Reducing MRO Inventory Using RCM Principles

Tissue mills can benefit as well as paper mills producing all grades

JAY SHELLOGG

Tissue mills, like many other capital intensive industries, know that cost cutting from the maintenance budget can be a difficult task when done in an environment of fixing broken equipment. With no understanding of when and why equipment is failing, there are too many unknowns to deal with. We tend to find that cutting maintenance budgets and repair and operating (MRO) inventories beyond a certain level becomes more and more difficult. As tissue mills age, there is a tendency toward growth in MRO inventory.

A mill manger once told me that MRO inventory levels were a good indicator of the maintenance culture within a mill. High levels of inventories along with decent run ability indicated to him a reactive firefighting culture; relatively low MRO levels and good runnability indicated a culture that pointed more toward proactive maintenance. Whatever a mill’s maintenance culture is, that culture is less of a reflection of the maintenance department than it is of the overarching beliefs of the mill’s leadership. Maintenance culture is mill culture, not departmental.

Today’s tissue mills face a competitive manufacturing market that is tied directly (or almost) to their consumer base. In the pulp/paper/tissue industry, tissue, more so than any other product, is directly tied to the point of sale consumer. In fine paper, while many individuals buy some paper for individual use, the bulk of sales are to customers who do not directly use the product.

Here is an example of what I mean. A health care insurance company may buy tons of paper, which makes them a customer, but the actual consumers of the paper are that companies employees. So the consumers are at least one step removed from the manufacturers. The same holds true with linerboard, where typically a consumer is driven to make a purchase based on the product contained within the packaging.

I know there are always exceptions, but for the most part people buy a brand of cereal because they like the cereal, not because they like the box it comes in (fancy graphics and marketing aside). In tissue, more so than not, the customer is the consumer. I know the big box store makes the first purchase, but they in turn sell to the consumer. So for the big box store to make the first purchase, the consumer must be satisfied with the product.

A pulp and paper mill I worked with had a significant investment in MRO inventories. So for them, inventory management and reduction was an important strategy. Looking for new ways to improve decision making, business process effectiveness, as well as reducing working capital, they turned to a new approach in managing storeroom inventory levels. The need for inventory management is no less important, and may be more so in the tissue market.

A NEW CHAPTER

This mill undertook a new chapter in its quest for operating at world-class asset reliability levels. The mill embarked on its first use of Reliability Centered Maintenance (RCM) methodologies. The original project objective was to reduce the reactive maintenance costs associated with repairing equipment after it had already reached a failed state.

To do so, the creation of new asset reliability programs founded upon a formal understanding of how an asset fails (referred to in RCM terms as failure modes) was undertaken. An RCM-based methodology was selected for the project. With asset failure modes formally documented, it then became possible to apply one of two major categories of maintenance strategies to the asset:

1. A Condition-Based Maintenance (CBM) strategy for assets such as bearings that fail randomly, but are eligible for a proactive task for the detection of the given failure mode (via the mill’s choice of proactive options such as inspections, PdM technologies, PM, etc.). The chosen proactive task results in an understanding of the state of the given failure mode at the point in time of the inspection.

2. A No-Scheduled Maintenance (NSM) strategy for assets such as electronic devices that fail randomly with little or no notice.

Both of these maintenance strategies require a different approach for making storeroom spares stock versus non-stock decisions.

CBM AND REDUCING
STOREROOM INVENTORY

As mentioned, understanding the correct point in time of the inspection (the inspection frequency) becomes critical within the new proactive maintenance program to ensure the identification of:

1. The failing state prior to the asset reaching actual functional failure

2. The failing state with adequate lead time for the maintenance planner to plan and schedule the corrective task prior to the asset reaching functional failure.

It was the planning function within 1. that gave rise to the opportunity to revisit the approach to determining an assets spares stocking policy for assets being maintained with a CBM strategy.

To ensure the inspection frequency is correct, RCM uses a time horizon at the individual failure mode level called the P to F Curve, with P being the point the potential failure is identified (an inspection point) and F being the point of functional failure. If the state of P is such that a corrective task is required, then the remaining time to F must provide the adequate corrective task time horizon. This P to F time horizon is formally documented during an RCM analysis with the assets subject matter experts (typically the assets maintainer’s operators and engineers) utilizing their knowledge and experience to document how long the progression to F will be. Math then determines the Nett P-F curve, which is the remaining time available to the maintenance planner once a given P point is detected.

