VUCA Overall Notes - Summary Managing in a Volatile, Uncertain, Complex and Ambiguous Context PDF

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VUCA Overall Notes...

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*Focus on FUTURE!!! VUCA Week 1: Megatrends A large transformative global force that impacts everyone on the planet with no one singular underlying cause (usually a confluence of multiple causes and events). ▪ Slow, steady and powerful for decades ▪ Forces that define our present and future worlds, and the interaction between the megatrends is as (if not more) important as each individual megatrend. 6 EXAMPLES OF MEGATRENDS 1) Global Market Place ➢ Gulf (huge diff) between ‘mature’ and ‘rapid-growth’ countries continue to shrink  globalisation ➢ New tier of nations driven by their own (rising) middle class has come to the front  E.g. Vietnam & Indonesia: cheap labour & materials  domestic mkts now have their own opportunities too Opportunities (+) ➢ ↑ interdep btwn nations (globalisation through foreign exchange, trade investments, ss chain networks, waterSG, financial system linkages etc.)

Challenges (-) ➢ War for talent/jobs ➢ Potential conflict of interests  Must balance tension btwn local & global priorities

➢ Hotspots for innovation 2) Changing Demographics ➢ Global pop size ↑ rapidly ➢ YOUNGER workforce in some parts of world VS OLDER workforce in others  Silver Tsunamis, Sandwich Gen (30s-40s take care of parents & kids) Opportunities (+) ➢ ↑ gender equality: rising # of women in workforce. Women make up over half of the workforce (e.g see women in male-dominated industries like engineering and IT)

Challenges (-) ➢ Job dd growing > ss, unable to meet dd? ➢ Competitive environment, ↓ job retention rate, ↓ labour price ➢ ↑ govt expenditure on elder care (healthcare service, medical equip) in DCs ➢ Silver gen

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3) Rise of the ↑ Middle Class: Urbanisation ➢ More people living & moving to cities than ever before – jobs, settle down (China, India) Opportunities (+) ➢ ↑ dd for goods & services/consumption good for biz BUT ➢ ↑ dual income families = ↑ PP

Challenges (-) ➢ puts pressure on nat resources: we’re consuming a lot and not replenishing it. It doesn’t last forever. (E.g. pool balls made from ivory)

4) Enabling Technology *very powerful megatrend – way you do biz, interactions (B2B, B2C) ➢ Diaspora Effect – money earned overseas is returned home thru internet banking ➢ Brain Circulation – leave the country but come back and set up institutions ➢ Data Analytics & Big Data – growing trend (e.g. analytics 3.0) Opportunities (+) Challenges (-) ➢ ↑ tech diffusion rate & efficiency – way faster o Drives a wedge btwn lower (insuff $ to afford high tech equip/learn skills) & higher income families  may ↑ for others to start using a new tech invention income inequality  E.g. Telephone took 35 years VS Internet only 7 years o Public and personal lives being blurred  Org can spread their influence much  E.g. online data & privacy, security issues and easier & across more platforms personal rights (e.g. relatable ads) o Mobile Penetration (1st interaction with interacting with tech): China, India, (e.g. Africa Phenomenon Mobile payment- salary credited into phones  pay child’s school fees) o Key enabler of change – Enables smart nation initiatives  ↑ efficient o Neglected parts can provide more jobs in branches (e.g. cross-selling packages)

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Tech take over jobs, generate new pdt mkt makes others obsolete  E.g. typist & bank tellers (ATM) & half of jobs in US at risk of computerisation over next 2 decades

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Man + machine work tgt  job descriptions change? Ppl need to constantly upgrade skills

Opportunities (+)

 E.g. Cleaner operating road sweeping vehicles Challenges (-)

o Information age – impact of internet on our ➢ Changes in habits  children more reliant on tech daily lives  get info easily & faster ➢ Possible social problems:  internet not as regulated, easier to commit crimes  X critical thinking 2

5) Rise of the ↑ Individual Opportunities (+)

Challenges (-)

➢ Empowerment through education  ↑ incomes

 ↑ expectations

➢ Inter-connected pop (social media)

➢ Blurs public VS private boundaries. What to share & not share? We think it’s ok to share but when it comes back and hits us, we don’t expect such things.  E.g. upload photos on holiday but robbers will know no one at home  Celebrities - Kim Kardashian posts Snapchats of her night out before she was robbed at gunpoint in a Paris hotel room. ➢ Seek validation from likes & ↑ followings but that doesn’t necessarily mean we know them well. Solid connections? Substance? ➢ Indiv can express distaste and frustration with biz/pdts online  affect rep as info quickly disseminated  network effect when netizens see post (use more/boycott?)  ↑/↓ sales  E.g. Arab Spring, Umbrella Revolution (HK ) – social media aided individuals in organising protests

6) Economic Power Shift Opportunities (+) ➢ Shift in competitive adv, econ power West  East (China, India): Rise of Asia  Africa the next Asia, Middle East’s Potential (mobile tech) ➢ BRICS (Brazil, Russia, India, China, South Africa) developing countries ↑ global middle class + MNCs wanna tap on their mkts

Challenges (-) ➢ Skilled workers from LDCs will move to DCs  ↑ comp for jobs

 East usually wanna go to west to do biz, but now is opp!

