Identify Goal
Define Problem
Define Problem
Gather Data
Define Causes
Identify Options
Clarify Problem
Generate Ideas
Evaluate Options
Generate Ideas
Choose the Best Solution
Implement Solution
Select Solution
Take Action
MacLeod offers her own problem solving procedure, which echoes the above steps:
“1. Recognize the Problem: State what you see. Sometimes the problem is covert. 2. Identify: Get the facts — What exactly happened? What is the issue? 3. and 4. Explore and Connect: Dig deeper and encourage group members to relate their similar experiences. Now you're getting more into the feelings and background [of the situation], not just the facts. 5. Possible Solutions: Consider and brainstorm ideas for resolution. 6. Implement: Choose a solution and try it out — this could be role play and/or a discussion of how the solution would be put in place. 7. Evaluate: Revisit to see if the solution was successful or not.”
Many of these problem solving techniques can be used in concert with one another, or multiple can be appropriate for any given problem. It’s less about facilitating a perfect CPS session, and more about encouraging team members to continually think outside the box and push beyond personal boundaries that inhibit their innovative thinking. So, try out several methods, find those that resonate best with your team, and continue adopting new techniques and adapting your processes along the way.
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In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.
Podcast transcript
Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.
Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].
Charles and Hugo, welcome to the podcast. Thank you for being here.
Hugo Sarrazin: Our pleasure.
Charles Conn: It’s terrific to be here.
Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?
Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”
You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”
I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.
I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.
Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.
Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.
Simon London: So this is a concise problem statement.
Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.
Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.
How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.
Hugo Sarrazin: Yeah.
Charles Conn: And in the wrong direction.
Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?
Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.
What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.
Simon London: What’s a good example of a logic tree on a sort of ratable problem?
Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.
If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.
When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.
Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.
Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.
People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.
Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?
Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.
Simon London: Not going to have a lot of depth to it.
Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.
Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.
Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.
Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.
Both: Yeah.
Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.
Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.
Simon London: Right. Right.
Hugo Sarrazin: So it’s the same thing in problem solving.
Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.
Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?
Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.
Simon London: Would you agree with that?
Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.
You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.
Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?
Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.
Simon London: Step six. You’ve done your analysis.
Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”
Simon London: But, again, these final steps are about motivating people to action, right?
Charles Conn: Yeah.
Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.
Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.
Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.
Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.
Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?
Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.
You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.
Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.
Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”
Hugo Sarrazin: Every step of the process.
Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?
Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.
Simon London: Problem definition, but out in the world.
Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.
Simon London: So, Charles, are these complements or are these alternatives?
Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.
Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?
Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.
The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.
Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.
Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.
Hugo Sarrazin: Absolutely.
Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.
Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.
Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.
Charles Conn: It was a pleasure to be here, Simon.
Hugo Sarrazin: It was a pleasure. Thank you.
Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.
Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.
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How can I calculate the distance from each student’s home to their assigned school to determine if they’re eligible to ride the school bus? How can I calculate the commute time from each employee’s home to their work site so I can most effectively target ridesharing incentives? How can I calculate the driving time from each patient’s home to the location of their doctor’s appointment to better understand access to health care in my state?
All these questions are examples of a common class of network analysis problem: calculating the travel time, distance, or route path between preassigned pairs of origins and destinations.
In this type of analysis, the goal isn’t to find the closest destination (a closest facility analysis) or to calculate the travel time or distance from all origins to all destinations (an origin destination cost matrix analysis). Nor is the goal to find the optimal way to dispatch a fleet of vehicles for paired pick-ups and drop-offs (a vehicle routing problem analysis). Rather, the goal is to calculate the optimal path from each origin to its assigned destination and get the travel time, distance, or route geometry of that path. This type of problem can be solved using the Network Analyst route solver.
Learn more about the Network Analyst analysis types
This post describes how to set up and solve this type of problem using the route solver. Then, it covers some ready-to-use tools that make solving this type of problem easier.
As long as you set up the problem correctly, the Network Analyst route solver can calculate routes between multiple pairs of locations within one solve operation.