With the remaining time to functional failure clearly understood and available for comparison against the vendor’s spare part lead time, an informed stock vs. non-stock decision can be made. For example, a four- to five-day vendor lead time on the spare part required for a failure mode with a two-year P-F interval leads to a clear non-stock decision on the spare. Said another way, if the vendor lead time exceeds the Nett P-F inspection interval, then there is no need to stock the part, providing you rigorously perform the inspection.

NSM AND REDUCING
STOREROOM INVENTORY

It can be a challenge for the reliability professional to convince an organization that through proactive CBM programs, the need for stocking spare parts can be reduced. In this second example, the challenge of convincing an organization is even more difficult when it comes to equipment for which no proactive task can be developed and therefore an NSM maintenance strategy is adopted.

If the failure mode is random (upwards of 80 percent of failure modes are) and the P–F curve is of no practical use (such as in the failure of an/many electronic device or devices), then on what technical basis can a non-stock spare decision be validated? The answer they found in their RCM analyses involved two additional data elements captured with the failure modes:

1. The consequence of the failure

2. The statistical probability of the failure occurring.

An example of how this works is that, out of an RCM analysis, the team will have identified several failure modes with a recommended action to review the spare parts stocking policy. This action is routed to the reliability engineer, who conducts a review of the probability of failure based on both the manufacturer’s MTBF data and the mill’s internal MTBF data for the asset to account for any operating context differences between how the manufacturer foresaw the asset being used and how it is actually being used.

Once determined, the MTBF is compared against the vendor lead time, cost of downtime, cost of the part, including expediting, and the number of locations that the part will spare. Based on this information, a probability cost model is calculated that determines when (in number of years), for a population of installed identical parts, the cumulative probability of failure will equal or exceeds 50 percent. It is this number of years to reach the 50 percent probability of failure that is used to make a cost decision of whether stocking the part is cheaper than the downtime cost of not
stocking it.

Once developed, the cost model requires only the following data inputs to provide the stock/non-stock answer:

1. Unit cost to purchase

2. MTBF in number of years

3. Number of running units.

Stock Example:

1. Unit cost to purchase = $16,567

2. MTBF in number of years = 8

3. Number of running units = 8.

In addition, we know:

• Time value of money (10 percent interest rate)

• Probability of failure = 50 percent

• Probability of success = 50 percent

• Number of years for population to reach 50 percent probability of failure = 1.52 years (Y+)

• Probability of NO failure in any one year = .63446343

• Probability of failure in any one year = .36553657

• The odds of failure in any one year = 1 to 2.735704392.

Note that not until “Y+” the time elapsed does the population exceed a 50 percent probability of failure. Therefore, the cost model is as follows:

C = Time value of money LESS the unit cost of the part

C = $2,589 (Weighted cost to stock the part)

Contribution of areas where the part is to be spared ($888/ hr.):

Lead time = 3 days

Downtime cost = $63,936.

Should the part be stocked? YES

Lead time to not stock:
0.1 days
2.9 hours.

Non-Stock Example:

1. Unit cost to purchase = $14,000

2. MTBF in number of years = 61.8

3. Number of running units = 1.

In addition, we know:

• Time value of money (10 percent interest rate)

• Probability of failure = 50 percent

• Probability of success = 50 percent

• Number of years for population to reach 50 percent probability of failure = 61.8 years (Y+)

• Probability of NO failure in any one year = .988846691

• Probability of failure in any one year = .011153309

• The odds of failure in any one year = 1 to 89.65948819.

Note that not until “Y+” the time elapsed does the population exceed a 50 percent probability of failure. Therefore, the cost model is as follows:

C = Time value of money LESS the unit cost of the part

C = $5,046,530 (Weighted cost to stock the part)

Contribution of areas where the part is to be spared: $1,776 per hr

Lead time = 7 days

Downtime cost = $298,368.

Should the part be stocked? NO

CONCLUSION

Because the new asset reliability programs are based on RCM principles, they identify failure modes. These failure modes, formally documented, made it possible to apply one of two major categories of maintenance strategies to the asset:

1. Condition-Based Maintenance (CBM) maintenance strategy:
• for assets such as bearings which fail randomly but are eligible for a proactive inspection task for the detection of given failure modes (resulting in an understanding of the state of the given failure mode at the point in time of the inspection).

2. A No-Scheduled Maintenance (NSM) strategy:

• for assets such as electronic devices, which fail randomly with little or no notice.

These maintenance strategies require a different approach for making storeroom spares decisions (stock vs non-stock), but taking them into account and with rigorous application of this approach, the mill achieved a storeroom inventory level of less than 2 percent of the asset value with an increase in overall mill uptime.  

Jay Shellogg is director of reliability service at Strategic Maintenance, Texarkana, Texas, USA. His email address is jayshellogg@strategicmaint.com.

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