of-life” the machine – all in the name of managing assets, controlling capital costs, and simply keeping manufacturing processes operating optimally.
Supply Chain Visibility
Using IoT for supply chain visibility can alert manufacturing teams if materials are running low, and can do so in time to find the optimal supplier under the circumstances: whether from a traditional supplier with higher prices, for example, or from an alternate supplier with lower prices but longer lead times.
IoT is a Tool for Renewal
Newer manufacturing assets may come equipped with a range of built-in sensors. However, many organizations continue to use production machines that have served them well for years, or even decades, and they have recovered their capital costs many times over. While a cost recovery analysis for a new, IoT-ready machine might be warranted, retrofitting an existing machine with sensors could be the more prudent option.
And so, in a sense, IoT acts as a tool for renewal of assets, and it can be operated profitably (and with routine maintenance and updates) for years to come.
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