What about unplanned?

Everybody’s looking at process inefficiencies to improve maintenance but there’s lower hanging – and bigger – fruit to focus on first: unplanned events!

Maintenance has pretty simple goals; guarantee and increase equipment uptime and do so at the lowest possible cost. Let’s take a quick look at how unplanned events influence these three conditions.

Guarantee uptime

When production went through the evolutions of JIT (Just In Time), Lean,… and other optimisation schemes, schedules got ever tighter and deviations from the plan ever more problematic. WIP (Work In Progress) has to be limited as much as possible for understandable reasons. However, this has a side-effect of also limiting buffers, which means that when any cog in the mechanism locks up, the whole thing stops. Therefore, maintenance receives increasing pressure to guarantee uptime, at least during planned production time. Operational risk is something investors increasingly look at when evaluating big ticket investments or during M&A due diligence and for good reason; it’s like investing in a top athlete – don’t just pick the fastest runner, pick the one who can do so consistently!

Failures are bound to happen so the name of the game is to pre-emptively foresee these events in order to remediate them beforehand; planned, and preferably outside of production time.

Increase Uptime

The more you are able to increase (guaranteed) uptime, the more output you can generate from your investment. Unplanned events are true output killers; not just because they stop the failing machine but also because they may cause a waterfall of other equipment – depending on the failing machine’s output – to come to a halt. Unplanned events should therefore a) be avoided and b) dealt with in the fastest possible manner. The latter means having technicians and parts at hand, which can be a very expensive manner (like insurance policies; they’re always too expensive until you need them). In order to avoid unplanned failures, we have therefore introduced preventive maintenance (for either cheaper or cyclical events) and condition based or preventive maintenance. Capturing machine health and deciding when to pre-emptively intervene in order to avoid unplanned failures is a pretty young science but one that shows the highest potential for operational and financial gains in the field of maintenance.

Lower maintenance cost

By now most people know that unplanned maintenance costs a multiple of planned maintenance; by a factor three to nine (depending on the industry) is generally accepted as a ballpark figure. It therefore keeps surprising me that most of the investments have traditionally been made in optimising planned maintenance. Agreed, how to increase efficiencies for planned maintenance is easier to grasp but we have by now come to a level where returns on extra investments in this field are diminishing. Enter unplanned maintenance; can either be avoided (increase equipment reliability) or foreseen (in which case it can be prevented). Increasing equipment reliability has not always been the goal of OEMs. In the traditional business model, they made a good buck from selling spare parts and they therefore had to carefully balance how to stay ahead of the competition without pricing themselves out of the market (reliability comes at a cost). Mind you, this was more an economic balancing act than a deliberate “let’s make equipment fail” decision. Now however, with uptime-based contracts, OEM’s are incentivised to improve equipment reliability. Unfortunately, unplanned failures still occur; and due to tighter planning and higher equipment utilisation requirements, these failures’ costs have increased! Therefore, in order to lower maintenance costs, we have to lower the number of unplanned events. The only practical way is to become better at foreseeing these events in order to be able to plan interventions before they occur. The simple plan is: gather data, turn it into information, make predictions and take action to avoid these events. And voilà, 3-9 times more money saved than if we focused on planned events!

Life can be simple.

What’s happening to my train (and by extension, any equipment)?

