Shifting from Time-Based Maintenance to Condition-Based Maintenance

Maintenance operations consume a significant share of budgets in manufacturing. A major contributor to this cost lies in the reliance on time-based schedules, where maintenance tasks are performed at set intervals regardless of asset health. The approach, however, has its own cons.

Condition-based maintenance (CBM) changes the pattern. It relies on real-time data, inspections, and diagnostics to determine when intervention is truly needed. Maintenance teams focus only on assets showing actual wear or risk of failure. The result: reduced costs, fewer unnecessary interruptions, and better resource allocation.

The following discussion explains both methods, why a shift makes sense, the areas where condition-based maintenance delivers savings, a step-by-step process for transition, the challenges likely to arise, and how to overcome them.

What is Time-based Maintenance?

Time-based maintenance is a scheduled maintenance approach where servicing occurs at fixed calendar intervals or after a specific number of operating hours, regardless of actual equipment health. It leans toward a reactive response because failures can happen between scheduled interventions.

What is Condition-based Maintenance (CBM)?

Condition-based Maintenance (CBM) is a preventive maintenance strategy where servicing is triggered by real-time asset health data, gathered through sensors, inspections, or diagnostics. As a proactive maintenance strategy. It allows targeted interventions only when performance indicators show deterioration or a risk of failure.

Why to Shift from Time-Based to Condition-Based Maintenance?

Condition-based maintenance is known for reducing maintenance costs by around 30 percent. It has evolved as one of the preferred maintenance strategies. There are several compelling reasons as to why manufacturing maintenance functions should start investing effort in condition-based maintenance moving from time-based maintenance. These are:

• No unnecessary servicing: Maintenance occurs only when asset condition indicates need, which avoids wasteful work and resource overuse.
• Low parts replacement costs: Components are changed based on wear data, and there is no premature replacement so obviously part replacement costs are low.
• Minimal equipment downtime: Work is performed only when signs of failure appear, which improves equipment reliability, and minimizes production halts.
• Excellent resource allocation: Labor and tools are directed to assets requiring attention, which leaves no need for blanket servicing of healthy equipment.
• High operational throughput : Teams respond to actual needs, so interventions are faster interventions and disruptions to production flow are also fewer.
• Optimal maintenance budget: Spending focuses on genuine risks, which brings down routine maintenance costs and aiding in better financial forecasting.
• Extended asset life: Components last longer when serviced only when needed, which preserves equipment value over time.
• Better decision-making: Data-driven insights replace guesswork, which facilitates precise interventions and better prioritization.
• Boosts safety: Addressing wear indicators early prevents sudden breakdowns that could endanger workers or damage other equipment.

How to shift from Time-Based to Condition-Based Maintenance to Save Costs

Below is a step-by-step workflow to successfully shift from condition-based maintenance to time-based maintenance. Each step adds value to the next step and completes this transformation cycle.

Step1: Build the business case and define scope

Start by pulling twelve months of data for every asset, covering labor hours, planned shutdown hours, spare consumption, overtime, rush freight, scrap tied to equipment faults, and energy draw for key lines. Use this dataset to calculate the cost-to-serve per asset and establish how much each machine is contributing to the overall maintenance burden rate. Your goal will be to create a fact-based foundation that shows where money and effort are being consumed.

Next, sort assets by total cost impact and run a Pareto analysis to identify those machines that are driving most of the expenses. Next, factor in downtime value per hour and review how much inventory, especially from ABC classes, is being tied up due to recurring failures. By combining labor, downtime, and parts data, you can clearly see which assets create the highest strain on budgets and operations.

For instance, you might find a packaging line showing excessive labor hours on scheduled motor overhauls and frequent micro-stops. Highlight the case for change i.e. fewer unnecessary teardowns, reduced spare usage, and steadier throughput. Based on these findings, limit phase one to the 15–20 assets with the highest combined labor, downtime, and parts cost.

Step 2: Map failure modes and choose early signals

Now, for each of the high-priority assets, list the dominant failure modes, their effects, and the earliest reliable symptom that can be tracked. Apply an FMEA or FMECA-lite approach to quickly map where each machine is most likely to fail and what signals indicate the start of that process. Use the P-F curve to understand the interval between when a potential failure can first be detected and when it turns into a functional failure, as this window defines how much time you have to act.

Once the failure modes are clear, tie each symptom to a measurable signal. So, if there is a pump showing internal wear, it can be tracked through discharge pressure variance, rising motor amperage, or oil particle counts. In compressed-air networks, early signals such as pressure decay tests or ultrasonic surveys can indicate leaks well before they compromise header pressure.

When selecting signals, focus on those that balance sensitivity with reliability. Distinguish between threshold-based alerts and trend-based monitoring, and weigh the risks of false positives versus false negatives. The decision point is to pick a few key signals per failure mode that provide the longest P-F window while keeping unnecessary noise to a minimum.