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7) Black Swan Events An event that comes as a surprise, has a major effect, and is often inappropriately rationalised after the fact with the benefit of hindsight. o Rare, Hard to predict o High consequence o After occurrence, rationalised by hindsight o E.g. Commodity price fluctuations, nat disasters, terrorist attacks, volatile events like ISIS, Brexit, Oil, Sept 2001 attacks, Arab Spring etc o Solution: evaluate and analyse *Rare events can be more significant than average ones & may occur more often than we think!

8) Others: Climate Change and Resource Depletion o o o o ▪

Global warming ↑ urbanisation  ↑ energy consumption ↑ 50% food production to feed ↑ population 40% gap between water ss & dd

Solution to Megatrends / Challenges Faced in a VUCA World: o Innovation

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VUCA Week 2: Understanding VUCA (an event is a result of ALL 4 elements) (A) VOLATILITY ▪ ▪

Unstable or unexpected changes caused by shocks and triggers Lasting an unknown duration with no clearly defined end ➢ Nature, speed, vol, duration, rate of change, conseq and magnitude of change is in an unpredictable pattern  ppl may be afraid  act a certain way ➢ Good/bad depends on ppl and situation  E.g. Stock Market, Commodity Price Fluctuations, Property Prices (ppl who want to sell want prices to drop so they can buy more)

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Volatility is Turbulence ➢ Phenomenon that is v. uncertain & occurring more freq, must be ready for this ➢ Financial turbulence ↑ in intensity and persisted longer than in the past  Half of the most turbulent financial quarters during the past 30 years have occurred in 2002.

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Drivers of Turbulence ➢ Digitalization, connectivity (higher level of interconnectedness), global competition, business model innovation, climate change (natural disaster e.g. Fukoshima Nuclear Plant, Taiwan earthquake)

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Dealing with Volatility (i) Build in slack in the system (buffer to allow for flex so firm can adapt to change) ➢ SS chain plan just-in-time inventories (depends on industry and goods!) ➢ In general: ↓ excess inventories, ↑ productivity, very efficient ➢ Good – Fresh foods: deliver to retailers next day  keeps food fresh, ↑ qlty ➢ Bad – automobile: need time to manu, cannot deliver pdts in time  E.g. Toyota – just-in-time strat was a headache ➢ Decoupling (outsource?) & Redundancy (remove duplicate jobs) (ii) Devote resources to prepare ➢ Stock Pile Inventory – but may cause more $$ to store goods  cost must match risk  cost-benefit analysis ➢ Create bench strength (surplus in labour) – ppl on reserve to take up position  E.g. IT companies – during pdt launch, anticipate bugs & ↑ user enquires  bandwidth to tackle these problems (iii) Build in agility, learn to work with changes adapt, flexi, improvise, think on feet ▪

Conclusion ➢ Dealing with volatility is often exp – requires more resources than expected  investment must match magnitude of risk ➢ Not always bad – provides opportunities for risk taking; can take adv of unknowns 5

(B) UNCERTAINTY ▪ ▪ ▪

Lack of predictability and good knowledge about future Cause & effect are poorly understood  not knowing how to plan the best response X use past events as predictors of future outcomes  Makes forecasting and decision making very challenging 

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Causes of Uncertainty: ➢ Lack of info and understanding towards a situation ➢ A volatile situation can lead to uncertainty o E.g. Retail – changing purch behv, cust going in the way they want it to, v dd  wants online shopping but but no patience to wait, wants to collect in-store, but some stores don’t allow cos diff inventory systems  wants cheaper price online VS in-store pricing

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4 Levels of Uncertainty: (i) L1: Clear Enough Future – A single forecast that is precise enough for det strategy ➢ E.g. low cost VS full service airline (ii) L2: Alternate Futures – A few discrete (separate) scenarios that define the future ➢ actions dependent on competitor’s actions (e.g. petrol chemical prices) (iii) L3: Range of Futures – A range of possible outcomes but no natural scenarios ➢ X discrete (separate) – X 10%, 30%, only 10% to 30% ➢ E.g. MNC wants to enter new market, but cannot predict EXACT outcome (iv) L4: True Ambiguity (Unknown-unknowns) – X basis to forecast future ➢ X idea of how future will shape up ➢ E.g. signed a contract, BUT what if person dies next day/contract is fake?