At regular intervals, company managers are asked to provide their forecast for the next period(s). While some dread this exercise – and it is a tough ask for them to put together this forecast – others can almost pull the numbers on request. Why? Because they have good visibility on current and past performance, on current and past conditions and are well informed on forecasted evolutions in the market. Why is that so difficult for other? In short: lack of visibility. So many companies put KPI’s in place but then forget to make it easy to monitor these KPI’s.
For equipment, remote monitoring can be put in place and it is even more important with moving than with static assets (RCM: Remote Condition Monitoring) as reaction times are de facto longer due to the remoteness of the equipment in case of an unplanned event. I was therefore, to say the least, surprised to find out that most rail companies still have very limited visibility on their assets’ whereabouts, let alone the assets’ condition! With increasing pressure on punctuality and efficiencies, things have to change rapidly. Some process improvements has been put in place. Some signalisation has been upgraded and, finally, some cross-European initiatives have been put in place (such as ETCS – European Train Control System and ERTMS – European Rail Traffic Management System). In some cases, the lack of punctuality has been solved by… adapting the train tables to the observed performance of the trains (really!)…
I’m often asked why visibility is so important. Let the following graphic illustrate this:
Only a small part of total downtime is actual repair time! Obvious as this seems, time and time again when executives check the numbers, they’re flabbergasted by this finding. Turns out they’ve all been investing tons of money in improving the repair process and very often they’ve forgotten to get the diagnosis and parts/mechanics logistics in order. I was once told by a rail operator that in order to get better remote diagnostics, in case of a failure they asked the train driver to ‘make a picture of the control screen with his smartphone and send it back to central’. May sound funny but I actually think this was a great idea! And, in effect, it cut a big chunk off their diagnose time.
How do we take things one step further? Once we have (digital) visibility, we can use this data to make predictions on assets’ condition. With this information, it is possible to avoid (some, not all) remote failures, which leads to less downtime, higher punctuality and service levels, lower maintenance costs, etc. How so? A number of conditions need to be fulfilled:
– the predictions need to be actionable (i.e. they should tell you what to do, not just give you an abstract statistical forecast)
– the predictions should be based on accurate and up-to-date data
– the predictions should be accurate enough to warrant an intervention
– the predictions should provide enough lead time to get the repair done (planned, mechanic and part in place, etc.)
Given these conditions, RCM can lead to CBM (Condition Based Maintenance). While RCM is a mature technology, it is not yet generalised; very often, trains are quite capable of capturing their condition through sensors which feed an on-board diagnostics system, but all too often, the capability to offload this data from the train is lacking. CBM as an approach to maintenance is equally mature but even less widespread, mainly due to the lack of data and processes. However, it is well-known that CBM increases equipment performance which, in rail, results in increased punctuality, less breakdown and, ultimately, higher capacity.

Industry 4.0 in a nutshell

In a nutshell? It’s not about the machines! It’s about what ties them together.

The whole Industry 4.0 movement is drawing a lot of attention – and rightly so! Business leaders and policy makers alike underline the importance of the fourth industrial revolution to maintain some sort of a balance in a globally competing world. While for some businesses it makes perfect sense to compete on price alone, for others the focus on value (functionality, form factor, brand, reliability,…) allows them to compete by shifting the playing field away from just price.

In order to respond to evermore rapidly changing product requirements, shifting business models (i.e. selling products as a service), increasing reliability requirements, etc. technology is called to the rescue. Production needs to be more automated whilst regaining flexibility. Production runs are shortening. Product lifecycles are shortening. This creates a planning as well as a maintenance nightmare!

The first, and current focus of industry 4.0 has been to make machines more ‘intelligent’. Mechanical structures are fitted with sensors and many actions can be steered electronically. Increasingly, machines are able to perform a series of actions semi- or totally automatically. This is invading our private lives as well; think of self-driving cars (not quite yet) but also self-cooking devices (i.e. just toss the ingredients in a Thermomix, point it at the right recipe – connected to the internet – and it’ll prepare your meal). We’ve come a long way!

The addition of sensors and introduction of IoT (Internet Of Things) and M2M (machine-to-machine) technologies now allows centralised systems to gather all the information from these machines. And that’s where the real value lies! Industry 4.0 is about connecting things, much more than it is about making individual equipment more performant. A sportsteam also only gets slightly better by improving individual players’ performance. It’s when they train together that real value gets created.

