The Missing Link in Why You’re Not Getting Value From Your Data Science

The Missing Link in Why You’re Not Getting Value From Your Data Science

by Robert Morris, Ph.D.

DECEMBER 28, 2016

Recently, Kalyan Veeramachaneni of MIT published an insightful monologue in the Harvard Business Review entitled “Why You’re Not Getting Value from Your Data Science. The author argued that bfbusinesses struggle to see value from machine learning/data science solutions because most machine learning experts tend not to build and design models around business value. Rather, machine learning models are built around nuanced tuning and subtle, yet complex, performance enhancements. Further, experts tend to make broad assumptions about the data that will be used in such models (e.g., consistent and clean data sources). With these arguments, I couldn’t agree more.



At Predikto, I have overseen many deployments of our automated predictive analytics software within many Industrial IoT (IIoT) verticals, including the Transportation industry. In many cases, our initial presence at a customer is in part due to limited short-term value gained from an internal (or consulting) human driven data science effort where the focus had been on just what Kalyan mentioned; a focus on the “model” rather than how to actually get business value from the results. Many companies aren’t seeing a return on their investment in human driven data science.

wallThere are many reasons why experts don’t cook business objectives into their analytics from the outset. This is largely due to a disjunction between academic expertise, habit, and operations management (not to mention the immense diversity of focus areas within the machine learning world, which is a separate topic altogether). This is particularly relevant for large industrial businesses striving to cut costs by preventing unplanned operational downtime. Unfortunately, the bulk of the effort in deploying machine learning solutions geared toward business value is that one of the most difficult aspects of this process is actually delivering and demonstrating value to customers.


In the world of machine learning, over 80% of the work revolves around cleaning and preparing data for analysis, which comes before the sexy machine learning part (see this recent Forbes article for some survey results supporting this claim). The remaining 20% involves tuning and validating results from a machine learning model(s). Unfortunately, this calculation fails to account for the most important element of the process; extracting value from the model output.

In business, the goal is to gain value from predictive model accuracy (another subjective topic area worthy of its own dialog). We have found that this is the most difficult aspect of deploying predictive analytics for industrial equipment. In my experience, the breakdown of effort required from beginning (data prep) to end (demonstrating business value) is really more like:

40% Cleaning/Preparing the Data

10% Creating/Validating a well performing machine learning model/s

50% Demonstrating Business Value by operationalizing the output of the model

The latter 50% is something that is rarely discussed in machine learning conversations (with the aforementioned exception). Veeramachaneni is right. It makes a lot of sense to keep models simple if you can, cast a wide net to explore more problems, don’t assume you need all of the data, and automate as much as you can. Predikto is doing all of these things. But again, this is only half the battle. Once you have each of the above elements tackled, you still have to:

Provide an outlet for near-real-time performance auditing. In our market (heavy industry), customers want proof that the models work with their historical data, with their “not so perfect” data today, and with their data in the future. The right solution provides fully transparent and consistent access to detailed auditing data from top to bottom; from what data are used to how models are developed, and how the output is being used. This is not only about trust, but it’s about a continuous improvement process.

Provide an interface for users to tune output to fit operational needs and appetites. Tuning output (not the model) is everything. Users want to set their own thresholds for each output, respectively, and have the option to return to a previous setting on the fly, should operating conditions change. One person’s red-alert is not the same as another’s, and this all may be different tomorrow.

Provide a means for taking action from the model output (i.e., the predictions). Users of our predictive output are fleet managers and maintenance technicians. Even with highly precise, high coverage machine learning models, the first thing they all ask is What do I do with this information? They need an easy-to-use, configurable interface that allows them to take a prediction notification, originating from a predicted probability, to business action in a single click. For us, it is often the creation of an inspection work order in an effort to prevent a predicted equipment failure.

