Over the years serving in various capacities on both sides of the table on analytical projects, I have accumulated observations and opinions about how to successfully engage outsource analytical service providers. Today’s article enumerates and describes outsourcing mistakes that I have seen repeated over and over.
When I started this series of articles, this segment was originally titled, “5 Common Outsourcing Mistakes.” But in writing the article and further reflecting on the topic, a couple of additional mistakes presented themselves and strike me as worthy of mention.
So, without further ado, here are Seven Common Outsourcing Mistakes for your consideration:
- Skipping it / hoping things improve
- Pre-Project: poor planning, internal communication, and alignment
- Engaging: counter-productive secrecy
- Scoping: failure to explore the possible
- Follow-up: not having any, or walking away confused
- Wasting the data
- Having no corporate strategy
1. Skipping It / hoping things improve
My old Big Boss at Applied Materials was fond of saying “Hope is not a strategy!” I have seen countless times where engineers are struggling to figure out what is going on with their technology development program. They are debating causes and correlations. They need deeper understanding of mechanisms and behaviors. They need answers, and they lack necessary data and insights to get them.
They should not hesitate to engage outside help (contact Covalent!). But, all too often, and for a variety of reasons, engineers “skip it,” move on, try something else, and hope everything improves.
Engaging an outside lab, scoping a project, getting a quote, securing a purchase order, sending samples, reviewing results (and paying the bill!) – all seems like a big pain-in-the-backside. Wouldn’t it be easier just to run another batch of experiments and hope the results improve?
The cost of outsourcing at the point of purchase is easy to judge. And it strikes some engineers as an expensive luxury. But the “let’s try again and hope it gets better” strategy is deceptive. It seems to be free. It is not. The cost of a lost week, a lost month, a lost year – all dwarf the cost of conducting some projects with an outsourced analysis partner. To have their best chance to succeed, your team needs data, and they need to understand what it means. They need the help of analytical experts.
Inertia and inaction can be seductive. But, great engineering leaders know that knowledge and data can often be the difference between success and failure.
2. Pre-Project: Poor planning, internal communication, and alignment
A successful project begins with deliberate and careful planning, preparation, alignment and execution.
Here are some common problems that can derail the project before it even gets started.
- Alignment on project logistics, budget, timing, goals, objectives: When dealing with problems that are not well defined, communication and alignment are difficult. “Let’s send the samples out for measurement” sounds simple as instructions go. But did the assignee really, fully understand the clear intent and instructions? How many samples? To get what data, precisely? By when? How much are we willing to spend? Ideally, these questions should be discussed and agreed-upon even before seeking a quote.
- Organization and cross-team communication: It is not unusual to find out at the end of the project that the shipping department (or the customer!) sent the wrong samples.
- Have a clear hypothesis in mind: Max learning is possible when you first take the time to write-down what you expect the answer to be. As well as what some alternative answers might be, and what those would mean. Don’t just get the data and chat about it. Use the project as an opportunity to validate or challenge your view of what is happening.
Thoughtful and detailed preparing and planning makes for a great foundation to have a successful project.
3. Engaging: counter-productive secrecy
If I could make one plea to our customers, it would be to be more open. I understand perfectly well why so many engineers keep information close-to-the-vest. They fear that the service provider would knowingly or unknowingly leak key information to a competitor.
I can honestly report that I have never once heard of competitive intel leaking through the analytical lab. We are in the business of helping companies with confidential projects. It is what we do. If we are lax in dealing with confidentiality, we would not last long as a company. Also, and in case you are not comforted by my previous point, the risk is intrinsically low because we only see a small part of what the customer is actually doing. We may see what they are making, but we generally have no idea how they are making it. In almost every situation, the customer can be open about the problems without disclosing any core IP.
Weighed against the small (in my view) and vague risk is the very clear benefit of opening up and providing more detailed information and context. Simply put, if the people scoping, measuring / imaging, analyzing and reporting are made fully aware of the problem statement, previous efforts, and suspected causes, the project is more likely to succeed. By taking that risk, you get their “whole brain” thinking about your problem, looking for clues as they go. You will engage the fuller scope of their talent and attention. And you will consistently achieve better results.
4. Scoping: failure to explore the possible
Someday, perhaps, I will write an entire article just on this topic. Achieving optimal results in the analytical project often calls for some joint creativity and “exploration of the possible” – technically, operationally, and commercially.
The first potential problem comes when a customer pre-determines the analytical technique and constrains the scoping discussion in a counter-productive way. “I am looking to get some AFM data on surface roughness. What is the price and the turnaround time?” In some cases, the customer knows exactly what they want. And, in those cases, the question is fine. But, in many cases, the customer is, at least in part, guessing about the technique needed.
In those cases, rather than ask for a specific technique, it would be much better to describe the sample you have and what you are looking to understand. “I have a glass substrate and I am looking to get the surface roughness across the entire substrate.” That will spur a more involved discussion on resolution, scale of measurement, number of spots and so on. Perhaps there is a profilometry technique you were not familiar with that would be more suited to the job.
