Cracking the Variability Code – Financial Impacts

Cracking the Variability Code – Financial Impacts

Article
Published on August 17th, 2020
Resources

Sheila Kester, VP of Operations at TwinThread

In any industry, driving quality performance and knowing how to deal with variability is the key to success. Variability is present everywhere, and when your experts are pushing for optimal performance, your organization is at risk of suffering from further unchecked variability.

You could walk through process-dependent plants for decades, and through the years you’d most likely be interested in getting to the bottom of the same prevalent issue - how to control variability? And, how would controlling variability impact a company’s finances? These are just a few questions we can start asking as we look deeper into variability’s impact on your organization.

There are a multitude of implications that variability has on an organization. Within this resource, we will explore 5 real-world examples where gaining control over variation positively impacted the organization looking to crack the code of variability within their operations.

Variation in Output and Revenue Impact

Every great organization is seeking innovative ways to deliver premium quality products consistently, and its customer-base comes to expect nothing less. Your organization would not allow below-quality products to be released to the public, but that’s not the issue. The problem is when there is a lack of control of the output.

When a company produces three different product quality levels: generic, standard, and premium, and there is a lack of control over output at a granular level, the producer may have no choice but to point all quality levels toward the highest grade. Unfortunately, customers notice. So, instead of opting for the premium, being wise to the lack of difference from product line to product line, they begin to buy one level below their needs. This can cause a revenue loss of around 0.25 per unit, or millions annually.

How can this code be cracked so that revenue is not impacted by a lack of control over variability in production output? By including more data elements than you think you need.

Drawing on a larger data lake will allow you to drive recipes to your specific, targeted quality levels.

Ensuring you integrate a comprehensive Predictive Operations Platform, that has in its arsenal an easy-to-use predictive quality application, and an in-line testing model for predicted quality results (which can be used to see real-time output projections) is the very best way to harness control over variability within whatever you produce.

With this methodology in place, the gap between delivering the highest quality products and hard economic benefits for your company will quickly disappear.

Variability in Process Conditions – Price and Cost Impact

Your operation may have highly sensitive products that are extremely susceptible to breakage. Line instability (caused by factors that may be presently unknown to your problem-solvers) can cause off-quality products and lead to very costly line downtime. Therefore, uncovering these unknown factors is vital when the breakage is resulting in 20% rework (or more), adding millions in annual costs.

But what can you do to crack the code? As part of an overall plan to continuously optimize quality and production, a quick-to-connect Predictive Operations Platform can give real-time access into your process variations by showing your subject matter experts not only what your top-drivers of breakage are, but why those top-drivers are happening. Implementing an innovative model of pairing process engineers and operations teams, and satiating them with the right real-time data - they are bound to uncover opportunities for honing operations in.

This approach gives domain experts and operators the necessary insight and forewarning to adjust process conditions and thus preventing breakage. Using this methodology, breakage can be reduced by 40%, which allows for more room to set prices competitively and win more business.

The combination of predictive technology with innovative industrial teams can help your organization hone in variability specific to your operational conditions, therefore saving your organization thousands in annual costs.

Too Much Variation – Capital Cost Implications

When you are working with heavy equipment or capital assets in some form or fashion, it may not be top of mind, but variation in equipment performance has capital implications.

Heavy equipment manufacturing is one of the largest and most competitive sectors of the manufacturing sector. So, it’s important in a large industry like this to have as little variation in equipment performance as possible.

Take the drilling industry, for example. There’s no shortage of organizations in this space that own and operate a large fleet of drilling equipment. With such a demanding industry clientele, drilling requires a continuously operating fleet. If a piece of your fleet has to go into repair unexpectedly, because of variability during its operation, you can’t confidently project to clients when maintenance and repair cycles will occur. Unchecked variation in equipment operation can spell large and unpredictable idle time (downtime events), which can easily consume millions in capital costs.

Whether you’re an equipment manufacturer, service provider, or operator - the effort to further optimize equipment operation, and reduce maintenance costs (resulting from outside optimal parameter functioning) can be painstaking without getting a proverbial leg-up from a predictive solution.

That means, you substantially reduce the probability that your crucial equipment will be placed (unexpectedly) on standby. So, not only are you improving overall fleet efficiency, but you are also saving millions of dollars annually and ultimately achieving lower cost of production for equipment operators.

Too Much Variation – Reducing Labor Costs and Raising Prices

Similarly to working with capital assets, having high variations in mean time between failures when servicing thousands of small assets will cause quite a blow to operational efficiency when faced with the prospect of simultaneous multiple repairs.

Being unable to efficiently schedule field-based repairs could represent a serious risk to the financial well-being of your company. The last thing you want is to incur unnecessary costs resulting from multiple intermittent in-field damage repairs because of variation in operation across your fleet.

Fortunately, this doesn’t need to be the case. Implementing predictive analytics will help you to better understand when your equipment is likely to fail. Using a predictive platform to identify dips in performance and slight variations in function can paint your domain experts a picture of when it will be most efficient to conduct preventive maintenance. What’s more, these insights on variation can be scaled. Switching from a reactive approach to a proactive, predictive methodology will allow you to batch maintenance efforts, resulting in asset repairs happening in one trip (instead of several). This means big savings on labor costs. And, not only that. It means greater uptime - freeing your fleet up and empowering you to go after more business.

Variations in operation, if left unattended, will lead to higher labor costs and lower overall pricing for your services. However, with your subject-matter experts working with the predictive operations solution, these variations will dwindle before they ever cause any hold-up in your operations.

Without the ability to identify operational variability, you’ll be forced to take a reactive approach in how you operate. Taking on a preventive approach allows you to take greater control over your operational future.

Lack of Variation in Energy Costs

An organization not fully in control of variability has challenges. But, what about when you’re trying to push your optimization further but aren’t able to identify any significant variation in your production processes? Many production operations consume significant amounts of WAGES (Water, Air, Gas, Electricity, Steam) resources. Production restarts are infrequent, and energy consumption data is often not available until production is complete.

Most organizations today are looking to optimize energy costs, but many companies have never optimized their centerline for key contributors resulting in large energy consumption by product line. This can easily result in spending more on wasted energy. You could be consuming much more energy than you need to produce the intended product run.

In this case, a lack of variability is the problem (in that, your energy consumption could be static across your operations but is being expended at greater intervals than is required). To solve this, all you need is a predictive energy efficiency application that empowers your subject-matter experts to conduct product line capability analyses, which will support them in accurately identifying the optimal setpoints for your specific goals of production output, quality, and energy usage.

Cracking the code for lack of variation in energy consumption and cost can help you identify hard economic benefits and reduce unnecessary energy consumption. Then you’ll see observable variation - in cost savings year-over-year.

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Running a large-scale operation without an understanding of and control over variability can lower your quality of performance and production. Fortunately, this doesn’t have to be the case. Your in-house experts have unique operational knowledge and the innovative drive to eliminate any challenges. With a comprehensive predictive analytics solution in their hands, variables will be uncovered, understood and honed in.

A comprehensive Predictive Operations Platform with a suite of multifaceted applications will be that guiding light for your domain experts. Allowing them to get right to the insights they are looking to locate; they’ll be able to act on what they learn in days - meaning variation will be a non-factor.

If you’d like to learn more about what a comprehensive Predictive Operations Platform looks like and how easy it should be to implement and use, get started here.