McKinsey & Company – an operating model perfected through decades of refining

1. Brief description: McKinsey is a leading strategy consulting Firm
2. McKinsey creates sustainable value for clients of which it extracts fixed fee or % of monetary value
3. McKinsey’s continuously evolving Operating Model is designed around its Business Model

1. Brief description: A leading strategy consulting Firm with its people as its largest assets

McKinsey & Company is a global management consulting firm that serves leading businesses, governments, non governmental organizations, and not-for-profits. McKinsey helps clients make sustainable improvements to their performance and realize their most important goals. They’ve been operating for almost a century (89 years to be accurate). (1) Given the industry, McKinsey’s core assets is its people.

2. McKinsey creates sustainable value for clients of which it extracts fixed fee or % of monetary value

McKinsey has a dual mission: “help clients make distinctive, lasting, and substantial improvements in their performance and to build a great firm that attracts, develops, excites, and retains exceptional people.” (2) For the purposes of this discussion we will focus on the first, but it is clear that number 2 is essential given McKinsey’s biggest assets being its employees. So what kind of “improvements” does McKinsey help make? The answer depends on the function (e.g. whether McKinsey is supporting clients do: a) strategy refresh, b) operational improvements, c) organization re-design, d) transformations, etc… However, McKinsey’s customer promise is a positive change that is:

a. Substantial and relevant: The change must be relevant at a company-level, ideally, one of the top priorities of the CEO. McKinsey basically helps takes a CEO approach to Problem Solving and looks at the organization from the point of view of the CEO

b. Measurable: The effects of the change must be measurable, ideally quantitatively. It is critical for the McKinsey team and the client team to align on key metrics that should be measured. This is specifically relevant when the fee is a % of monetary value created

c. Sustainable: The boost to a company’s performance should be sustainable over several years after the McKinsey team leaves.

McKinsey then captures part of this value created through its fixed fees per project or a % share of the monetary value created for the client over a certain period of time (e.g. extra Net profit for then next 3 years) or through fixed fees conditioned on the actual achievement of the performance management. In addition to direct monetary gains, McKinsey sought to build and sustain relationships with its clients.

3. McKinsey’s continuously evolving Operating Model is designed around its Business Model

McKinsey designs and continuously refines its Operating model using 3 pillars of lean, centered around customer-centricity:

a. Technical System

b. Management Infrastructure

c. Mindsets and behaviors

The framework is depicted below. (3) Each pillar is tightly linked to the Core value proposition

a. Technical System

Technical System is in a way the hardware of the company: How are resources allocated to the projects? McKinsey’s solution is simple: a relationship-manager partner and a small dedicated teams supported by a wide expert network and a research arm (with a budget larger than the top 10 B-schools combined – don’t quote me on this). This ensures that the changes are substantial and relevant as follows:

  • The partner, who oversees all projects with the same client, ensures connection with the CEO of the company thereby maintaining a connection between the CEO’s top priorities and the Core On-the-Ground team. This ensures the changes are substantial
  • The Core team is full time on the project, meets with client employees across all levels of the organizations and ensures McKinsey understands the client’s perspective, and that the proposed solutions are relevant and applicable to the company environment. The Core team agrees with the client on ways to “see” the impact of the change, ensuring measurable metrics
  • Research department ensures that the most cutting-edge solutions/ innovations as well as the most traditional “game-books” are surfaced to the team
  • The Experts have seen similar projects across multiple industries and geographies, ensure the recommendation is practical and helps team think through unexpected problems that have emerge on previous projects. The change after-all needs to be sustainable, and this requires the changes to be seamlessly implemented over time, after the McKinsey teams leaves the project.

The processes of the project work are flexible, but the quality systems are rigorous: Developed recommendations should be signed off by each key stakeholder, including the partner, the experts, other partners who have previously worked with the client or have relevant expertise in the area, the client employees across multiple levels of the organization, and ultimately, the highest level main client counter-part (usually company CEO).

In addition, McKinsey teams and partner follow up with the client after completion of the project to ensure the performance metrics are being monitored, and are showing positive impact. The Feedback loop is necessary for the continuous learning of McKinsey (more in section c. below)

b. Management Infrastructure

The Management infrastructure includes the performance management as well as the talent management. This matches McKinsey’s dual mission of client impact and people development.

  • The performance management matches exactly with the value proposition: for example, each consultant’s performance is measured across multiple metrics, including for example the analytical problem solving skills, and whether the consultant is able to step back from a problem to think about it through a CEO’s mindset. This ensures that the solution/ changes proposed are substantial and relevant. Similar metrics exist for the partner, the experts and the researchers.
  • The talent management is also rock solid: starting from the case interview, every interviewee is subjected to a problem faced by a top level executive of a company (usually real life examples) with no guidance on information availability. The candidate should be able to hone in on the problem and ask for the right information to look for. After all, in an actual work environment, getting information is time consuming, and so, if a consultant does not know what information to look for, the project might take a lot of time and end up being irrelevant with non-substantial changes recommended. Similarly, candidates are judged based on their analytical skills, and whether or not they are able to define numerical metrics to track performance for certain changes. Post recruiting, consultants are subjected to on-the-job coaching as well as formal training.

c. Mindsets and behaviors

Mindsets and behaviors are basically the software of the company. The two most relevant topics are:

  • Ownership of the problem: For changes recommended to be practical, and thereby sustainable and substantial, the team should “own” the problem. The core consultant team takes full accountability for the problem. This indirectly leads to performance across all 3 value proposition metrics. For example, the team understands that it can only “manage what it can measure” and would thereby recommend measurable change
  • Continuous Learning: To deliver sustainable changes, McKinsey ensures that its tracks whether its previous recommendations has led to actual sustainable change at the client. This is the feedback loop discussed in section a. above. The feedback loop monitors sub-optimal performance and comes back with actionable steps. An example of continuous improvement is the launch of “McKinsey implementation”. Given the criteria of measured impact at their client (and sometimes McKinsey fees being conditional on the achievement of this impact), McKinsey has realized that there were many instances (especially in emerging markets), were clients faced multiple practical obstacles while implementing the recommendations suggested by the McKinsey team. The issues were driven by either impractical recommendations or limited capabilities at the client (or both). In both scenarios, McKinsey took on the challenge of implementation and launched “McKinsey implementation”, a group of expert implementation consultants who would work hand-in-hand with the clients to implement the recommendations and ensure proper and sustainable knowledge transfer.









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Student comments on McKinsey & Company – an operating model perfected through decades of refining

  1. As many professional service firms claim, McKinsey see its people as the most important asset. However, do you think as the technology advances, Artificial Intelligence will replace humans to do some of the analytical work? As a very capital-lean company, I figure it’s very difficult for McKinsey to invest in the technology in this area. How do you think McKinsey would react to this possible trend?

  2. Really interesting post. As we’ve discussed in a number of our classes this year, work-life balance is top of mind for many millenial high achievers. Consultancies are notorious for the time and stress demands it places on its people, which as you’ve noted, are critical assets. Companies like HourlyNerd have created a business model that is potentially disruptive to McKinsey on two fronts: 1) it offers superior flexibility, autonomy, and work-life balance for consultants and 2) it has the potential to lower a client’s consulting costs significantly. In the face of these disrupters, how, if at all, can / should a larger consultancy like McKinsey adapt to 1) retain its status as an elite and prestigious employer and 2) preserve its high-priced revenue model?

  3. I’m curious to know how McKinsey, its competitors, and strategy consulting in general are thinking of responding to the increasing importance and prevalence of big data in making important business decisions. Historically (at least in the US), the talent pool that contributes to this field has been extremely averse to joining McK-type firms. Do you think McK will respond by adjusting its operating model to incorporate such capabilities, or tweak its business model to not include such analytical offerings?

  4. To be successful in such a global environment consultants have to collect and share knowledge inside the firm. However, collecting and sustaining that knowledge keeping client data confidential doesn’t sound like an easy task and creates more questions than answers:
    How do you keep client data confidential but share the knowledge?
    How do you create a feedback loop between the teams and internal research groups?
    And how do you ensure that the knowledge is not gone from the firm when an expert leaves?

  5. Thanks all for the great comments. Below my thoughts:
    1) Feiran:
    a) AI is still in very early development and still needs to replace many less thought heavy industries before reaching consulting
    b) Within upcoming years, repetitive Analytical work can be replaced with AI. The org structure of McK has such analytical work in a support function (special department). I’m guessing as AI becomes better, this department can be down-sized leaving some people to tailor the technology to the consultant’s needs
    c) McKinsey currently invests in Technology break-throughs through its “Digital Labs”. IMO, this doesn’t enable to develop moonshots, but allows us to leverage moonshots to improve our Operating Model.

    2) BT (Tyler Biddix?): I don’t see HourlyNerd as a competitor. I see them as a competitor to GLG or AlphaSites as they get you in touch with an expert who can provide industry trends, industry breakthroughs and insights into competitive landscape. Couple reasons:
    a) I really doubt a company would be comfortable sharing confidential data with someone on HourlyNerd. McKinsey relationships with client are very very long-term and trust-based
    b) I’ve worked on projects remotely, and, as a consultant, you gain way more insight by being on-the-ground and interacting with client on daily basis

    3) Kchoo: As mentioned to Feiran, McKinsey is using its “McKinsey Digital Labs” to leverage such technology. I fully agree with you that “Big data” will become THE key driver of decision making and McKinsey hasn’t focused as much as needed on that.

    4) Teti: In all cases, I guess our value is HOW we analyze data and strategies, not WHAT data we have
    – We keep client data confidential by masking client names and sharing either average numbers across multiple companies or sharing qualitative info. More and more companies are becoming public and are sharing info, which makes our database less valuable with time
    – Feedback loop is a bit messed up at the moment. While I worked at Booz & Company we did a better job of codifying our knowledge from every project, by creating IP pieces (which is basically a “masked” version of the final presentation, where we hide company name and remove all company specific numbers)
    – You’re always part of the McKinsey family even after you leave 🙂 As long as I have the expert’s name, I’ll reach out to him whether or not he’s left the Firm. On a more serious note, there are multiple people working on same project over time and chances are super high one of them is still at the Firm (from Director to Experts to Research analysts to managers to consultants)

    Thanks again everyone!

  6. Good job Dany. I would be curious to hear your answer on following questions :
    (a) We have seen in the news that McKinsey started its “Implementation Practice”, with a slightly different proposition and operating model than its core strategy arm. How does this new practice work? How does it fit to McKinsey’s strategy? How does new practice tie back to its business model?
    (b) Does McKinsey’s operating model differ from its competitors?
    (c) How does McKinsey incorporate advancing technology into its operating model?
    (d) How does the expert path differs from consultant path? What are the different capabilities and know-how requirements for each?
    Very well done again

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