The input to the route solver is a single table of stops. Ordinarily, when you solve the analysis, you get a single route that visits all the stops in the table.
For example, you can set up a Stops table with four stops:
When you solve the route, you get a single route that visits each stop:
However, you can assign stops to different routes by setting a value in the RouteName field. Stops with the same RouteName field value will be assigned to the same route, and the analysis results will include a separate route for each unique RouteName in the table.
Revisit the example with four stops, but this time, use the RouteName field to split the four stops into two routes. Stops A and B both have a RouteName field value of Route 1, and Stops C and D have a RouteName field value of Route 2.
When you solve the analysis, there are two separate routes: one from Stop A to Stop B and another from Stop C to Stop D.
So, if you have preassigned origin-destination pairs, you can load each pair into the Stops table and give them a unique RouteName field value. This way, when you solve the analysis, each pair will get a unique route connecting the origin and destination.
The screenshots above show an analysis in ArcGIS Pro, but this configuration works for all the various APIs across the ArcGIS platform where you can access the Network Analyst route solver.
Learn more about the route solver in ArcGIS Pro , in the ArcPy site package , and the ArcGIS developer APIs .
Unfortunately, real-world data is usually a little more complicated, and it may take some effort to manipulate it into the right format for the Stops table. Next, you’ll look at a more realistic example.
Suppose you have a dataset of commuters with their assigned work sites and another dataset with the locations of the work sites. You want to calculate the route and mileage of each commuter to their work site.
You want to add each commuter and their assigned work site to the Stops input table for the route analysis. Because you want a separate route for each commuter to their work site, you must use a unique RouteName field value for each commuter-work site pair. You’ll learn how to set this up in a moment, but first, the table below shows the output you want to achieve:
The Stops table has eight stops: four for each of the four commuters and four for the work sites each commuter is assigned to. Since two commuters work at the same site (Excellent Elementary), that site is included in the Stops table twice, once for each worker assigned there.
Each commuter-work site pair has a unique RouteName field value to ensure that there’s a separate route connecting each commuter and their work site. Since the commuter names are unique, you can use them for the RouteName values, but you could use anything, as long as each origin and its assigned destination have the same value and that value is unique to the pair.
The map below shows the resulting routes:
To load the Stops table from the original inputs, you have to do a bit of data manipulation.
When loading the commuters, you can use field mapping to transfer the commuter’s name from the original dataset to the Name and RouteName fields in Stops. In ArcGIS Pro, this is accomplished using the Add Locations tool.
Loading the work sites is trickier because you must make a copy of each work site for each commuter who works there. You can do this using a one-to-many join with the Add Join tool.
Joining the Commuters table to WorkSites using a one-to-many join results in a copy of each work site for each commuter assigned there. The fields on the right side of the table come from the joined Commuters table.
Now you can load the joined WorkSites table into the Stops table for the Route analysis. Use field mapping to map the WorkSites table’s Name field (the work site’s name) to the Stops table’s Name field and the joined Commuters table’s Name field (the commuter’s name) to the Stops table’s RouteName field. With the Add Locations tool in ArcGIS Pro, it looks like this:
As shown earlier, the resulting Stops table includes an entry for each commuter and an entry for each commuter’s work site with the RouteName field used to define origin-destination pairs.
Manually configuring your data in the format described above can be challenging. However, ready-to-use tools for solving preassigned origin-destination pair problems are available in the Map Viewer and ArcGIS Pro. These tools use the route solver, but they simplify setting up the inputs.
The Calculate Travel Cost tool in Map Viewer in ArcGIS Online and ArcGIS Enterprise is specifically designed to calculate routes between origins and destinations. You can specify matching ID fields in the input origins and destinations to define which origin is paired with which destination.
If you’re a developer, you can also access this tool with ArcGIS REST API and ArcGIS API for Python .