Even better than describing the characteristic you are looking to measure or analyze is to take one step back and describe the “why” of your request. “We are having adhesion problems on a deposited film, and are wondering if the problem is correlated with the surface roughness of the substrate.” Now we are talking. The analytical scientist can really dig in and help solve the larger problem. Maybe there are several potential causes to investigate, or an optimal analytical sequence of steps to pursue.
Once you have a good technical approach, you can still stumble when it comes to optimizing the operational and commercial aspects of the project. It is very common for a customer to see the commercial quote, find that it is beyond their budget, and reflexively down-scope the project to save money (usually by just reducing the number of samples).
This may be unavoidable, but it is worth some effort to defend your analytical ambition and see if some solution to the commercial gap can be found. We really do want you to get all the data you originally thought you would need. So, let’s try to work something out.
Here are two ways to do that:
- Focus on the cost drivers – reduce labor / time spent. Perhaps we assumed you need analysis and a report, but in reality you just need raw data. Perhaps we can set up the instrument to run unattended overnight, thereby saving labor costs. Maybe you can batch samples into groups that are more efficient to run back-to-back.
- Reduce uncertainty – In many cases the analytical scientist will not be truly certain about the time required or the quality of the data to be produced until they get the sample-in-hand and try it. You can be very experienced in a technique and even the general sample type, but still get surprised. Quoting the whole project in the face of uncertainty places the Covalent quoter in a tough position of guessing the expected, and worst-case, scenarios a priori. In those situations, it may be better to quote a single sample on a trial run. The customer will see the quality of the data, and Covalent engineers will better understand how challenging the sample is, or what can be done to optimize the project on our end.
Faced with a quote that you feel you can’t afford, try to work with the analytical partner to engineer a solution. Until you try, you won’t know if one is possible.
5. Follow-up: Not Having Any, or Walking Away Confused
If you spend the money and the time on engaging an outside expert, then you really should make sure you get value from the project. Not every project requires follow-up, but good outsource partners are happy to have a debrief to answer any questions and discuss next steps. Reports, when requested and provided, are not always 100% clear or understood by the reader. Or perhaps the data conflicts with some other data that has been collected.
Questions are often remaining, and clarifications are often needed. Follow-up can be an expensive element of a high-touch service business, but frequently that is where the real value is generated. Don’t waste the opportunity to really understand the results.
6. Wasting the data
Similarly, I would strongly encourage customers to think about what they will do with the data generated by the outside project.
Companies in advanced materials and nanotech invest significant resources developing robust data strategies. Every experiment run, every hypothesis posed and challenged, every piece of data collected should be structured, labeled, and preserved for subsequent learning. This is R&D religion these days.
But, when they outsource, they nearly always waste the data!
All too often, the data gets buried anonymously in the SharePoint of the requesting engineers, never to be found again. Unstructured and unsearchable data is as-good-as-gone. What sample was that? What DOE? What supplier / raw material? Maybe the engineer remembers. But, if they leave, then the data is effectively lost.
7. Having no corporate strategy
This topic has been covered in more detail in my previous articles in this series. Every engagement with an outsourced service provider is an investment. Not just the money spent, but the time involved in educating that provider on your samples, your challenges, your objectives and goals.
Transactional engagements at the individual engineer level have their purpose and value. I know that certain engineers have strong individual preferences for their favorite analytical scientist at some particular service provider. They have a relationship developed over years. They have history.
But as a company leader, you need to consider the power of an institutional-level relationship that supersedes the engineer-to-engineer accumulated histories. Our customers are trying to solve very hard problems. Advancing the state-of-the-art requires genuine collaboration and partnership that is not achievable with organic, every-engineer-for-themselves strategy.
Essentially, our analytical team of experts, our lab filled with high-end analytical instruments, our network of outside relationships with tool manufacturers and other labs, our knowledge of modeling and analysis, our suite of software tools – all of that should be seamlessly available to you as an “On-Demand” extension of your own team and resources! That scale of benefit is achievable only by taking outsourcing up to the level of corporate strategy.
Several of these mistakes are really “it takes two to tango” sorts of problems. Yes, customers can do better in not making them. But the analytical service provider can help solve the problems, or at least make them less likely, by intentionally developing systems, business processes, service offerings and technology that address the root causes.
How can we ameliorate these mistakes and issues? Well, Covalent’s Digital Platform is intended to reduce customer friction (a leading cause of #1), facilitate both better scoping (#4) and better follow-up (#5), and even enable tracking and management of the resulting data (#6). Our Ionic Membership Program and our Enterprise-level retained engagements are each ways to establish deeper trust and partnership (thereby addressing #3, #4 and #7). We are still relatively early in developing the full potential of both the Platform and the business models, but we are already seeing clear customer benefits and positive feedback.
Great planning, open communication, focused problem solving, and genuine collaboration – these are the best practices to avoid outsourcing mistakes.
As always, I welcome your feedback, additional observations or comments, or alternative opinions. You can always email me at firstname.lastname@example.org.