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Weak Signals for Strategic Intelligence: Anticipation Tool for Managers

Book Description

The expression "We did not see it coming!" has often been heard in recent years from decision makers at the highest levels of the private and public sectors. Yet there were actually early warning signals, but they were often ignored due to a lack of appropriate methodology. Focusing on the concept of a weak signal, this book provides methods for anticipating problems and dealing with blind spots. Along with examples of this concept, the authors provide answers to questions of feasibility, including how to recognize a weak signal, and how to exploit it. Numerous applications are also presented throughout.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Introduction
    1. I.1. Introductory example: a surprising encounter on the corner of an alley: Tata
      1. I.1.1. Sales engineer A, on a July 2006 morning
      2. I.1.2. Salesman C (from the German car manufacturer X), late August
      3. I.1.3. Financial executive B (an employee of the German car manufacturer X), some four months later
      4. I.1.4. Post scriptum
    2. I.2. Conclusion
  5. Chapter 1: Concepts, Issues and Hypotheses
    1. 1.1. Introduction: governance and radar
      1. 1.1.1. Steering the ship
      2. 1.1.2. Corporate governance and strategic decision-making
      3. 1.1.3. The ship’s radar (radio detection and ranging)
      4. 1.1.4. The organization’s “radar”, a tool for its governability
    2. 1.2. The organization’s environment and its governance through a “storm”
      1. 1.2.1. The ship, the ocean, and any danger to be faced
      2. 1.2.2. The enterprise, its environment, uncertainty, hazards, and opportunities
        1. Examples of causes of hazard
          1. Competitors
          2. Instability, volatility, turbulence
          3. Lack of visibility
        2. From the environment to the strategy
        3. From strategy to the environment
      3. 1.2.3. Scrutinizing and interpreting the environment
    3. 1.3. Anticipation (act of looking forward)
      1. 1.3.1. Anticipating: definition and examples
        1. Summing up
      2. 1.3.2. Do not confuse anticipation with forecasting
      3. 1.3.3. Anticipation and scenario-based prospective: possible complementarity
      4. 1.3.4. Anticipating odd events, discontinuities, anomalies, etc
    4. 1.4. Anticipative information: two types
      1. 1.4.1. Definition
      2. 1.4.2. Difference between strategic information and day-to-day management information
      3. 1.4.3. Two types of anticipative information
        1. Capability information
        2. Information that may trigger a warning
    5. 1.5. Weak signals
      1. 1.5.1. Definition of a weak signal
      2. 1.5.2. An example of weak signal as the trigger to a warning
      3. 1.5.3. Should we prefer a “strong” but backward-looking signal, or a “weak” but forward-looking signal?
        1. Commentary
        2. Ignored strong signals
      4. 1.5.4. Conversion, transformation of a weak signal into an early warning signal
      5. 1.5.5. Should we refer to a “signal” or a “sign”? Intentionality of the sender
      6. 1.5.6. Weak signals… or decoys, deceptions, and information asymmetry
      7. 1.5.7. Characteristics of a weak signal: “stealthy information”
        1. Useful characteristics
        2. Regrettable characteristics
      8. 1.5.8. Sources emitting weak signals: examples
        1. Field sources
        2. Digital sources
        3. Weak signals provoked by the receiver himself
    6. 1.6. Detecting weak signals
      1. 1.6.1. Individual intelligence (in the Latin sense of the word): a definition
      2. 1.6.2. Cognitive style of a person
      3. 1.6.3. Individual cognitive biases
      4. 1.6.4. Fear
    7. 1.7. Interpreting, amplifying and exploiting weak signals to support strategic decision making
      1. 1.7.1. Need for collective intelligence (CI) for interpreting weak signals
      2. 1.7.2. CM: justification and definition of the process
        1. Definition
        2. Deliverables expected from collective creation of meaning
        3. Thought process carried out during collective creation of meaning
          1. Inductive reasoning
          2. Heuristic reasoning
          3. Lateral thinking
          4. Associative memory
          5. Bounded rationality
      3. 1.7.3. Definition of CI as the emergence of CCM
      4. 1.7.4. From CCM to knowledge management
        1. Discontinuous mode of collective creation of meaning
        2. The need for mobilization and knowledge management
    8. 1.8. Puzzle® method for the operationalization of CCM
      1. 1.8.1. Issue: why the puzzle metaphor?
        1. A puzzle without a model
        2. Definition
      2. 1.8.2. Definition of the Puzzle® method
        1. Positioning information items in relation to one another
        2. Constructing potential links among items of information
        3. Storing successive puzzles for an audit and/or for possible future modeling of the treatment of weak signals
        4. Avoiding confusion between the graphical representation of the puzzle and the drawing of a mindmap obtained using software
      3. 1.8.3. Fundamental hypotheses of the Puzzle® method
      4. 1.8.4. Work group and CI
    9. 1.9. Global VASIC process for detecting, recognizing and utilizing weak signals
      1. 1.9.1. Targeting of anticipative scanning and information sources
      2. 1.9.2. Tracking and individual selection of weak signals
      3. 1.9.3. Escalating information, collective/centralized selection and storage
      4. 1.9.4. Dissemination and preparation of information for CCM sessions
      5. 1.9.5. Animation
      6. 1.9.6. Measurements: performance indicators of the VASIC process
    10. 1.10. Conclusion
      1. 1.10.1. Results on completion of Chapter 1
  6. Chapter 2: Detecting, Recognizing and Corroborating a Weak Signal: Applications
    1. 2.1. Recognition of a weak signal: examples
      1. 2.1.1. A lady heading up the purchasing function at a car equipment manufacturer? How bizarre!