If you have a large set of paired locations or you’re struggling to set up your data as described above, you can download and use the Solve Large Analysis With Known OD Pairs tool from Esri’s large-network-analysis-tools GitHub repository .
This tool offers two options for setting up pairs of input locations:
The tool improves performance by solving the routes in parallel. It’s usable out of the box in ArcGIS Pro with no coding required, but it’s also open-source Python code you can modify to suit your needs.
You can calculate the travel time, distance, and route paths between paired locations using the ArcGIS Network Analyst route solver. You can configure the analysis manually, or you can use ready-to-use tools in the Map Viewer or ArcGIS Pro. Give it a try!
If you have questions or suggestions about this workflow, reach out on Esri Community . We would love to hear from you!
Melinda Morang is a product engineer on Esri's Network Analyst Team.
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When it comes to PCs, most users expect a reliable and lag-free experience. The boot up process takes only seconds, programs open almost without delay, and videos run smoothly. But every now and then, there’s sand in the gears: the PC crashes without warning or copying files takes minutes.
In most cases, it’s not the software but the hardware that’s responsible for the drop in performance. But where the bottleneck is located cannot be determined right away. Is a memory module defective or is the CPU overheating? Is the hard disk or SSD working at its capacity limit? Or is the application simply slow because it’s pulling data from the internet?
If your PC is slower than usual, you can find the issue by using special tools that display the current performance of the individual components. Read on to learn more.
Further reading: Is your laptop slow? Try these 9 things before you give up
The Windows task manager displays data and statistics on the most important PC components clearly and graphically. In case of problems, this system program is your first port of call.
The Task Manager is a useful tool for performance control. Microsoft has revised and upgraded the program considerably in recent years.
To call up the task manager, press the keys Ctrl>Alt>Del and select Task Manager (you can also alternatively type task into the Windows search window). In the app, click on Settings at the bottom left and activate the option Always on top .
Now switch to the Performance tab and open the CPU, Memory, GPU, Ethernet, and/or WLAN tabs in turn. Keep the tab open and work as usual on the PC and observe the information of the components. If they are permanently above 80 percent, then there’s something wrong here.
The freeware Core Temp shows the current temperatures of the individual CPU cores.
If your computer crashes again and again or runs noticeably slower, the cause is often an overheated CPU. The trigger can be a badly fitted or malfunctioning fan. The processor’s protection circuit reduces the clock frequency as soon as the temperature rises sharply. If this is not enough, the CPU is deactivated to prevent damage and the PC switches off without warning.
So you’re going to want to check the temperature of the processor with the help of Core Temp . The freeware offers a constantly updated database with all older and current CPU models from AMD and Intel and displays their data after starting. It also calls up the individual core temperatures and displays them on the tool interface and at the bottom of the taskbar.
The program window shows the maximum permissible CPU operating temperature at Tj. Max and the current temperatures below it. If these are permanently above 80 degrees, there’s probably a cooling error. Now you’ll want to look inside the computer to see if the fan is spinning and is firmly seated on the processor.
Windows performance monitoring tracks the percentage of processor time required for a thread in the graph (shown below).
If the PC is simply doing its tasks too slowly, this may also be due to the CPU. In this case, observe the utilization curve in the task manager. Another helpful tool is Windows’ performance monitoring, which you can call up by entering perfmon in the search field. After starting it, open Performance > Monitoring tools > Performance monitoring in the left column.
Now a constantly updated diagram appears showing the processor time at the bottom. This value indicates how much time the CPU needs to execute a thread. Thread here means certain program actions, i.e. in word processing for text input, for saving, for printing, and so on. The less processor time the CPU has to spend to execute a thread, the better. If the value in the performance monitoring is permanently above 80 to 90 percent, the CPU is too slow and should be replaced.
Recurring PC crashes can be caused not only by the CPU, but also by the memory components of the main memory. Defects due to production errors or overheating are by no means rare. If individual memory addresses can no longer be addressed, Windows crashes.
