Potential for failure - for startups in 0-1.

Building Hungry Wheels I have learnt a great deal about Equipment Maintenance. It has taught me many lessons, guided my decisions, from finance to legal to product design.

The P-F Curve is the simplest yet biggest lesson an entrepreneur can learn. I am sharing it here in its simplest form.

What is a P-F curve for a 01 startup?

A P-F curve is a graph that shows the health of health of an idea/ startup over time to identify the interval between potential failure and functional failure.

Overview

The eventual failure of any idea is inevitable, VCs and PEs know that. Looking into lifespans of startups it become a key reason for founders and boards to agree to M&As. Wear and tear naturally occur with continual exposure to market forces, founder burnouts, low capitalisation and other factors - one of key ones being timing. In the same way your pair of shoes eventually get worn out after 500 miles of walking, your core idea like a key plant equipment (e.g. pumps, motor bearings) will ultimately reach its functional failure point. A point when it just won't work anymore, not in the way you began, For the future doesn't fit in containers of the past.

Let me start with an example which you can translate into any item at home or at work.

The good news is that the functional failure point (i.e. the end of equipment life) takes a long time to occur. The P-F curve helps to characterize the behavior of equipment over time. Its used to assess the maximum usage that can be gained from the equipment.

Potential failure and Functional failure.

There are two main points of the P-F curve that need to be identified.

1. Potential failure?indicates the point at which we notice that equipment is starting to deteriorate and fail or an idea is becoming useless to its target audience. Since startups are about timing v/s evolving and new value chains, this is a must to plan.
2. Functional failure?is the point at which the idea or equipment has reached its useful limit and is no longer operational or relevant. Since startups are about limited capital and unlimited opportunities, this becomes critical to foresee. It helps one choose the pone to go after.

These two points define what’s called as the?P-F interval—the time between when the failure is initially noticed and when the idea/equipment fails completely.

This is what must be done before you start-up. At planning stage itself you need to accept that these failures will happen, then you must know what can cause these, speeden them up, or pause them, and that becomes the playbook and what I call the INNOVATION PIPELINE.

How to create a P-F curve for your startup

(Let me gamify this.)

The basic parts of the P-F curve are given above. Actual data can be expected to vary on a case to case basis. I have built proven models for creating INNOVATION PIPELINES in multiple industries, I can help you do this.

Let me start with a simple analogy for this post... For instance, the lifespan of a heavy duty pump might not be the same as that of a mechanical bandsaw (analogies for different digital product features, physical parts or markets, marketing initiatives etc. you get the drift.). It then follows that expected failure points for different ideas/ initiatives/ equipment will vary. Care must be considered when building P-F curves. Different types of equipment are expected to have varying interval values.

For example, assume that a pump that’s been normally operating for eight months suddenly produces more noise than usual. Unnecessary noise can be a sign of failure (another way of seeing this is too many failures/ returns of parts or no usage of a feature inside an app or no ROI on marcomm campaigns). With the inspection and confirmation of maintenance personnel/analytics) we can then say that the first noticed sign of failure (i.e. the potential failure point) occurred at eight months. In a startup you can project this, much like the zeroes we all love projecting.

Note that the actual start of deterioration might have happened before the eight-month mark. This is what I help you find, to see things before others see them. So we can assume that the actual start of failure happened some time before point P. However, it is only the potential point of failure that we can measure in time with certainty as it was the first event when noticeable symptoms of failure were recorded.

For the same example, we can suppose that the pump continues to operate for another six months until it totally breaks down—that is the functional failure point at 14 months.

How to maximize the curve

Now that we’ve visualized how the P-F curve relates to real-life scenarios, we have the chance to prepare for the inevitable functional failure. The idea is to balance our resources to prolong the P-F interval economically, giving the startup the opportunity to beat the pressures of constraints created by lack of timing and capital.

Common practice is to maximize the use of the P-F curve with?condition-based maintenance (CBM)

What is condition-based maintenance?

Condition-based maintenance (CBM) uses sensor devices to collect real-time measurements (ie. pressure, temperature, or vibration) on a piece of equipment. CBM data allows maintenance personnel to perform maintenance at the exact moment it is needed, prior to failure. (Replace all this with trackers needed to listen to and calculate impact of market, internal and capital forces). As an example...

• 79% of businesses see predictive maintenance as the main application of industrial data analytics
• 10% (and maybe even less) of industrial equipment ever actually wears out, meaning a very large portion of mechanical failures are avoidable

By applying CBM and proactively checking the condition/ relevance of the?equipment/ idea, we are able to infer the rate of deterioration of the equipment/idea over time in advance.?Maintenance personnel or INNOVATION PIPELINE experts?are then able to plan and assess whether it is cost-efficient to mitigate the causes of failure given the projected P-F interval.

The P-F curve and CBM

At the early signs of failure, it may be helpful to perform routine CBM tasks to assess the health of the equipment.

Continuing with the pump example, a P-F curve coupled with CBM tasks to monitor pressure and flow rate conditions may resemble the following graph:

A maintenance team can attach condition monitoring sensors to the equipment after the point of potential failure to assess how much more the equipment can be maximized.

Post launch even, the INNOVATION PIPELINE experts can attach condition monitoring to the parameters on a fortnightly or longer basis depending on the point of potential failure to assess how much more the equipment/ idea can be maximized.

Look forward to hearing your views on this learning I have had.