Predikto has learned by doing, and iterating. We understand how to get value from machine learning output, and it’s been a big challenge. This understanding led us to create the Predikto Enterprise Platform®, Predikto MAX® [patent pending], and the Predikto Maintain® user interface. We scale across many potential use cases automatically (regardless of the type of equipment), we test countless model specifications on the fly, we give some control to the customer in terms of interfacing with the predictive output, and we provide an outlet for them to take action from their predictions and show value.

As to the missing 50% discussed above, we tackle it directly with Predikto Maintain® and we believe this is why our customers are seeing value from our software.


Robert Morris, Ph.D. is Co-founder and Chief Science/Technology Officer at Predikto, Inc. (and former Associate Professor at University of Texas at Dallas).

Predictive maintenance is not for me…


… because that’s not how it’s done in our market
Well, what shall I say? Prepare for the tsunami! While it’s true that switching (fully or partially) to predictive maintenance requires a transformation of how business is done, one thing is for sure: in every (!) market, there’s bound to be a player who moves first. And predictive maintenance has such disruptive potential that the first-mover advantage may be much larger than many executives suspect. The truth in the statement lies in the fact that many stakeholders, not in the least the mechanics, need to be taught how to deal with the prescriptions resulting from predictive analytics. And this – like most change processes – will require determination, drive and  a lot of training in order to make the transition successful. I believe it was Steve Jobs who once said something along the lines of ‘People don’t know they need something until I show it to them’. The same goes for predictive maintenance; it’s not whether the market (any market!) is moving that way, it’s about when and who will be leading the pack.
… because we don’t have data
Aaah, the ‘garbage in, garbage out’ argument. Obviously this is a true statement. But ‘we don’t have data’ typically points to different issues than what the statement says; most often it refers to either the fact ‘there is data but we don’t know where’ (as it’s often spread across many different systems – ERP, CRM, MRO, MMO, etc.), or it points to the fact that ‘we have no clue what you need’. Predictive analytics are associated with Big Data and, while often true that more data is better, this doesn’t have to be the case. I’ve seen many, surprisingly good, predictions from very limited data sets. So what’s key here? Work with partners who can easily ingest data from different sources, in different formats, etc. Also work with partners who can point you towards the data you need; you’d be surprised at how much of it you already have.
… because we don’t have sensors
A refinement on the previous objection and this one slightly more to the point. Many predictive maintenance applications  – but not all – require equipment status data. However, as we’ve seen from the first objection, a full roll-out of predictive maintenance approach may have to overcome organisational hurdles and take time. Therefore, start with the low-hanging fruit and gradually get a buy-in from the organisation through early successes. What’s more, upon inspection, we often find plenty of sensors, either unbeknownst to the client or in situations where the client doesn’t have access to the sensor data (in many cases, they do have access to the data but can’t interpret it – a case where automated data analytics can help!).
… because we don’t have a business case
This is worrying for a number of reasons; not the least because companies should at least have investigated what the potential impact could be to their business of new technologies, business concepts, etc. The lack of a business case make come from a lack of understanding of predictive maintenance and unlike as described in the last argument, a denial of this lack of understanding. As we know by now, one of the main (but not only) benefits of predictive maintenance is the moving of unplanned maintenance to planned maintenance. According to cross-industry consensus, unplanned maintenance is 3x-9x more expensive compared to planned maintenance. The impact of moving one to the other is obvious! However, many companies fail to track what’s planned and what’s unplanned and therefore have no idea on potential savings that could be generated by predictive analytics. Lacking this KPI, it also seems impossible to assess other impacts such as customer satisfaction, employee satisfaction, reliability improvements, etc.
… because our maintenance organisation is already stretched out
Let’s say we have a prediction for a failure and want to move it to planned maintenance (the event is still taking place but it’s nature changes). A commonly heard objection is ‘but my maintenance planning is already full’. Well, what would you do when the failure occurs? In that case, a maintenance event would still take place, only it would either upset the maintenance planning or would be executed by a specific ‘unplanned maintenance’ intervention team. Either way, time and money can be saved by predicting the event and executing the maintenance during a planned downtime. As a matter of fact, moving unplanned to planned actually frees up maintenance time and resources (to be totally correct: after an initial workload increase to catch up to the zero point). The opposite is actually called ‘the vicious circle of maintenance’: unplanned events disrupt maintenance planning, which is therefore often not fully or perfectly executed, which in turn generates more unplanned events, etc.
… because we don’t trust the predictions
Humans are not wired for statistics! While we have a natural propensity for pattern recognition, this talent also tends to fool us into wrong conclusions… Any predictive maintenance (or, by extension, any predictive analytics project) initiative should be accompanied by good training to make sure predictions are interpreted correctly. Even better, business apps tuned towards individual job requirements which translate the prediction into circumstantial actions should be developed and deployed. At the current state of affairs, starting with a limited scope project (call it a pilot or POC), should allow the delivery team to validate the presence of enough and good enough data, build the business case, etc.
… because we don’t understand predictive maintenance
Well, at least they’re being honest! Companies that are aware of their lack of understanding are halfway down the path towards the cure…
Understanding predictive maintenance starts with understanding predictions (see above) but then also seeing how this affects your business; operationally, financially, commercially, etc. Predictive, and by extension prescriptive, maintenance has  far-reaching impact, both internal and external (non-predictive approaches will have a hard time competing with well-implemented predictive maintenance organisations!).
These are, by far, not all the objections we encounter but they give a general idea of how/why many organisations are scared of the currently ongoing change. As with so many technological or business advances, it’s often easier to formulate objections that to stick one’s neck out and back up the new approach. However, as history has shown us (in general and with regards to maintenance as shown by the evolution from reactive > proactive > condition based), maintenance is inevitably evolving this way. Getting it right is hard but not getting it is expensive! We’ll inevitably get front-runners and a flock of successful followers but many of the late starters will simply be run over by more efficient and effective competitors. Business is changing at an ever-faster pace and executives have to be ever-more flexible at adapting to the changing environment. Make sure you get one or more trustworthy partners to introduce you to the concepts and tools for predictive maintenance. At this time of writing, the front-runners are already out of the starting blocks!