With Memtest you can check the built-in RAM for errors. However, the software requires some preliminary considerations. Since Windows does not allow access to memory that is already in use elsewhere, Memtest can only ever check the free RAM. Therefore it’s best to reboot the computer, not call up any other program afterwards, and just start Memtest. Since Windows moves its subsystems dynamically in the memory, the tool will sooner or later examine the entire RAM.
Memtest can examine a maximum of between two and 3.5 GBytes of contiguous memory. Therefore, look in the Task Manager under Performance>Memory to see how much memory is currently available and divide the Mbyte value by 2,000. The result is the number of Memtest instances you should open in parallel. In each of them, enter 2,000 megabytes as the memory volume. Let each instance run until a coverage of 100 percent is reached. According to the manufacturer, this will detect 95 percent of all memory errors. If Memtest encounters an error, the program stops and issues a message. In this case, it’s best to replace the memory latch.
The graphics card can also cause crashes, especially if it is being used to capacity by a PC game. In this case, the GPU can overheat and memory errors can occur.
You can read the temperature in the task manager under Performance>GPU . Better suited than the on-board tool, however, is GPU-Z. This tool collects and displays additional information about the hardware. As a rule of thumb, the GPU should not get hotter than about 80 degrees. Otherwise, there’s a risk of hardware defects. If GPU-Z detects higher values, check whether the card fan is running and firmly seated. Whether it can be replaced depends on the graphics card itself.
OCCT can be used for a memory test of the graphics card. On the user interface, click on Test>VRAM on the left. Select the correct graphics card and the test duration. The default setting is 30 minutes, which is sufficient even for generously equipped cards. Now click on the start symbol at the bottom. If the program diagnoses a memory error, you have no choice but to install a new graphics card.
If a game only runs slowly and jerky with a low frame rate, the GPU might simply be overtaxed. This can be tested with the 3DMark benchmark, but the free demo version is very limited.
The tool SSD-Z reads the S.M.A.R.T. data of your SSD or hard disk and points out errors when reading or writing.
If booting Windows suddenly takes a disproportionately long time despite the SSD, you should check the data medium. Crystaldiskmark has proven itself as a speed test. The tool performs several test runs and measures the time for writing and reading data. If the values are conspicuously low, we recommend taking a closer look. Crystaldiskmark not only detects hard disks and SSDs, but can also measure USB drives and sticks.
SSD-Z is suitable for more in-depth analyses. This tool displays the technical data and the current temperature of the storage medium and also lists the S.M.A.R.T. values of the drive. SSD-Z also comes with its own benchmark, but it is less precise than that of Crystaldiskmark.
This article originally appeared on our sister publication PC-WELT and was translated and localized from German.
Roland Freist bearbeitet als freier IT-Fachjournalist Themen rund um Windows, Anwendungen, Netzwerke, Security und Internet.
Griswold is a home-care company that assigns caregivers to older people to help with daily living in their homes.
Founded in 1982 and headquartered outside Philadelphia, Griswold operates eight locations and has about 150 franchises in more than 30 states.
Michael Slupecki, the CEO of Griswold, said turnover rates in the home-care industry have been increasing nationwide, especially in the wake of COVID-19.
Slupecki said that in the past few years, Griswold's turnover rate had reached about 80% annually in the locations it operates in Pennsylvania. Finding and replacing caregivers is costly for the company and affects the care clients receive.
"There's a high correlation between consistency in the caregiver workforce and client satisfaction, which goes to your reputation, which goes to the strength of your business in attracting more clients and more caregivers," Slupecki said.
Griswold wanted to improve caregiver experience , reduce turnover, and predict which helpers were most likely to quit, he said.
Last year, the organization started using TeamEngage, a system created by WellSky, a health and community-care tech company, and powered by Ava, an artificial-intelligence platform.
The tool incentivizes and rewards caregivers for completing activities and reaching goals.
Katherine Schiavino, the chief financial officer at Griswold, and the company's human-resources team determined which metrics would be used to recognize caregivers, such as being on time for work, taking extra shifts, and referring new caregivers. They also decided on a point system for rewards, she said.
