Last week, we kicked off this mini-series of articles on metrology, imaging and characterization[1] outsourcing. Today, we turn to some of the finer points of a key process driving those outsourcing decisions – the analysis determining whether you should insource or outsource a particular analytical activity (data acquisition and / or analysis), a.k.a, the “buy vs. build†analysis.
QUICK LINKS
#1 – Carefully consider the “all-in†costs.
#2 – Consider frequency of use. Realistically. Near-term and into the future.
#4 – Collaborate with your potential outsource partner on the analysis.
#5 – Be mindful of the incentives.
#6 – No matter the decision, you should still qualify a preferred outsource partner.
#7 – And, finally, don’t confuse “insource†with “in-house.â€
Introduction
Outsourcing decisions are nested within a company’s broader metrology strategy – an often overlooked and usually under-appreciated driver of technology innovation success or failure. Metrology is the sensory feedback system for most any new technology development program. A well-placed investment in metrology can return manyfold in terms of time-to-market and product competitiveness.
Many corporate labs are riddled with unused and barely used instruments. These analytical corpses often reflect of a poorly executed Buy vs. Build, and are the embodiment of wasted capital, resources, time, and effort.
Constructing an honest, thorough, and strategic Buy vs. Build is very difficult. The process is fraught with complexity, hidden incentives, and incomplete information. And it requires imagination – projecting needs forward into an uncertain future. Doing it well can ultimately be a creative, healthy exercise that challenges operational and technical assumptions that might otherwise have been left unexamined.
A note of caution: my editors warned me that this piece is too long – that modern audiences can’t sit down and focus for 5 to 10 minutes of uninterrupted reading. No matter. I am old school, and I think highly of this community. However, just in case, and to provide a little added incentive, I will tip my hand by letting you know that there is a twist at the end of this installment. For, while you are wise to view this as a naked pitch for Covalent to be your analytical partner, you may be surprised by what that can mean!
So, without further ado, here are 7 tips to improve your Buy vs. Build for analytical services.
#1 – Carefully consider the “all-in†costs.
This is basic to any Buy vs. Build, but merits special emphasis. Owning tools and building teams involves more expense than the initial capital purchase and subsequent monthly payroll. Consumables, maintenance contracts, utilities, floorspace, and service visits can add up to significant costs. Also, tool uptime needs to be factored into the analysis. When a tool is down, you either need to live without the data or outsource during that time.
When considering labor costs, don’t forget taxes, healthcare, recruiting fees, equity and variable compensation plans, other benefits, and overhead allocations for office space, computers and so on. Also, people go on vacation, get sick, or quit. So, you will need to train multiple operators, or, again, go without data or outsource at those times.
Don’t forget software! Some tools (like FTIR and XRD) generally involve purchasing or licensing of databases that can be quite expensive. And, of course, there are general licensing costs for analytical or R&D software, Microsoft Office, etc.
All of this adds up, and collectively the “other†costs are usually not trivial.
Finally, consider the cost of flexibility. Flexibility is hard to quantify, but remember that, once spent, capital is gone. When you hire someone, you are making a commitment to them, and they become a part of your fixed cost structure. Outsourcing is intrinsically more flexible, and that can matter.
#2 – Consider frequency of use. Realistically. Near-term and into the future.
Utilization rates, now and into the future, are obviously key input variable of any decent Buy vs. Build. One of the reasons so many unused tools end up in corporate labs is that people conducting the analysis unintentionally tend to overestimate the frequency of need.
Here are a few potential causes of that over-estimation:
- A tool is necessary to answer a key question or solve a key problem. But, once solved or answered, the instrument is no longer deemed critical.
- An instrument may be purchased without really understanding its limitations or true capabilities. Maybe the data provided ultimately turns out to be inconclusive or uncorrelated with performance.
- Development programs go through stages and cycles, and bottlenecks move around. There are times when we get a surge of business from a customer to support a program, and then they are quiet for 3 months. That does not mean they were unhappy – just that they are focused elsewhere during that time. So the average utilization for an instrument supporting such a program may be quite low.
- Analytical instruments can get attached to specific engineers. And, when that engineer leaves the company, the instrument can become orphaned if the rest of the team doesn’t care as much about that data, doesn’t fully understand the technique, or doesn’t know how to operate the tool.
We all tend to suffer from recency bias, and the hot topic of late can easily be projected to a perceived long-term need. But things change, and development projects evolve. Be sure you a) really need the instrument for a sustained period of time, and b) know exactly how you will use the data, and how useful it is, before you commit to the purchasing and operating the tool.
#3 – Overvalue Time.
I believe it is true that the most successful hard-tech innovation companies these days attack development projects with outrageous urgency. What is one day worth? Depends on the project, company and industry. But, I can pretty much guarantee that it is worth a lot. If the data and analysis is holding up progress, you should probably be approaching its generation with a hair-on-fire attitude.
For frequent characterization needs, consideration of speed most always favors insourcing – at least when referencing the traditional outsourcing business model.