Today, the technological foundations exist to create businesses that create like an organism. Just like all the parts of our body perform their tasks individually, it’s the internal and external interactions that make us perform. For airplane fleets for instance, this means end-to-end planning (not just for current capacity requirements but also for future and taking into account maintenance requirements, delays, etc.). Co-ordinating the fleet to minimise ramp time and maximise airtime (that’s an airline’s productive time) requires knowing not only about each flight but also about up- and downstream flights (it’s a hub and spoke business). Those flights may be internal but they may also be operated by third parties. Therefore, external collaboration and data exchange is required. Individual flights get increasingly ‘smarter’ – flight routes and speeds are optimised to minimise fuel consumption. This introduced variability which has to be compensated at airports, etc. One sees that with growing ‘intelligence’ comes growing complexity. 

Industry 4.0 is not about machines, it is about data. Or better, it is about information. Data generated by the equipment and other internal (planning, sales, finance,…) and external (suppliers, clients, weather, stock market,…) needs to be centralised in order for every system to turn it into information. What may be information for a PPS (Production Planning System) may not be information for another system, etc. 

One of the new focus areas is the optimisation of maintenance. Better information should make condition based or predictive maintenance possible. Whereas the intro of sensors has rendered the diagnostics of equipment ever better, most predictive maintenance efforts have focused on finding ‘rules’ and fixed ‘algorithms’ for making predictions about equipment’s need for maintenance. With increased flexibility being one of the key advantages of Industry 4.0, one easily understands that these approaches will not perform. Or rather, they may at one point in time only to miserably fail the next. What we need is an equally flexible and intelligent approach to predicting equipment health or failures. Once this level of sophistication is reached at all points in the chain (part/machine/production line/external collaboration/…) will we really strike Industry 4.0 gold.

Maintenance optimization goals

When asked about optimising maintenance, different angles can be explored.

First, maintenance optimization can target the outcome; i.e. better maintenance. Better maintenance should result in less failures between maintenance intervals. Additionally, better maintenance can target lower costs for the same outcome. Business process optimization is the usual approach to achieving these targets.
Today, I’d like to highlight two aspects of maintenance optimization that are at the heart of predictive analytics projects for maintenance. The first, and typically the one corporate executives think of when starting predictive analytics initiatives, is to lower the frequency of maintenance.
Screenshot 2016-03-03 15.47.39
This can be done by spacing out the intervals equally, more often than not through a relaxation of the safety levels. Mind you, this is not necessarily bad as in many cases, the maintenance interval is the result of safety upon safety upon safety which leads to overly conservative maintenance requirements. A better approach is to introduce condition based elements in order to allow for the delay of a maintenance event based upon the observed condition of the equipment.
This leads to certain intervals being more spaced out than others due to many factors such as: what is produced, how many, what materials,… Correct evaluation of the condition of the equipment is complex and cumbersome. Instead of relying on manual processes, sensors, diagnostics, and finally predictive systems now allow us to automate the process of assessing the condition and remaining cycle time before the next maintenance. A good system will warn with enough lead time to allow for planning the maintenance event.
Screenshot 2016-03-03 15.56.17
All this is very nice but it only focuses on planned maintenance. The bulk of the pain/cost comes from unplanned events though. And that’s also where the biggest savings potential comes from. Gathering condition, usage and circumstantial data actually allows us to get pretty good visibility on future unplanned events. Avoiding them by planning a pre-emptive maintenance turns these into planned events with minimal impact to the production schedule. Given enough lead time, the pre-emptive maintenance can be made to coincide with a planned maintenance, lowering production impact even further.
Screenshot 2016-03-03 15.47.52
While the ultimate goal is to space out maintenance events (higher availability for production and less maintenance cost), there are some hurdles to be overcome before getting there:
– resistance from the maintenance teams: “we’ve done it a certain way for so many years, why change?”
– resistance from management for taking responsibility
– legal hurdles: certain markets (i.e. passenger transport) are heavily regulated and the regulator needs to be convinced on the validity of the new approach
This is why (in most cases) the initial focus should be on eliminating unplanned events. Besides the lower barriers to get there (organisational, regulatory,…), the potential cost savings are also higher! Let’s make sure non-specialised executives are aware of this.

Part removal economics

You just installed the latest and greatest in predictive analytics and out come the first results. Now what?