Dreaded NFF

NFF (No Fault Found) is an often-used KPI to which may get a whole new meaning with the introduction of predictive maintenance. Let’s go back to the origin of this KPI. Some two decades ago it gained in attention as companies increasingly focused on customer satisfaction, people found out that many so-called ‘bad parts’ that were injected in the reverse supply chain tested perfectly and were therefore flagged NFF. There has been an ongoing struggle between the field organisations’ and the reverse supply chain’s goals. Field service did all it could to increase the number of interventions per engineer per day, even at the expense of removing too many parts, under the motto that time is more expensive than parts. Removed parts then get injected in the reverse supply chain where they’re typically get sent to a hub to be checked and subsequently repaired. To the reverse logistics/repair organisation, NFF create ‘unnecessary’ activity – parts needing to be checked without any demonstrable problem being uncovered. These parts then need to be re-qualified: documented, repackaged,… Therefore, NFF is really bad to the observed performance of that organisation.

Back to predictive analytics: whereas CBM (Condition Based Maintenance) will order for a maintenance activity based on the condition of the equipment/part – and therefore do so when there’s demonstrable cause for concern – predictive maintenance will ideally generate a warning with longer lead time. Often before any signs of wear/tear become apparent! Provided certain conditions are met (see previous posts on criticality/accuracy/coverage/effort), parts will be therefore be removed which technically will be NFF! Because the removal of these parts will prevent a much costlier event this is not a problem per se, but it will require rethinking not only internal and external processes but also KPI’s! If we want adoption of these predictive approaches to maintenance and operations, KPI’s need to be rethought to reflect the optimised nature of these actions. We can’t allow anybody to be penalised for applying the optimal approach!

Predictive (or by extension, prescriptive) maintenance has huge potential for cost savings and as we’ve seen before (see previous blog entries), these savings should be looked at from a holistic point of view. Some costs may actually go up in order to bring down overall costs. Introducing such methodologies therefore also demand a lot of attention to process changes and to how people’s performance is measured. The good news is that introduction of predictive maintenance can be gradual; i.e. start with those areas that offer high confidence in the predictions and high return. Nothing helps adoption better than proven use cases!