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Managing Uncertainty: ➢ Understand trends that affect demand and know the “knowables” ➢ No fixed way/mtd  adapt to changing landscape ➢ Invest in info: o Collect, interpret & share – Unsure about tech  reach out to others for help, work with partners o ***Gather info from NEW (not existing) sources, create new info networks – speak to customers e.g. IBM talked to mainframe computer customers (no mkt for personal computers), BUT issue was that they needed to talk to NEW ppl (non-users, potential customers) as diversity brings you new and interesting perspectives! Not existing users! o Look at new perspectives – have an open mind, critical thinker ➢ Risk-based analysis ➢ Conduct simulations in experiments

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Strategic Decisions:

POSTURES (SHOW STRATEGIC INTENT) 1. Shape the Future

MOVES (PORTFOLIO OF ACTIONS) 1. Make Big Bets

➢ Play a leadership role in establishing how industry ops  proactive ➢ Battle to set stds, create dd (e.g. OS Andriod vs iOS, blue-ray  pdts may change but open VS close concept doesn’t change!)

➢ Focused strategies with +ve payoffs in ≥ 1 scenarios BUT -ve effect in others ➢ Make commitments but X big bets  Small bets but may not be directly involved e.g. invest – can make big and lose big

2. Adapt to Future ➢ Win through speed, agility and flexibility ➢ Recognise and capture opportunities in existing markets so company can have upper hand

3. Reserve the Right to Play ➢ Invest sufficiently to stay in game but avoid premature commitments (bets) ➢ E.g. know you’re gonna be impacted  buy over compt (tech sector) ➢ Ready to write-off cost of innovating if biz plan doesn’t work out?

2. Make Options ➢ Scalable investments that can be ramped up or scaled back. ➢ Decisions that yield sig +ve payoffs in some & small -ve payoffs in other scenarios  if +ve then big gain, but if not then ok with loosing what you invested

3. Make No-Regret Moves ➢ Strategic decisions that have (v. confident of having) +ve outcomes/payoffs in any scenario

(C) COMPLEXITY ▪

Many interconnected & interdependent parts/variables forming an elaborate network of info/procedures with other non-linear interactions  1 will impact the other

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Diversity – X homogeneous (diff in nature) √ COMPLEX ≠ Complicated ➢ Many interactions that are constantly changing ➢ Many parts BUT act in ➢ Unpredictable non-linear interactions ➢ Predictable fashion  hard to pinpoint causation e.g. aircraft e.g. air traffic control situation

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Drivers of Complexity ➢ Digitalisation, connectivity, trade liberalisation, global competition and institutional change ➢ E.g. electric self-charging cars – who’s stds to adopt? Company would have adv

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2 Levels of Complexity (i) *Organisational / Institutional  # countries – econ situation of countries, govt rules & regulations  # brands & ppl to manage  # partners – diversification, joint-venture partners 7

(ii) Individual (employees) ➢ Poor int processes – ↑ bureaucratic/red tape (consults, approvals  automated)  ↑ time spent managing int processes > understanding cust’s needs ➢ Confusing role definitions – duplicated roles ➢ Unclear accountabilities – staff serving both HQ & regional office & both diff dd *Blind spot many executives have: focus on org complexity at expense of indiv  wasted effort, org damage ▪

*Types of Complexity (institutional related) (i) Imposed Complexity (X fixed) ➢ Laws, industry regulations and interventions by non-governmental organizations  Manage everyone’s needs and perspective (ii) Inherent Complexity (√ fixed with some difficulties) ➢ Intrinsic to the b iz and can only be resolved by exiting a portion of the biz (if cannot manage the complexity/it requires too much resources) (iii) Designed Complexity (√ fixed with some difficulties) ➢ Results from choices about the business’ operations (where, what, to whom, how) ➢ Companies can remove, alter some of these but this impacts the control systems and business models o E.g. ↑ complex by ↑ barriers to entry  ↑ diff to enter mkt, prevent competition (iv) Unnecessary Complexity (√ easily fixed) o Arises from growing mismatch btwn needs of org and supporting processes o Once identified, it is easily managed (removed/altered/improved)

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Dealing with Complexity *Within org: ↓ institutional complex, ↑ org effectivity, ↓ unnecessary costs, new sources of profit, company able to adapt quickly  competitive adv ➢ Reduce – Remove complexities that don’t add value ➢ Outsourcing help from agencies (E.g. companies engage Recruit Express leaving HR more time for more pdtive matters) ➢ Restructure/Redirect – Channel beneficial complexities to employees who can handle it effectively, or be trained to cope with it ➢ Manage – pinpoint where complexity hinders pdtivity & why ➢ E.g. survey the scene & take stock of situation (quanti & quali focus grp) In the env: ➢ Adapt & align to new environmental complexity ➢ Flexible

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(D) AMBIGUITY ▪

A situation where there is a doubt about nature of cause & effect r/s

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Lack of knowledge about the basic rules of the game  E.g. Collapse of the Soviet Union – Under which laws to sign contract? SU or Russian law? Do ppl understand the free mkt econ/terms in contract?