The AI tool automatically assigns points to caregivers when they complete activities or achieve goals, Schiavino said. For example, if they clock in on time or accept a shift to fill in for someone, the system gives them points. They also receive points for their birthdays and work anniversaries.
The points can be redeemed for gift cards, such as for Starbucks, or a preloaded Mastercard, she said. When caregivers receive a reward, it's announced on a community platform in the system, which others can see. And their colleagues can congratulate them there.
Employee recognition used to be a manual process, but office staff sometimes lacked the capacity to consistently celebrate caregivers, Slupecki said. The AI tool automates honoring these milestones and minimizes the administrative workload, ensuring that caregivers' work isn't overlooked.
"It helps reach out to all of our caregivers and recognize them in a way that they should be recognized," he said.
The AI platform also tracks and analyzes factors that might contribute to caregivers quitting so the company can find solutions to retain them, Slupecki said. For instance, it identifies those who haven't had a rate increase recently or who've been assigned a client who lives a long driving distance away.
Griswold has reduced turnover by about 20% in the locations it owns since it started using TeamEngage in October, Slupecki said.
Schiavino attributed that to the reward system and to how the tool helps identify caregivers who need extra encouragement. For example, someone might be arriving at a client's home on time but not clocking in right away — so the system notifies the company and caregiver that they're late. The system will then automatically incentivize the caregiver to change their behavior.
Receiving recognition on the community platform has improved morale and fostered a sense of community , which is important since caregivers work in client homes and don't typically gather together, Schiavino said.
The AI tool has also helped Griswold increase employee referrals since caregivers earn points when they recommend others for the job.
Griswold is using TeamEngage across the locations it runs in Pennsylvania — which employ about 400 caregivers — and in a few franchises, Slupecki said.
The company said it planned to discuss the tool's benefits at its franchise conference in August to encourage more franchises to improve their turnover, he said.
"It's worked really well for our caregivers," Schiavino said. "They've really connected with this."
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Linda Yaccarino, the C.E.O. of X, has worked hard to bring back advertisers and fix the platform’s business. But its owner, Elon Musk, is always one whim away from undoing her work.
By Kate Conger
Kate Conger has been covering X ever since she joined The New York Times in 2018, back when it was still called Twitter.
Late last year, Linda Yaccarino reached out to Don Lemon’s agent with an offer.
Ms. Yaccarino, the chief executive of X, a powerhouse advertising executive who had been hired away from NBC about seven months earlier, pitched the agent on bringing the former CNN anchor’s new web-based show to the social media platform, citing its massive reach, political influence and connections with advertisers. Soon after, Mr. Lemon became one of the first high-profile names to sign onto Ms. Yaccarino’s plan to help save the company’s sagging advertising business with video and TV-like programming.
Elon Musk, who owns X, agreed to be Mr. Lemon’s first guest.
The interview was held at the Tesla headquarters in Austin, Texas, which, Mr. Musk quickly pointed out to Mr. Lemon, was “about three times larger than the Pentagon.” The two men sat on white Eames-like swivel chairs, a small white table between them. Mr. Musk was in a black T-shirt, Mr. Lemon in a white spread-collar shirt and a dark blue sweater.
The interview started out awkwardly, with Mr. Musk acknowledging that he hadn’t really watched Mr. Lemon when he anchored a 9 p.m. show on CNN. (“I’ve seen a few segments.”) It grew increasingly contentious over the next hour, and Mr. Musk became visibly frustrated with questions about his politics and drug use. “It’s pretty private,” Mr. Musk said when Mr. Lemon asked him about his prescription for ketamine, which Mr. Musk had posted about on X in 2023.