        1. Context
        2. Dialog
        3. Conclusion
      2. 2.1.2. When a weak signal is displayed on a sign in the street!
        1. Conclusion
      3. 2.1.3. A research center at EADS: why Singapore?
        1. Background
        2. Reasoning performed by the animator (it is he who italicized words in Box 2.1)
        3. Conclusion
      4. 2.1.4. Danone
        1. Background
        2. Beginning sequence of the experiment
        3. Interpretation
        4. Conclusion
    2. 2.2. Making a new weak signal reliable
      1. 2.2.1. Reliability of the information source
      2. 2.2.2. Comparing the weak signal with other information obtained previously
      3. 2.2.3. Consulting with an “expert”
      4. 2.2.4. Feedback from the animator to the gatekeeper who provided the weak signal
    3. 2.3. Conclusion
      1. 2.3.1. Result
  7. Chapter 3: Utilization of Weak Signals, Collective Creation of Meaning: Applications
    1. 3.1. The Roger case: should we fear this new entrant to our industry? (the banking sector)
      1. 3.1.1. Issues for Roger as a company
      2. 3.1.2. Context
      3. 3.1.3. Codexi
      4. 3.1.4. Information to be used
      5. 3.1.5. Conduct of the collective work session
      6. 3.1.6. Results
        1. Emergence of CI
        2. A surprise to participants
          1. Bounded rationality
          2. Positive end result: creation of meaning has been performed collectively
        3. Stimulating regrets: an interesting lead
    2. 3.2. The case for “valorizing CO2 as a commodity”: a preliminary study for the selection of a new strategic direction
      1. 3.2.1. The main problem: how to “give birth to an idea” within the Board of Directors (BoD)?
      2. 3.2.2. Challenge: arousing the interest of the BoD
      3. 3.2.3. Preparing for the session (which will prove to be the first session)
      4. 3.2.4. Background of the experiment (first session)
        1. Ideal objective
      5. 3.2.5. Conduct of the session (first session)
        1. Targeting
      6. 3.2.6. Second session, three months later
        1. Conduct of the session
        2. Commentary
      7. 3.2.7. Conclusion and post-scriptum
    3. 3.3. The Danone case. The ministry is worried: are there signs showing that companies will destroy jobs over the next two years? Could Danone leave France?
      1. 3.3.1. The issue at hand
      2. 3.3.2. Fresh interest in weak signals
      3. 3.3.3. Background: lack of cross-disciplinarity
      4. 3.3.4. Organization and conduct of the experiment
      5. 3.3.5. Targeting of a field of study
      6. 3.3.6. Selection of Danone as an agent
      7. 3.3.7. Conduct of the CCM experiment
        1. Initiating interest in weak signals
        2. Puzzle Links: alleviating initial data fragmentation, and consequently Danone’s low visibility
        3. Danone weary of obstacles encountered in France?
        4. The sirens’ call from abroad
      8. 3.3.8. Conclusion at the close of the last session: huge plausible risk on the horizon!
    4. 3.4. The Opel case: initiating collective transversal intelligence to aid strategic decision-making
      1. 3.4.1. Issues and background
      2. 3.4.2. CI
      3. 3.4.3. Organizational context
      4. 3.4.4. Preparatory step upstream of the first CCM session
      5. 3.4.5. Conduct of the CCM session
      6. 3.4.6. Conclusions
        1. Emergence of transversal CI
        2. Methodological findings reached by the animator, lessons learned and leads toward new enrichments for the Puzzle® method
          1. One observed difficulty
          2. A new problem
    5. 3.5. Conclusion
      1. 3.5.1. Results
  8. Chapter 4: Preparation of Weak Signals for Sessions in Collective Creation of Meaning: Applications
    1. 4.1. Introduction: two starting situations
    2. 4.2. The Roger case (continued): how are the news briefs used in the Roger CCM session prepared?
      1. 4.2.1. Preparation of the news briefs used in the CCM
      2. 4.2.2. The search for raw data: a substantial task
      3. 4.2.3. Extraction of news briefs: a time-consuming, delicate task
      4. 4.2.4. The Internet trap
    3. 4.3. CO2 valorization case: automatic search for “news briefs”
      1. 4.3.1. Guiding idea: “FULL text” distillation
      2. 4.3.2. Steps in the search for “possible weak signal” news briefs
        1. Using Approxima in the “CO2 valorization” case
        2. The importance of learning
    4. 4.4. The Danone case: preparation of the weak signals
      1. 4.4.1. “Manual” search
      2. 4.4.2. “Manual” extraction
      3. 4.4.3. Automatic news briefs search and extraction
      4. 4.4.4. Conclusions on the “CO2 valorization” and “Danone” cases using the Approxima prototype
    5. 4.5. Software modules for assisting in the automatic search for news briefs
      1. 4.5.1. Lookup table of characteristic words for the field being explored. Continuation of the “CO2 valorization” case
      2. 4.5.2. Enhancing the anticipative- and characteristic-word bases
        1. “Anticipative” words
        2. “Characteristic” words (or keywords)
      3. 4.5.3. Semantics problems: synonyms, polysemes and related matters
        1. Automatic search for “adjacent” information items
        2. Associative computer memory
        3. Searching for odd associations of words or information items
          1. IBM
          2. MACAO
        4. Creation of noise
      4. 4.5.4. Software enabling “event searches”
      5. 4.5.5. Integration platform for commercially available software modules
    6. 4.6. Conclusion
      1. 4.6.1. Result
  9. Conclusion
  10. Glossary
  11. Bibliography
  12. Index