8 minute read 16 Jul 2021
Engineer checking machine

How manufacturers can optimize their processes using digital tools

By Matthew Burton

EY EMEIA Consulting Center Partner and Digital Operations Leader

Supply chain leader with over 20 years experience in industry and consulting. Focused on digital transformation.

8 minute read 16 Jul 2021

Digital twins can help in optimizing processes in the manufacturing industry.

In brief
  • Massive shocks to demand and supply shone a bright light on the areas of manufacturers’ operations that were inefficient.
  • Taking a deep, end-to-end look at processes is key to uncovering redundant activities that contribute to complexity and undermine overall process performance.

Every successful manufacturer has processes that work — or else the company wouldn’t be in business. That said, could those processes be better? Yes, they could definitely improve. While effective, many processes in a typical manufacturer do not run optimally. They tend to include ad hoc activities, ongoing tweaks and adjustments, fine-tuning, nonstandard practices, work-arounds and other shortcomings that add complexity, waste, redundancies, time delays and inefficiencies. These can be hidden away during good times but rise to the forefront when the processes are stretched and challenged. 

That’s exactly what’s happened during the COVID-19 crisis. Massive shocks to demand and supply shone a bright light on the areas of manufacturers’ operations that were inefficient and on those that prevented a flexible response to these unforeseen changes. 

In typical processes, from demand planning through planning and scheduling, all the way back to materials purchasing, individual process issues get magnified across the supply chain. Actions in subsequent process steps attempt to compensate for these issues, often only making matters worse. In the end, tiny errors at the start of the supply chain process ripple out with much larger impact, compromising manufacturers’ ability to deliver, let alone adapt and meet new types and waves of demand. It’s the classic supply chain bullwhip.

As the world eventually moves into a recovery that’s likely to be punctuated by further disruption and localized restrictions, manufacturers will continue to find that they’ll need to be able to flex their supply chains in a way they’ve never had to till now. Doing so will require them to optimize their processes, with the help of digital technologies, to make them more resilient and responsive.

Improvement opportunities are hiding in plain sight

Manufacturers have myriad opportunities to optimize their processes. A good example is the typical production line. Just one part of the effective shop floor process is the use of very clear centerlines — specific settings every machine should adhere to in order to run properly. A centerline enables an operator to set the machine once and run the batch. But many manufacturers instead still rely on their operator’s experience to set up a machine for a particular product batch. And the operator ends up continuing to adjust the process throughout the run. 

Making matters worse, the operator in the next shift has his own way of setting up and running the machine. As a result, the settings — and, hence, the machine’s output — will not be consistent and reliable from person to person and shift to shift. The production line, overall, will technically be “working.” However, by replacing all those micro-interventions and redundant efforts with standard settings, that are mathematically calculated and applied to optimize the machine’s performance, the company could significantly improve the line’s throughput and yield.

The same type of discipline applies to other key processes, such as identifying the equivalent of the “centerline” for procure-to-pay or order management. So, how can the company set up its system so that every time it needs something (for instance, to buy some more packaging materials), no one has to touch it? How can it set up the system so that it places the order on its own? This system should ensure that the manufacturer gets the product they need, when needed, and in the right quantity and format for its production line — every time.

Optimized processes matter because they’re one part of creating greater resilience. When a shock hits, it brings a lot of complexity for a manufacturer to deal with, in a short period of time. Managing that complexity is much easier if it uses well-run standard processes instead of a complex process that relies on people’s knowledge and ongoing attention just to keep it running.

It all starts with simplifying and standardizing the process

The first step in optimizing a process is to determine how the process really operates versus how it should run. In many companies, that can be a wide gulf. Taking a deep, end-to-end look at the process is key to uncovering redundant, unnecessary activities that contribute to complexity and undermine overall process performance. With such an understanding, a company can simplify and standardize the process, eliminate redundant steps and automate parts or all of it so it executes consistently with minimal intervention. In addition to enhancing process performance, automation frees up people to more effectively deal with exceptions and unforeseen challenges — such as a global pandemic throttling their supply chain.

But leading manufacturers aren’t just stopping there. They’re taking the next step to develop a digital twin for key processes (see figure 1). A digital twin gives a manufacturer a virtual model of a process that shows how the process is working — for e.g., the performance of specific production lines, or a view of the end-to-end supply chain with the state of inventory, current demand picture and potential supply bottlenecks. This kind of visibility is vital to not only being able to quickly address potential issues before they ripple into a problem, but also to understand how the company is positioned to respond to a sudden disruption.

Optimizing processes

Figure 1: Optimizing processes.

Even more powerful is a digital twin’s ability to generate insights into how certain events might affect a company’s business. By simulating and modeling impacts and potential responses, a digital twin can suggest the best courses of action for the company to take. This is a huge bonus for a company during times of great uncertainty and can help leaders make much more informed decisions about how to proceed. So, for example, if a trading border shuts down, or if a plant is closed or if a line breaks down, the company will be able to pivot and adjust the supply chain because it’s able to quickly model alternative scenarios about where to produce, how and where to ship, and even where to redirect in-transit shipments.