Today’s focus area for operations: increase uptime!

As other domains such as procurement, supply chain, production planning, etc. get increasingly lean, attention focuses on the few remaining areas where large gains are expected from increasing efficiency. Fleet uptime or machine park uptime is thé focus area today. Indeed, investors increasingly look at asset utilisation to determine whether an operation is run efficiently or not. As we know, in the past many mistakes have been made by focusing on acquisition cost at the cost of quality. This has led to a lot of disruption with regards to equipment uptime which, in turn, renders inefficient any of the lean initiatives mentioned above. So, what are the important factors determining uptime? We’ll look at the two most important ones:

– reducing the number of failures

– reducing time to repair (TTR)

Reducing the number of failures sounds pretty obvious: purchase better equipment and you’re set. Sure, but how do you know the equipment is better? Sometimes, it’s easily measurable; i.e. I’ve known a case where steel screws were replaced by titanium ones. Although the latter were maybe five times more expensive, their total cost on the machine may have been less than 1,000$ whereas one failure caused by a steel screw cost 25,000$. Taking an integrated business approach to purchasing saved a lot of money over the lifetime of the equipment. In other cases, the extra quality is hard to measure and one has to trust the supplier. This ‘trust’ can be captured by SLA’s, warranty contracts or even fully servicized approach (where the supplier gets paid if and when the equipment functions according to a preset standard).

Number of failures can also be reduced by improving maintenance; pretty straightforward for run of the mill things such as clogging oil filters, etc. One just sets a measurement by which a trigger is set off and performs the cleaning or replacement. This is what happens with your car; every 15,000 miles or so certain things get replaced, whatever their status. The low price of both the parts involved and the intervention allows for such an approach. Things become more complex when different schedules need to be executed on complex equipment: allow all of the triggers to work independently (engine, landing gear, hydraulics, etc. on a plane for instance) may cause maintenance requirements almost every day. At least some of these need to be synchronised and ideally, the whole maintenance schedule should be optimised. Mind you, optimisation doesn’t necessarily mean a minimisation of the number of interventions! It should rather focus on minimising impact on operational requirements.

In order to further reduce the number of failures, wouldn’t it be great if we could prevent those events that occur less often? This involves predicting the event and prescribing an action in order to minimise its impact on production. This is exactly the focus of prescriptive maintenance; combining predictions (resulting from predictive analytics) with cause/effect/cost analysis to come up with the most appropriate course of action. Ideally, if maintenance is prescribed, it enters the same optimisation logic as described above. Remember, the goal is to optimise asset utilisation.

Reducing TTR is too often overlooked or just approached by process standardisation. However, many studies have shown that TTR is highly impacted by the time it takes to diagnose the problem and the time to get the technician/parts on site – especially in the case of moving equipment. Predictive analytics may help reduce both: the first, by providing the technician with a list of the systems/parts most at risk at any moment in time and the second by making sure the ‘risky’ parts are available. There’s nothing worse than having to set in motion an unprepared chain of actors (technical department, supplier, tier 1,…) for tracking down a hard to find part. This is even worse when the failing machine slows down or halts an entire production chain…

Poor ROA (Return On Assets) is often a trigger for takeovers because the buyer is confident they can easily improve the situation. It’s one of the telltale signs of a poorly run or suboptimal operation and has to be avoided at all cost. If your sights are not yet set on this domain, chances are other people’s are!

What did the Coffee Pot say to the Toaster?

The Internet of Things (IoT) is at the precipice of the Gartner Hype cycle and there is no shortage of the “answers to everything” being promised. Many executives are just now beginning to find their feet after the storm wave that was the transition from on-premise to cloud solutions and are now being faced with an even faster paced paradigm shift. The transformative tidal wave that is IoT is crashing through CEO, CTO, and CIO’s offices and they are frantically searching for something to float their IoT strategies on but often are just finding drift wood.