Mr. Lemon brought up complaints of sexual harassment at Tesla and SpaceX, both run by Mr. Musk, then asked if he had advantages in society as a white man. Mr. Musk raised an eyebrow. “You keep putting words in my mouth,” he objected. And when Mr. Lemon asked about the advertiser exodus from X, Mr. Musk shook his head: “Don, I have to say, choose your questions carefully. There’s five minutes left.” As the interview ended, Mr. Musk shot up from his chair, offering an abrupt handshake to the anchor.
The next day, he texted Mr. Lemon’s agent: “Contract canceled.”
A day after Mr. Musk called off the deal, Ms. Yaccarino called Mr. Lemon to find out what went wrong. She seemed confident she could patch things up between him and her boss. But Mr. Musk remained firm; Mr. Lemon had to be dismissed. The deal died — and with it, yet another attempt by Ms. Yaccarino to chart a profitable course for the troubled site.
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Why do so many family offices use excel.
Microsoft Excel is a surprising backbone in the private wealth industry - a consequence of cost, ... [+] opacity, and willingness to embrace new tools.
Microsoft Excel is a household name - and has been so well adopted that it’s become the generic name for “spreadsheets” (alongside other contenders such as Google Sheets or Apple Numbers).
However, an era dominated by sophisticated financial software and complex data analytics that Excel helped foster, it might seem surprising many family offices - with traditionally abundant access to capital - still rely on the software for many core functions and tasks.
A recent LinkedIn post poked fun at this irony, given that family offices can be slow to adopt new technologies that support more sophisticated data aggregation and investment management tools that their core operations typically demand.
Diving into the comments on the post, there’s a lot to unpack.
In short - Excel can be used as a Swiss Army Knife in almost every organization, and the same holds true for family offices.
Data teams use Excel to organise data, sales teams use it for organising leads and sales funnels, marketing teams for customer data, HR teams for employee info, C-level roles for reporting across key metrics and then, of course, finance teams use Excel for everything financial from budgets, to cash flow, projections, P&Ls - you name it.
In a family office environment, investment teams also use Excel for investment tracking and, alongside with finance teams, build reporting.
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There are several reasons why Excel has solidified its position as an indispensable tool in managing the complexities of family wealth - among them, and perhaps most naturally, excel is incredibly versatile, widely used, and understood - making it accessible to a broad range of users within the family office.
Excel was first released in 1987 - making the program itself 37 years old today. For many older familiar generations, Excel has remained a mainstay throughout a significant portion of their lives.
As Microsoft has continually enhanced Excel, family offices have similarly tailored spreadsheets to suit their specific needs across an array of use cases - from tracking investments to managing household expenses. While also having a powerful array of features, Excel also has a relatively straightforward learning curve - enabling new users to onboard quickly.
Marc-Phillipe Davies, co-founder of Deallocker, said it best - quipping that "Excel is not unsophisticated just because everyone has it and costs next to nothing. It's proof how incredible a tool it is! Shallow learning curve, cheap, universal, reliable, trustworthy”.
For that reason, many family offices continue to hold on to Excel as a trusted source of truth. Which leads to…
The saying, ‘if it’s not broken, don’t fix it’ easily applies to many family offices that continue to use Excel. Older familial generations can feel most at home with the software.
It’s also worthwhile to mention that this resistance is also not usually the fault of the family office itself. In an industry that’s renowned for its opacity, there are few benchmarks that would compel family offices to significantly enhance their operations by updating their technology stack; contributing to a resistance to, and fear of change that is pervasive - even when new options and features can significantly enhance portfolio reporting capabilities across complex financial instruments at a minimum.
As Michael Casciano from EVO Wealth Tech playfully added, "Inertia is the second most powerful force in wealth management after compounding interest!"
There’s also the fact that compared to specialised financial software, Excel is relatively inexpensive, making it an attractive option for many family offices, especially those in the early stages of their establishment or those that have existed for many decades.
"I also see a heavy reliance on spreadsheets, and reluctance to change is high for the reasons mentioned, especially ‘cost’," outlined Ian Keates, Chief Executive Officer at Altoo AG.