One automotive company used a digital twin to help predict the financial health and viability of its tier 1 and tier 2 suppliers. This was vital, considering the just-in-time nature of the auto industry. If just one piece doesn’t make it to the factory, the company won’t be able to make the car. Modeling the health of the supply base enabled the company to identify a handful of suppliers that were under financial duress, and then make targeted interventions to improve their operational performance and profitability, consequently, reducing supply risks to the original equipment manufacturer (OEM).

Good processes all run on good data

Good, accurate data is a critical element of a digital twin’s effectiveness. If, for example, the order multiple for packaging materials is wrong, someone needs to manually change this every time an order is placed. Couldn’t the buyer avoid this by simply changing the master data for the order? They could do this, but for various reasons, doing so never quite makes it to the top of their priority list. So the practice can go on for months or even years. 

Such master data errors can be found across a manufacturer’s operations, causing processes to bog down. In some cases, a manufacturer may have multiple entries for the same supplier and not know it. There may be a single typo in one of the addresses, or slight variations of the supplier’s name, or a headquarters address in one entry and a subsidiary address in another. If the manufacturer wants to analyze the amount it buys from that supplier, it has to de-duplicate all of those records so that it can get an accurate picture of total spend, which is an important tool in negotiating more favorable pricing terms.

Another example is lead time. There's often a lot of variability in delivery lead times, which isn’t surprising given it's a complicated process. But more often than not, we find that the system parameter settings for lead times are unduly generous or just plain wrong. If someone knows the settings aren’t accurate but doesn't fix them, he typically compensates by adjusting on the fly, which can only exacerbate the problem. 

These and other situations are good examples of why manufacturers need a way to clean up their data on a regular, ongoing basis. The right analytics and machine learning can actually automatically identify these anomalies, suggest corrections and even (within rules) automatically correct master data. This kind of self-correcting or self-healing capability can be applied to nearly every type of day-to-day supply chain data, and can be part of an effective digital twin or operate on its own. In any way, it enables manufacturers to continually and automatically fine-tune their processes.

Microsoft technologies play a key role in optimizing processes

When considering digital twins, people may often picture actual 3D models — for instance, one of a jet engine that replicates how the engine behaves in the physical world. But when applied to the end-to-end supply chain, digital twins are more likely to be intuitive, easy-to-use dashboards. In either case, digital twins could in theory be fully automated — continually monitoring how a process is running and, on their own, make adjustments or take other actions to maintain optimal performance. In practice, that’s still a long way away for many industries and may never make sense for some. More often, there’s a symbiotic relationship between systems and humans. Automation and artificial intelligence (AI) may come up with recommendations, perhaps even test and run through the most likely alternative scenarios. But humans are still needed to review the options, perhaps refine them with the latest information that may not have been available earlier and make a final decision on the course of action.

While digital twins were on the cutting edge of innovation a decade ago, they’re increasingly becoming more mainstream and feasible for a wide range of manufacturers thanks to advancements in technology. In particular, a variety of technologies are putting digital twins well within reach of most manufacturers and are the foundation of the digital twin capabilities within solutions, such as EY Smart Factory. 

Such technologies, like those powered by Microsoft Azure, start by helping manufacturers address one of their biggest, most enduring pain points: gaining good end-to-end supply chain visibility. This is accomplished by pooling data in a Data Lake from disparate supply chain systems — including information generated via internet of things (IoT) sensors embedded in equipment and other assets — as well as data on external factors, such as weather, traffic and newsfeeds. Aggregated data is then cleansed for access by a digital twin, which delivers insights generated from that data via a platform such as the Microsoft Power BI dashboard. Scenario modeling and recommendations are handled by technologies such as Microsoft Azure Cognitive Services, a family of AI services and cognitive application programing interfaces (APIs) that enables manufacturers to quickly and easily build intelligent apps that can drive more accurate and informed decision-making. 

“Cloud-powered tools are a boon for optimization,” says Neal Meldrum, Business Strategy Manager at Microsoft. “They can optimize factory operations by: equipping people with the data and tools they need to identify areas of waste; improving cycle times for manufacturing operations; making automation processes faster and easier; maintaining equipment more efficiently by accurately predicting maintenance needs; dynamically realigning production to meet changing demands; and increasing turn-times for inventory across the value chain — all while reducing energy consumption.” 

Cloud-powered tools are a boon for optimization.
Neal Meldrum
Business Strategy Manager at Microsoft

Optimization, not just effectiveness

It’s no secret that manufacturers have struggled to flex and pivot in response to the changes driven by the COVID-19 crisis. The situation has clearly illustrated that manufacturers need more than just effective processes. Most processes in manufacturers that are effective during “normal” times generally still aren’t running optimally, and they certainly aren’t equipped to handle major disruptions like COVID. 

Read the previous article in this series, Four ways to use digital to strengthen manufacturing resilience.

The views of third parties set out in this publication are not necessarily the views of the global EY organization or its member firms. Moreover, they should be seen in the context of the time they were made.


Manufacturers need to standardize their processes and ensure they have good up-to-date to master data, then take advantage of digital technologies to optimize those processes and gain visibility and control across the supply chain. Doing so will enable manufacturers to eliminate cost, waste and inefficiencies from their operations while creating more agile, flexible and resilient processes that can help them better weather a disruption—whether they can see it coming or not.

About this article

By Matthew Burton

EY EMEIA Consulting Center Partner and Digital Operations Leader

Supply chain leader with over 20 years experience in industry and consulting. Focused on digital transformation.