Dr. Timothy Chou and his latest book Precision: Principles, Practices, and Solutions for the Internet of Things is your shipwright. The framework presented by Dr. Chou cuts through the fog that surrounds IoT and provides a straight forward no jargon explanation of IoT and the power that is harnessable. Dr. Chou then goes on to present a showcase of case studies that are real life profitable IoT solutions by a variety of traditional and hi-tech businesses.

One of the case studies Dr. Chou features is based on my work at New York Air Brake where we utilized instrumented and connected locomotives to create the world’s most advanced train control system that has saved the rail industry over a billion dollars in fuel, emissions, and other costs. It was this work that gave me a taste of the power IoT has and gave me the passion to want to make a bigger impact in the rail and transportation industries utilizing IoT data and thus join the Predikto family.

A predictive maintenance example

A prediction doesn’t mean that something will happen! A prediction merely says something may happen. Obviously, the more accurate that prediction gets, the closer it comes to determining something will happen. Yet, we often misinterpret accuracy or confidence in a prediction; when something has 20% chance of failing or 90% chance of failing, we often mistake the result of the failure for the chance of failing. In both cases, when the failure occurs, the result is the same; it is only the frequency of this failure happening that changes.

What I describe above is one of the reasons why managers often fail to come up with a solid business case for predictive analytics. Numbers – and especially risk-based numbers – all to often scare off people when they’re really not that hard to understand. Obviously, the underlying predictive math is hard but the interpretation from a business point of view is much simpler than most people dare to appreciate. We’ll illustrate this with an example: Company A is in the business of sand. Could hardly be simpler than that. It’s business consists of unloading barges, transporting and sifting the sand internally and then loading it onto truck for delivery. To do this, they need cranes (to unload the ships), conveyor belts and more cranes (to load the trucks). Some of these items are more expensive (the ship-loading cranes) or static (the conveyor belts) than others (the truck loading cranes). In this case, this has led to a purchasing policy which has focused on getting the best cranes available for offloading the ships (tying down a ship because the crane is broken is very expensive), slightly less stringent on the conveyor belts (if it’s broken, at least the sand is on our yards and we can work around the failures with our mobile cranes) or downright hedged by buying overcapacity on the, cheaper, mobile cranes. This happens quite often: the insurance strategy changes with either the value of the assets as well as with their criticality to the operations. Please also note that criticality goes up with diminishing alternatives… A single asset is typically less critical (from an operational point of view) when part of a fleet of 100 than if it were alone to perform a specific task.

All these assets are subject to downtime; both planned and unplanned. We’ll focus on the unplanned downtime. When a fixed ship-loading crane fails, the ship either can’t be off-loaded any more or it has to be moved in reach of another such cranes (if that one’s available). Either way, the offloading is interrupted and the failure not only yields repair costs (time: diagnose, get the part, fix the problem – parts – people) but also delays the ship’s departure, which may result in additional direct charges or costs due to later bay availability for incoming ships. When a conveyor belt breaks down, there’s the choice of waiting for it to be repaired or for finding an alternative such as charging the sand on trucks and hauling it the processing plant. Both situations come at a high cost. Moreover, both the cranes and the conveyor may cause delays for the sifting plant, which is probably the most expensive asset on site whose utilisation must be maximised! For the truck loading cranes, the solution was to add one extra crane for every 10 in the fleet. That overcapacity should ensure ‘always on’ but comes at the cost of buying spare assets.