Interpretation of the forecast is key. First and foremost it is crucial to understand that no two forecasts mean the same. Let’s take a couple of examples to see how specific forecasts could mean very different things to your operations. Parts A and B are both forecasted to fail with a equal likelihood over the next two weeks. Should I remove these parts? The economic rationale behind this decision is often overlooked when companies set rules like “whenever the failure probability is over xx%, replace the part”. 

Let’s look at some of the variables (not exhaustive) which should not be overlooked:

Criticality; are parts A and B equally critical or is one maybe even not critical at all? Take IFE (In Flight Entertainment) for example. For one airline, individual failure may not be critical and they’ll have a rule that up to x (typically one or two) screens may be out of order before the failure causes a no-go situation. For another airline, customer experience may be the number one KPI and therefore IFE screens become AOG parts – one failure and the aircraft’s grounded. In light of this different handling of errors, the 70% indicator may lead to a pre-emptive maintenance or not. Criticality becomes even greater when safety is concerned and in some cases, the slightest hint at something failing may (or should) cause an intervention.

Cost of the intervention: if parts A and B have the same chance of failing but the cost of replacing (or even inspecting) both parts differs greatly, this may lead to different decisions on whether or not to perform the check. Some parts are readily accessible or sit just behind a hatch whereas to access other parts, one may have to dismount part of the asset. Taking this factor into account it is pretty obvious that a 2 minute intervention versus a 2 hour intervention may get different thresholds set as to what confidence level is required from the prediction before action is taken.

Waterfall costs: this is a very interesting one. Some parts may be very inexpensive but failure of that part may cause a bigger asset to stop, or worse, may cause downstream damage. The impact of a 10$ part can thus easily run in the $1,000’s. Failing to capture the full impact by only looking at individual part economics leads to very poor decisions. In this field, input from subject matter experts is key to grasp the total situation. It is very much like putting together a failure framework based on the in-service BOM. The easiest example is a production line; if one machine fails, the whole line comes to a halt and the cost avoided by maintenance (by not intervening) may lead to a huge cost on the production side. Beware of company silos when taking these decisions!

Availability of a replacement part: Like you wouldn’t want to fly on a plane without radar, no decision should be taken blindly. As the previous paragraph has shown about the importance of cross-company information flow, so it is with maintenance. An asset shouldn’t be stopped if the required part (or even the mechanic) is not available and similarly, the predictive analytics side should “warn” the parts department about imminent failure risk in order for them to get the logistics moving to get the part there on time. Systems should take to each other as the outcome of one determines (or at least should) the actions proposed by another.

The bottom line is that, just like supply chain is moving to Integrated Business Design, so should maintenance. The overall economic impact to the company bottom line and the impact to corporate objectives should prime over departmental KPI’s. Now, nobody wants to run a department whose KPI’s get negatively impacted. Therefore, change management and careful objectives design should be part of any move towards predictive maintenance (or any business transformation for that matter). The potential benefits are too great to let them get wasted on corporate politics or underperformance due to poor goal setting.

To wait or not to wait, that’s the question

With everybody talking about predictive analytics, what should I do as manager of an aftermarket division?

Before launching any project, one should carefully evaluate one’s current and projected future position with regards to aftermarket services; are these critical to your competitive offering or not? Some companies choose to sell their goods and leave aftermarket services to third parties – i.e. the traditional automotive business model. Fair enough although even traditional approaches get challenged regularly – either by opportunity (i.e. to increase grasp on the customer or increase margins) or by obligation (competitor offers the service, so should I).

Whatever the reason or current position vis à vis predictive maintenance models, slowly but surely, the market is evolving in the direction of these approaches. So the question is not so much ‘if’ but rather ‘when’ one should launch a PdA project. Here, too, I would like to propose two main drivers for determining the right moment: maturity and opportunity. 

