Tuan Nguyen, senior manager of DELMIA Global Enterprise Manufacturing Intelligence at Dassault Systèmes, talks about how manufacturers can take a more lean approach to management.

Manufacturing enterprises have invested heavily in lean practices for many years, wringing the inefficiencies out of every operation in the production process. Supply chains have been tightened, inventories reduced or virtually eliminated with just-in-time processing, and production operations at every stage streamlined and optimised.

But there is one area in the lean revolution that often is not considered—not because it doesn’t matter, but because it has been so difficult to deliver a solution. That neglected area? The management decision-making process.

Consider a global manufacturer that has practiced continuous improvement for a decade. Products roll off the assembly line with precision. The quality team is on top of production worldwide, so yields are consistently high. Warehouses operate at top efficiency. And then one day, a supplier problem develops. A key component, let’s say, begins trending out of spec.

What does the company do? That depends on the managers who have responsibility. How quickly can they identify the problem? What actions do they take? How soon can they correct the problem, and how accurately?


Applying a lean approach to management

These actions depend on information getting to that decision-maker in a timely way. And this is where lean systems can fall down. Global manufacturers have complex supply chains and multiple plants that often capture data in different ways and report in different formats. That data has to be gathered and analysed, and then delivered to each person in the enterprise who needs it, in a form appropriate for their role.

The plant manager may spot a problem quickly, based on local data. But what if it’s a regional problem that is only apparent when looking at aggregated data? Then it will take longer, perhaps a good deal longer. There are companies that are happy if they get aggregated global manufacturing reports on a weekly basis. But a lot can happen in a week. Faulty products can ship. Quality can get bogged down with testing. Warehouses can accumulate parts waiting for a management decision.


A new use case for enterprise manufacturing intelligence

This is why some enterprises are now implementing a new generation of manufacturing intelligence systems that provide global management reporting and analysis in close to real-time. This approach requires more than a graphical front-end that simply dresses up disparate or incomplete data. It requires real-time information gathering from all the plant floors, the ability to clean and aggregate the data from multiple sources, and the means to deliver that data up the corporate chain as it happens— all the way to the corner office if needed.

Such a system is not trivial to implement, and IT may grumble about yet another information project that will stretch its already thin resources. But there are solutions on the market that are relatively easy to deploy, and the investment is small compared to the efforts already expended on global lean initiatives.

Besides, without a manufacturing intelligence system, lean organisations are only lean when nothing unexpected happens. And, as every manufacturing enterprise knows, that is almost never the case. One unexpected event can undo months of savings and efficiencies.

For enterprises that are serious about continuous improvement, it would seem that manufacturing intelligence for managers is a necessary step.


Continuously improving management performance

Visibility into real-time manufacturing intelligence can let management improve and enhance lean programs. This improvement can be accomplished with access to real-time production information, ideally aggregated from across the enterprise. Better, faster access to information allows management to act with greater efficiency, which contributes to improved lean performance.

But what if we took the idea even further? What if manufacturers could continuously improve the management process itself, in the same way that they continuously improve a production process?

After all, they are both processes. When something happens in the enterprise that requires judgment and decisions, such as a quality crisis or a market shift, a management process is initiated. Management receives information (or doesn’t), investigates the situation (or not), and takes action (or fails to). Furthermore, there are meaningful metrics involved, such as time to discovery, time to resolution, outcome of resolution, and profit/loss impact.


Looking at management as a process

If management decisions affect lean performance, and if there are metrics that can be tracked, compared, and improved upon, then in theory, couldn’t it be used this information to continuously improve the actual management processes? The answer is yes.

When a manufactured part begins to trend out of performance, managers know this because they are tracking all such parts being produced globally. If one part from one plant is out of spec, they’ll take action. Perhaps they will find the plant is not following best practices, so they take steps to correct the problem and deploy the proper processes.

The same approach can apply to management decision-making. If an enterprise tracked how managers performed in these situations, we could wager there would be significant differences discovered. Some management teams would accomplish more with fewer resources. Some would resolve problems faster. Some would deploy new processes more effectively than others. These differences could be tracked by an information system—let’s call it a management intelligence system.


Management intelligence system

Suppose, for example, that a particular lean production initiative was being deployed globally. If we had a management intelligence system, we could track and compare how different regions deployed the initiative at the management level. We might find that one region was far ahead of the others in certain key metrics. For instance, we might find that the successful management team accomplished more with fewer resources, but had more regular inter-departmental meetings to facilitate problem-solving.

We could then identify and deploy these management best practices around the enterprise, just as we deploy best production practices.


Is this concept possible, or just fantasy?

In theory, this type of system could be created using the manufacturing intelligence technologies available today. It would simply require the creation of a new set of metrics—time spent, resources used, departments contacted, etc. This data could be captured by the same system that managers use to investigate production issues, through their computers and mobile devices.

The data is probably there; it’s simply a matter of figuring out how to access and use it.

We’re not proposing a type of big brother automate the management process. That would be small minded and doomed to failure. Management, by definition, involves human judgment and will never be as cut-and-dried as a production process. But certain elements of management could be measured and improved. What about mean time to resolution? And if used judiciously and fairly, a management intelligence system could yield significant information about how management is performing, and more importantly, where it could be improved.

In other words: lean management based on continuous improvement.

Note: this article originally appeared a blog on The Manufacturer website. http://www.themanufacturer.com/articles/a-lean-approach-to-management/