Let’s now mix in some numbers. Let’s say a ship-loading crane costs €5,000,000; a conveyor costs €500,000 and a mobile crane costs €250,000. The company has three ship docks with one crane each, 6 conveyors and a fleet of 20 mobile cranes, putting their total asset value at €22,000,000. If we take a conservative estimate that 6% of the ARV (Asset Replacement Value) is spent on maintenance, this installed base costs €1,320,000 to maintain every year. Let’s further assume that 50% of the interventions are planned and 50% are unplanned. We know that unplanned maintenance is 3-9 times more expensive than planned so for this example we’ll take the middle figure of 6x. We can now easily calculate the cost of planned and unplanned events by: €1,320,000 = 0.5x + 0.5*6x, where x is the total planned maintenance cost. Result: of the total maintenance cost, roughly €190,000 is spent on planned maintenance whereas a whopping €1,130,000 is due to unplanned downtime! If the number of maintenance events is 200, that means that one planned maintenance event costs €1,900 and one unplanned event costs €11,300. . Company A has done all it can to optimise the maintenance processes but can’t seem to drive down the costs further and therefore just decided this is part of doing business.

Meanwhile on the other part of town… Company B is a direct competitor of Company A. And for the sake of this example, we’ll even make it an exact copy of Company A but for one difference: it has embarked on a project to diminish the number of unplanned downtime events. They came to the same conclusion that for the 200 maintenance events, the best way to lower the costs was if they could magically transform unplanned maintenance into planned maintenance. They did some research and found that, well, they could – at least for some. Here’s the deal: if we can forecast a failure with enough lead time, we can prevent it from happening by planning maintenance on the asset (or component that is forecasted to fail) either when other maintenance is planned to happen or during times when the asset is not required for production. While the event is still happening, the prevent-fix being planned costs €1,900 as compared to a break-fix costing €11,300 – that’s a €9,400 difference per event!

The realisation that the difference between a break-fix and a prevent-fix was €9,400 per event allowed them to avoid the greatest pitfall of predictive maintenance. Any such project requiring a major shift in mindset is bound to face headwind. In predictive analytics, most of the pushback comes from people not understanding risk-based decision making or people not seeing the value associated with introducing the new approach. The first relates to the fact that many people still believe that predictions should be spot-on. Once they realise this is impossible, they often (choose to) ignore the fact that sensitivity can be tuned to increase precision albeit at a cost: higher precision means less coverage (if we want to get higher prediction confidence, we can get this but out of all failures, we’ll catch a smaller portion). “If you can predict all failures, then what’s the point?” is an often heard objection.

Company B did it’s homework though and concluded that they could live with the high enough prediction accuracy at a 20% catch rate. The accuracy at this (rather low) catch rate meant that for every 11 predictions, 10 actually prevented a failure and 1 was a false positive (these figures are made up for this example). Let’s look at the economics: a 20% catch rate means that of 100 unplanned downtimes, 20 could be prevented, which resulted in a saving of 20 x €9,400 = €188,000. However, the prediction accuracy also means that for catching these 20, they actually had to perform 22 planned activities; the 2 extra events costed 2 x €1,900 = €3,800. The resulting savings were therefore €188,000 – €3,800 = €184,200; savings of more than 16% on the total maintenance budget!


What’s more, there are fringe benefits: avoiding the unplanned downtime results in better planning, which ultimately results in higher availability with the same asset base. Stock-listed companies how important ROCE (Return On Capital Employed) is when investors compare opportunities but even private companies should beware: financial institutions use this kind of KPI’s to evaluate whether or not to allow for credit and at what rate (it plays a major role in determining a company’s risk profile). Another fringe benefit – and not a small one – is that on the fleet sizing for the mobile cranes (remember they took 10% extra machines just as a buffer for unplanned events), fleet size can be adjusted downward for the same production capacity because downtime during planned utilisation will be down by 20%. Even if they play it very safe and just downsize by one crane, that’s a €250,000 one-time saving plus an annual benefit of 6% on that: €15,000!

Company B is gradually improving flow by avoiding surprises; a 20% impact can’t go unnoticed and has a major effect on employee morale. They also did their homework very well and passed (part of) the reduced operational costs on to their clients. Meanwhile, at Company A, employees constantly feel like they’re running after the facts and can’t understand how Company B manages to undercut them on price and still throw a heck of a party to celebrate their great year!

The next efficiency frontier?