Let’s start with the latter: opportunity. Innovation is more often than not the driver for competitive advantage and great leaders know this. This is why many CEO’s get personally involved in launching PdA projects: they’ve understood the competitive edge this may give them over less capable players. While traditional approaches still operate reactively, forward-looking companies want to provide their clients with tools or services which tell them when and how they should maintain their equipment to guarantee optimal productivity. This can be delivered pro bono, purely as a competitive advantage or through gain-share models such as PBH (Power By the Hour) or PBL (Performance Based Logistics). 

Maturity is another factor playing a major role in deciding whether or not one’s company is ready to embark on a transformation from reactive to proactive support. Maturity should be checked on an organisational level (change management will be key!) as well as on a structural level (do we have the data/infrastructure to drive this transformation?). Because the move towards proactive aftermarket models is so transformational, more and more business adopt an approach which lowers the project risk:

  • flexibility: these projects are run in the cloud in order to avoid huge upfront infrastructure investments
  • speed: beware of projects requiring many data scientists – results should pop up in weeks and not months
  • applicability: projects should provide actionable results, not just forecasts

This modular approach allows companies to embark on PdA projects without sinking millions of dollars and a lot of company time in projects which deliver half-baked results. A friend of mine who leads a large consulting practice recently told me it is for the similar reasons they get more requests now for best-of-breed solutions than for big roll-outs from top vendors: speed, flexibility, commitment (every customer matters more to BoB vendors), expertise.

To conclude: whether or not you decide to wait will probable not change your company’s maturity level very much but it will have a huge impact on the opportunity as there’s bound to be a competitor already in the works with a PdA project. So, besides not being about ‘if’, I don’t think ‘when’ is a real option either. Just be smart about ‘how’ you approach this subject; start with a limited, small-scale project to determine what works best for your business. But above all: get moving!

Help, my data’s all over the place!

One of the main problems with any corporate IT project is getting all the data to the system. Data is generated in a multitude of systems and stored in just as many places and formats. Some data gets duplicated and/or transformed before stored in other systems. Rarely do even corporate IT departments know what is stored, where and in what format. Sometimes data is stored and nobody seems to know what it means… 

I visited a major bank recently and just for calculating their portfolio risks, they estimated some 7,000 spreadsheets were being used – I’d hate to be in the shoes of their risk officer (and I’d also be scared to be a shareholder)!

The traditional approach for gathering data for a specific usage has been to set up queries to extract data from source systems in a pre-defined format. This approach is not only long, it is also prone to errors and not very flexible (if the source data changes, adaptations need to be made to the integration). New technologies allow for the creation of data lakes (unstructured data storage) however. This is much faster and retains full flexibility on all the data.

The first result of grouping all (or as much as possible) data is to create visibility. In our case: how many delays, how many breakdown, which parts are causing most problems, etc. The capability to make problems visible allows huge savings (a major European railroad reported that such a project saved them 8% on their maintenance costs) because it allows people to make better decisions, avoids ‘flying blind’ or even avoids abuse (there’s no more hiding).

Once all the data is centrally accessible, projects such as predictive analytics can be launched with less effort. Provided the data centralization environment is up to the task, it should be flexible enough to allow for a stepped approach; i.e. just bringing together all corporate data first (ERP, EAM, CRM, planning,…) and adding equipment sensor data at a later stage. Those familiar with the traditional approach know how painful this can be as it would essentially require the definition of a new data structure. This is avoided by the big data approach.

What’s the conclusion? Don’t be put off any longer by data integration pains; choosing the right technology and approach allows for rapid, performant, flexible and appropriate deployment of a data storage architecture which can be the foundation of your predictive analytics project.

Doing so will allow you to optimise:

  • visibility; before setting off on any data analytics project, make sure you have optimal visibility over existing data (volume, quality, etc.)
  • project efficiency: the stepped approach delivers value faster 
  • consistency: data centralisation lowers IT landscape dependencies
  • flexibility/adaptivity: the tiered approach allows for application adaptation to changing requirements without requiring re-integration
  • functionality: new functions can be developed and rolled out gradually while retaining a single (centralised) data source

Faster,  incremental results with lower project risk leads to higher NPV (Net Present Value) – the only measure that should really matter!