Excel can additionally integrate with many other software applications (such as accounting systems and CRM platforms) enhancing its utility in the overall family office ecosystem.
While there are many platforms that can expand on Excel’s feature set and further integrate with more complex alternative investments or assist with ESG or Impact reporting, these toolsets can come with their own price tag - meaning that many family offices may simply choose to ‘go it alone’.
Finding the right technology foundation, or replacement for Excel, is not an easy task. Many vendors who are the first to point out that family offices shouldn’t be using Excel do not simplify their marketing, messaging, and introduction to illustrate how family offices can understand, compare, buy and implement their solutions.
While there are many interesting and unique selling points among family office software providers, few invest the time and effort to transparently highlight their value propositions and unique features that could otherwise make a move from Excel.
While Excel is a powerful tool, it's essential to recognize its limitations. For large and complex family offices, it might not be sufficient for managing all financial data and operations - additionally, manual data entry and calculations can be time-consuming and prone to errors. The key-person risk involved with only one person knowing how the sheets work they they built is also a risk that is often entirely overlooked.
For family offices seeking a solution in the middle, many are adopting hybrid approaches, combining Excel with specialised financial software for specific tasks. This allows them to leverage the strengths of both tools and optimise their operations.
The essential takeaway is that while Excel can suffice, family offices seeking to digitise - and who are ready to expand their abilities in-line with new tools as an investment - can access a wide array of new technologies that are reshaping the market.
As Oliver Topham, Business Development Manager at Flanks, concluded "…The mindset of 'we’ve done it this way for years and it works' will only hold the wealth management industry back and they need to consider how much time and money could be saved by looking into the many tech solutions out there today.”
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Extensive problem solving is the purchase decision marking in a situation in which the buyer has no information, experience about the products, services and suppliers. In extensive problem solving, lack of information also spreads to the brands for the product and also the criterion that they set for segregating the brands to be small or manageable subsets that help in the purchasing decision ...
2. Break the problem down. Identifying the problem allows you to see which steps need to be taken to solve it. First, break the problem down into achievable blocks. Then, use strategic planning to set a time frame in which to solve the problem and establish a timeline for the completion of each stage. 3.
Extensive problem solving is a systematic approach to breaking down complex problems into manageable pieces. It involves analyzing the problem from multiple angles, identifying all the factors that are contributing to the problem, and then coming up with a comprehensive solution. This process can be used to tackle any problem, from personal and ...
Problem-solving strategies can be enhanced with the application of creative techniques. You can use creativity to: Approach problems from different angles. Improve your problem-solving process. Spark creativity in your employees and peers. 6. Adaptability. Adaptability is the capacity to adjust to change. When a particular solution to an issue ...
Step 1: Identify the Problem. The problem-solving process starts with identifying the problem. This step involves understanding the issue's nature, its scope, and its impact. Once the problem is clearly defined, it sets the foundation for finding effective solutions.
Collaborate with Others. Collaborating with others can help you develop your complex problem-solving skills. Working in a team environment can expose you to new ideas and approaches, help you identify blind spots, and provide opportunities for feedback and support. 5. Seek Out Challenging Problems.
14 types of problem-solving strategies. Here are some examples of problem-solving strategies you can practice using to see which works best for you in different situations: 1. Define the problem. Taking the time to define a potential challenge can help you identify certain elements to create a plan to resolve them.
Learning the soft skills and critical thinking techniques that good problem solvers use can help anyone overcome complex problems. Learning problem-solving techniques is a must for working professionals in any field. No matter your title or job description, the ability to find the root cause of a difficult problem and formulate viable solutions ...
Step 4 - Implement and Monitor the Solution. When you've decided on the best solution, it's time to put it into action. The fourth and final step in effective problem solving is to put the solution into action, monitor its progress, and make any necessary adjustments. To begin, implement the solution.