Turnaround times for standard price jobs can range from 3-7 days to 2-4 weeks, depending on the service provider and the project details (with Covalent of course being on the quick end of the spectrum). For the occasional analytical emergency, outsourcing service labs often offer same day / next day / 2-day turnaround by applying some sort of RUSH surcharge.[2] Expedite fees are sometimes necessary and worthwhile, and companies avoiding them may be acting penny wise and pound foolish, as they say. But, for frequent needs, those fees would add up very quickly. As a result, a cursory Buy vs. Build exercise for high volume data needs inevitably tilts heavily towards insourcing, assuming the company can afford to do so.
But, watch out! There is a potential pitfall here. In many cases, there is no need to pay expedite fees to get fast data. Cost vs. Speed can be a false trade-off. You may very well be able to get high-speed, high-quality data and analysis at even lower costs than standard turnaround time projects!
How? Well, let me tell you…
#4 – Collaborate with your potential outsource partner on the analysis.
It drives me crazy when I hear customers bought a tool based upon bad data.
Imagine a customer is considering buying an XRD for pretty frequent materials screening and analysis. Their boss tells them to collect a quote from Covalent (and maybe a few other labs). They don’t tell us the context. And come back saying that it would be (let’s say) $1000 / sample with a 3-5 day turnaround time. RUSH service would be available for 2x that amount. The CFO, mindful of the need to get data every day and having read #3 above, plugs in $2000 x a certain high volume of samples, and… presto! It makes sense to buy the tool!
This was a fundamentally flawed analysis, and was unfair to both companies.
To conduct a proper analysis, you should attempt to really sit down with the potential outsource lab partner and explain your need in detail. Imagine you are looking for the following:
- High volume data with no more than 24 hours turnaround time.
- No report needed. No analysis needed, or automated analysis developed.
Outsourced analysis companies love higher volume work that can be operationalized, optimized and even automated. These sorts of engagements can provide baseload revenue that is particularly valuable given the variable nature of our demand. After some discussion around operational planning, batch sizes and sample variability, cancellation policies and such, you should be able to strike a much better commercial deal in terms of price AND turnaround time.
We have customers bringing us samples every day requiring us to provide next day turnaround time – not only with no expedite fees, but at a significant discount to standard work.
Optimization of what is possible requires open communication and creative problem solving.
#5 – Be mindful of the incentives.
Many of our best customers run customer internal labs. They are amazingly talented people doing important work. And we are grateful to work with them. But as an executive asking someone to run some analysis or even make day-to-day decisions about what to insource vs. outsource, you need to be mindful of the incentives of the people conducting the analysis and at least consider those in the final decision-making process. In its worst instances, people can skew analysis in favor of the outcome that gives them more control, power, prestige, or just intellectual engagement. Perspective matters in these things, and should be understood and considered.
#6 – No matter the decision, you should still qualify a preferred outsource partner.
Internal demand can vary wildly, leading to occasional long queue times, or to certain groups being de-prioritized and losing access to the internal lab. Tools go down from time-to-time. People leave.
Given inherent variability and uncertainty and supply and demand, it just makes sense to have a qualified outsource partner that can work with you to absorb the ebbs and flows of analytical work.
Above and beyond the demand-balancing aspects, there are technical reasons to involve an outsourced partner. Engineers in our business might just surprise you with solutions and innovative approaches unknown to the internal team. Remember, your outsource partner works all day, every day with customers across a range of applications. We see a few things! And we can be a key resource to improve your internal capabilities.
#7 – And, finally, don’t confuse “insource†with “in-house.â€
This one might blow your mind a bit in a sort of M. Night Shyamalan / Sixth Sense sort of way.
As you have been reading this article, did you imagine that by “outsource†I have been referring to sending samples to an outside lab? And that by “insource†I meant keeping the analysis and instrument in-house?
No!
Outsourcing and “sending samples out†need not be equivalent.
Does your company own the copy machine in your office? Probably not. Maybe that HR or IT professional sitting down the hall is a contractor employed by a dedicated outsource firm. What is commonplace in other industries is somehow almost unheard of in our industry.
I can’t speak for our competitors, but Covalent is more than happy to explore a range of potential arrangements that integrate the support you need, where and when you need it. Your operator or our operator, your tool or our tool, your location or our location. All combinations are possible! Modern instruments can be connected to the cloud and, in many cases, be run very effectively and securely from a remote location. Imagine having an instrument operator who never gets sick and never quits. Or an instrument in your lab that is seamlessly backed up by the same instrument off-site and therefore essentially never goes down.
Ideally, you really shouldn’t care about where the tool is or who operates it. What you most certainly need is a variable, but broad, scope of high quality, reasonable cost, quick turn data, reliably available, when and as-needed, that can equip your development engineers with all the insight and information they might possibly need to make great decisions.
So, there you have it. That is my list. You may notice a common theme of imagination, creative problem solving, and collaboration. Don’t assume you know that answer going in.
Give me a call or shoot me a note. You may be surprised by what is possible.
Footnotes
[1] Hereafter just referred to as “metrology†for the sake of simplicity.
[2] I have seen this ranging from 50% to 300% depending on the service company and circumstances.