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X precedent for making decisions on what to expect  Unknown-unknowns (Uncertainty) ➢ X know r/s between them, X know measures to take ➢ E.g. Launching new product in new unexplored mkt (unknown geog, limited industry & foreign market knowledge) VS familiar pdt cat  know what to do

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Dealing with Ambiguity ➢ Experimentation (new pdt in new mkt) ➢ Intelligent experimentation helps to determine what strategies can be beneficial or not in situations where formal rules of business don’t apply ➢ Nothing lasts forever. Will have more info later

CONCLUSION ➢ The world is turbulent and unpredictable ➢ It’s not just complicated, it’s complex (change is the only constant) ➢ Often >1 interpretation ➢ Simple actions may produce unintended consequences ➢ Be flexible, adaptive and agile ➢ Look from multiple perspectives ➢ Manage tradeoffs ➢ Everyone reacts differently when faced with VUCA, some can tolerate/less troubled  senior leaders must recog how employees react to it  cater solution  ↑ effectiveness

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VUCA Week 3: Understanding Speed (Adaptability & Time Pacing) ADAPTABILITY: THE NEW COMPETITIVE ADVANTAGE ▪

Past: most strategies were to build an enduring competitive advantage by establishing clever market positioning, known as first-order capabilities (be really good at doing what you do).

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**Present: In an uncertain & unstable environment, companies must nurture secondorder capabilities (learning to be good at doing new things) to foster rapid adaptation.

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4 Organisational Capabilities / Skills Required for Adaptive Advantage: 1. Read & Act on Signals of Change From External Environment ➢ Using Data Analytics to uncover hidden patterns, unknown correlations and other useful information to make better decisions and to figure out what is important.  E.g. Tesco’s detailed analyses of cust’s purch patterns  customise pdt offerings for each cust seg & provide early warning of shifts in buying behv  E.g. Google uses algorithms to update position of ads based on its relevance to the search – ↑ relevant, ↑ CTR, advertisers pay by CTR  ↑ revenue  E.g. F1 cars have 100s of in-built sensors to continuously collect and process data (weather & road conditions, engine rpm and angles of curves)  guide drivers’ split-second decisions  E.g. Doctors: predict health issues before it can happen  E.g. Police: identify crime patterns and prevent them 2. Experiment Rapidly, Frequently & Economically ➢ Situations that cannot be deduced/forecasted can be done so through experimentation, especially using new approaches and technologies since traditional ways can be costly, inaccurate and risk jeopardise brand’s rep ➢ Experimenting with tech more freq in uncertain envt to test innovative ideas and det what end-users prefer while developing the pdts  E.g. 2008, Proctor & Gamble’s Connect and Develop model uses a walk-in 3D virtual store to run experiments (quicker and cheaper) VS 2 weeks to build the stores  ↑ efficient, ↓ cost, ↓ risk than rivals BUT VR cannot solve all probs cos might even get it wrong in real life!  E.g. P&G wanted to penetrate low & middle mkts  tested low priced Gillette razors in Indian mkt but failed. Test subjects were int’l indian students staying in hostel so have running tap  need to understand cust needs and usage  P&G went down to India to observe how they used it – no running tap water, use a cup to rinse it so more diff to get rid of dirt.

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➢ Experimenting with business models, products and services  E.g. Ikea (Russia) tested 2 biz models simultaneously – traditional retail stores VS tapping on popularity of real estate through mixed mall devpt (more profit)  E.g. Fujifilm o Adapted to change and new trends o Branched out into skincare  AsiaLift

Kodak (died) o First digital camera BUT o Stuck to their roots, didn’t wanna change/adapt to env

➢ Produces failure BUT adaptive companies v. tolerant of failure  learn from it 3. Managing Complex and Interconnected Multicompany Systems ➢ Signal detection and experimentations mean that companies must work closely with customers and suppliers in determining strategies ➢ Create an entire adaptive business ecosystem to enhance flexibility and interactions with minimal barriers  E.g. Nokia would still be leading smartphone mkt but attacked by Apple’s & Android’s adaptive systems of many suppliers & app developers  asset + capabilities of many entities  leapfrog experience curve to become market leaders in record time.  E.g. EBay’s complex network of sellers and buyers relies on seller ratings and online payments to support the marketplace 4. Mobilizing Employees (Unlocking Employe...