Step 3: Design data capture and CMMS rules that trigger work

Choose the leanest data method that still protects the P-F window. Fixed sensors suit assets with fast deterioration. Route-based handheld measurements suit slower changes. Then tie all data to a rule in the computerized maintenance management system (CMMS) so action starts from evidence, not a calendar.

Rule can be: If velocity RMS on Motor-12 exceeds 7.1 mm/s for 10 minutes, create a corrective work order, priority High, attach job plan <job-plan-code>.

You also need to set a sample rate that fits the physics: fast for high-speed motors, slower for gearboxes, periodic oil analysis for slow wear. Then link spares to each rule so stores receive an auto-pick list with the work order.

Exception-based alerts, hysteresis to avoid chattering, deadband, time-over-threshold logic, rule-driven work order creation, API or gateway from sensors to CMMS are important tools here. Based on the rule and sensor data, the decision must be made.

Step 4: Create job plans, parts kits, and short outage scripts

A monitoring rule is only as good as the response that follows it, so create a clear job plan for every trigger. Lay out the exact steps, torque values, safety notes, photos, test points, and time standards. Attach correct part numbers and tool lists so technicians don’t waste time tracking down details.

Acceptance criteria must be precise, such as discharge pressure within ±3% at reference flow, with a time standard like 2.5 technician-hours. That kind of clarity prevents vague “looks good” sign-offs.

Also important is to stock pre-packed kits in stores for the top failure modes so technicians can grab what they need without delay. Use min-max levels set from lead time and consumption rate to keep stock lean. Build kits only for rules that trigger frequently and carry real cost impact, leaving the rest as pick-lists to avoid dead stock.

Step 5: Run a 90-day pilot with weekly governance and open scorecards

Pick one area or one line and freeze scope. Hold a 30-minute weekly review with maintenance, production, quality, and stores. Review active alerts, completed work, misses, and numbers.

Pilot scorecard. You will be carrying out the following comparisons to understand the efficacy of transition process:

• Unplanned downtime hours on pilot assets vs. prior quarter
• Technician hours on pilot assets vs. prior quarter
• Parts spend on pilot assets vs. prior quarter
• Mean time between repairs vs. prior quarter
• Energy draw per unit on the line vs. prior quarter

Results could be like 20% drop in technician hours on blanket PMs, 30% drop in rush freight, and 15% rise in mean time between repairs. Check if there are noticeable improvements due to transition.

Before-after study with matched periods, control limits on alerts, Pareto of causes for each alert, layered audit for rule adherence are key tools to leverage here. You will have to scale only after the pilot meets predefined thresholds on at least three of the five metrics.

Step 6: Scale with guardrails: limit count, limit noise, lock standards

Add only a small set of assets per month to avoid overload. Protect focus with three guardrails which are:

• Limit count: no new asset enters the program unless job plans, parts lists, and rules exist in the CMMS.
• Limit noise: each asset holds a maximum of five active alerts; any alert without a clear action exits the set.
• Lock standards: one template for rules, one template for job plans, one mobile checklist format.

Now, if a site tries to add 60 assets at once and drowns in alerts, with guardrails, the site adds 12 assets, hits targets, then adds the next 12. This is possible with stage-gate expansion, rule maturity index, alert effectiveness, and management of change for edits to thresholds. However, it is important that you conduct quarterly reviews to retire obsolete calendar PMs, reset min-max levels, and publish a one-page win sheet with dollars saved.

Step 7: Invest in people and habits that keep the gains

Tools matter, but habits matter more. Run short, hands-on instruction on vibration basics, infrared use, oil analysis interpretation, and CMMS rule logic. For using maintenance software applications like predictive maintenance and preventive maintenance software, train in use of these tools.

Also, the transition process involves stakeholders from across departments, so cross-training your maintenance teams is a necessary action. Pair a senior tech with a newer tech on the first months of alerts. Set a daily five-minute huddle across production, maintenance, and stores to align on today’s alerts and short outages.

Leverage skills matrix, job shadow, daily tier-one huddle, after-action notes on every alert with a verified defect. For efficient training and knowledge transfer, tie supervisor bonuses to rule adherence, first-time fix rate, and cost per unit for the pilot line.

What are the Challenges and How to Overcome them?

The process of transitioning from time-based maintenance to condition-based maintenance is not without its own challenges. Let’s see what these challenges are and what course can help avoid them:

Takeaway

We looked at the ins and outs of the process of transitioning from time-based maintenance to condition-based maintenance. If you adhere to the course we discussed, condition monitoring will be effortless to perform and you will see nice results in a quick span of time.

Since maintenance software including CMMS and predictive maintenance and preventive maintenance applications work as a centralized platform for data storage and maintenance analytics delivery, they are key to this transition. Implementation of these tools, supported by a well-structured maintenance plan, is the first step to initiate the shift and reach intended outcomes in a short span.