Mountains of consulting dollars have been invested in business process optimisation, manufacturing process optimisation, supply chain optimisation, etc. Now’s the time to bring everything together and with all these processes optimised, our whole production apparatus utilisation rate becomes ever higher. When all goes well, this means more gets done per invested dollar, making CFO and investors happy through better ROA (Return On Assets). However efficient, this increasing load on the machine park comes at a price: less wriggle room in case something unexpected happens. When in the past, companies had excess capacity, this not only served to absorb demand variability; it also came in very handy when machines broke down by allowing the demand to be re-routed to other equipment.
There’s no more place to hide now, so there are a number of options one can consider in order to avoid major disruptions:

  • increase preventive maintenance: this may or may not help. Law of diminishing returns applies, especially as preventive maintenance tends to focus on normal wear and tear and parts with a foreseeable degradation behaviour. A better approach is to improve predictive maintenance; don’t overdo where there’s no benefit but try to identify additional quick wins. Your best suppliers will be a good source of information. Suppliers than can’t help; well, you can guess what I think of those.
  • improve the production plan: too many companies still approach production planning purely reactively and lack optimisation capabilities. Machine changes, lot’s of stop and go, etc. all add to the fragility of the whole production apparatus (not to mention they typically – negatively – influence the output quality as well).
  • improve flow: I’m still perplexed when I see the number of hick-ups in production lines because ‘things bumped into each other’. Crossing flows of unfinished parts is still a major cause of disruption (and a major focus point for top performers such as Toyota). As most plant managers why machines are in a certain place and they either “don’t remember” or will say “that’s the place where they needed the machine first” or even “that was the only place we had left”. Way too rarely do plant layouts get re-considered. Again, the best-in-class do this as often as once a year!
  • shift responsibilities: if you can’t (or won’t) be good at maintenance, then outsource it! Get a provider that can improve your maintenance and ideally can work towards your goal, which is usually not to have shinier machines but to get more and better output. If you really decide you don’t care about machine ownership at all, consider performance- or output-based contracts.
  • get better machines: sounds trivial but current purchasing approaches often fail to capture the ‘equipment quality’ axis and forget to look at lifetime cost in light of output. Just two months ago I heard of a company buying excavators from a supplier because for every three machines, they got one for free. This was presented as an assurance that the operator would never run out of machine capacity. In this case, it had the adverse effect as the buyer thought why they needed to throw in an extra machine if they claimed they were as reliable as the best.
  • connect your machines: this is a very interesting step. Recognising that machines will eventually fail but at least making sure you get maximum visibility on what/where. Most of the time resolving equipment failures is spent… waiting! Waiting for the mechanic to arrive, waiting for the right part, etc.
  • add predictive analytics: predictive analytics not only allow you to prevent failures from happening but, relating to the previous point, to the what/where axis, predictive analytics allows the addition of why. Determining why something failed or will fail is crucial in optimising production output. Well-implemented predictive analytics allow us to improve production continuity by avoiding unplanned incidents (through predictive maintenance) but also allows for more efficient (faster) and effective (resulting in better machine uptime) maintenance.

So which of these steps should we take? Frankly, all of them. Maybe not all at once and (hopefully) some of them may already have been implemented. Key is to have a plan. Where are we now, what are our current problems, what are we facing,…? Formulating the problem is half the solution. Then – and this may surprise some – work top down. Start with the end goal, your “ultimate production apparatus”, and work your way back to determine how to get there. All too often people start with the simplest steps without having looked at the end goal and after having taken two or three steps they find out they need to backtrack because they took the wrong turn earlier in the process.

At any step, whether it’s purchasing equipment or to install sensors or whatever, look at whether your supplier understands your goals and is capable of integrating in “the bigger plan”. The next efficiency frontier is APM: Asset Performance Management. Not individually, but from a holistic point of view. While individual APM metrics are interesting for determining rogue equipment, only the overall APM metrics matter for the bottom line; did we deliver on time, was the quality on par, at what cost,…