7. Solution evaluation. 1. Problem identification. The first stage of any problem solving process is to identify the problem (s) you need to solve. This often looks like using group discussions and activities to help a group surface and effectively articulate the challenges they're facing and wish to resolve.
Learning Objectives. Describe how a retailer can increase sales from customers engaged in extended problem solving. Consumers with an extended problem solving mindset put a great deal of effort into their purchase decision, gathering information through research and taking care to evaluate all options, before arriving at a decision. Because of ...
The problem-solving process typically includes the following steps: Identify the issue: Recognize the problem that needs to be solved. Analyze the situation: Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present. Generate potential solutions: Brainstorm a list of possible ...
When you roll out the solution, request feedback on the success of the change made. 5. Review, Iterate, and Improve. Making a change shouldn't be a one time action. Spend time reviewing the results of the change to make sure it's made the required impact and met the desired outcomes.
How to Solve Problems. To bring the best ideas forward, teams must build psychological safety. by. Laura Amico. October 29, 2021. HBR Staff/EschCollection/Getty Images. Teams today aren't just ...
Here are the basic steps involved in problem-solving: 1. Define the problem. The first step is to analyze the situation carefully to learn more about the problem. A single situation may solve multiple problems. Identify each problem and determine its cause. Try to anticipate the behavior and response of those affected by the problem.
Steps for complex problem-solving. Below is a list of commonly used steps to successfully complete complex problem-solving: 1. Identify the problem and its cause. In order to solve a complex problem, it's often helpful to clearly identify the problem and determine its cause.
Definition. In the choice process, extensive problem solving includes those consumer decisions requiring considerable cognitive activity, thought, and behavioral effort as compared to routinized choice behavior and habitual decision making. [1] This type of decision making is usually associated with high-involvement purchases and when the ...
Problem-solving tools support your meeting with easy-to-use graphs, visualisations and techniques. By implementing a problem-solving tool, you break the cycle of mundane verbal discussion, enabling you to maintain engagement throughout the session. 28. Fishbone Diagram.
Balance divergent and convergent thinking. Ask problems as questions. Defer or suspend judgement. Focus on "Yes, and…" rather than "No, but…". According to Carella, "Creative problem solving is the mental process used for generating innovative and imaginative ideas as a solution to a problem or a challenge.
When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that's very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use ...
Extensive Problem Solving. buying situations which require considerable effort because the buyer has had no previous experience with the product or suppliers; also called Extensive Decision Making. See: Limited Problem Solving. Rate this term.
Effective problem-solving requires a thorough understanding of the situation. Make it a habit to gather comprehensive information before attempting to solve a problem. This means looking beyond ...
EXTENSIVE PROBLEM SOLVING meaning: the process of a customer trying to get all the information they need in order to be able to make a…. Learn more.
This type of problem can be solved using the Network Analyst route solver. Learn more about the Network Analyst analysis types. This post describes how to set up and solve this type of problem using the route solver. Then, it covers some ready-to-use tools that make solving this type of problem easier.
To call up the task manager, press the keys Ctrl>Alt>Del and select Task Manager (you can also alternatively type task into the Windows search window). In the app, click on Settings at the bottom ...
Here are a few examples of skills you may use when solving a problem: Research Researching is an essential skill related to problem-solving. As a problem solver, you need to be able to identify the cause of the issue and understand it fully. You can begin to gather more information about a problem by brainstorming with other team members ...
Situation analysis: What problem was the company trying to solve? Michael Slupecki, the CEO of Griswold, said turnover rates in the home-care industry have been increasing nationwide, especially ...
Linda Yaccarino, the C.E.O. of X, has worked hard to bring back advertisers and fix the platform's business. But its owner, Elon Musk, is always one whim away from undoing her work.
This is due to two major problems: inaccessible data and the time it takes to chart accurately. EMRs have a problem with inaccessible data due to the nature of data modeling and user interface design.
In short - Excel can be used as a Swiss Army Knife in almost every organization, and the same holds true for family offices. Data teams use Excel to organise data, sales teams use it for ...