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The Dynamics of Operational Problem-Solving: A Dual-Process Approach

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Abstract

Adopting a systems thinking perspective, this study establishes the micro-foundation of two distinct behavior modes in the context of operational problem-solving. More specifically, we explicate how problem-solvers cope with problems by unpacking potential behavior modes from a cognitive perspective. Drawn from dual-process theory, the first behavior mode is based on heuristic reasoning to eliminate problem symptoms whereby problem-solvers work around the problems employing short-term remedies and prompt fixes to temporarily solve the problem. The second mode relies on structured reasoning aimed at solving the problems fundamentally with the help of structured corrective actions. We label them as intuitive and analytical problem-solving respectively (IPS and APS). Although the effectiveness of APS to achieve sustainable success has been asserted in the literature, problem-solvers are more likely to adopt IPS, a phenomenon that is called “IPS dominance”. Motivated by field work at a manufacturing plant, we develop a system dynamics model to scrutinize these two behavior modes separately as well as the transition dynamics between them to shed light on the major reasons of for “IPS dominance”.

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Notes

  1. The system dynamics model is built in the Vensim Simulation Environment for Windows, version 6.4b.

References

  • Argyris C (1976) Single-loop and double-loop models in research on decision making. Adm Sci Q 21:363–375

    Google Scholar 

  • Argyris C (1977) Double loop learning in organizations. Harv Bus Rev:115–125

  • Astor, T., Morales, M., Kieffer, D., & Repenning, N. (2016) What problem are you trying to solve: an introduction to structured problem solving. MIT Sloan Manage Rev,Working Paper

  • Baer M, Dirks K, T., & Nickerson, J., A. (2013) Microfoundations of strategic problem formulation. Strateg Manag J 34:197–214

  • Beer M, Eisenstat R, Spector B (1990) Why change programs don’t produce change. Harv Bus Rev 68:158–166

    Google Scholar 

  • Bohn R (2000) Stop fighting fires. Harv Bus Rev:83–91

  • Brooks I (1994) Managerial problem solving: A cultural perspective. Manag Decis 32:53–59

    Google Scholar 

  • Bruccoleri M, Riccobono F, Größler A (2019) Shared leadership regulates operational team performance in the presence of extreme decisional consensus/conflict: evidences from business process reengineering. Decis Sci 50:46–83

    Google Scholar 

  • Büyükdamgaci G (2003) Process of organizational problem definition: how to evaluate and how to improve. Omega 31:327–338

    Google Scholar 

  • Calabretta G, Gemser G, Wijnberg NM (2017) The interplay between intuition and rationality in strategic decision making : A paradox perspective. Org Studies 38:365–401

    Google Scholar 

  • Choo AS (2014) Defining problems fast and slow: the U-shaped effect of problem definition time on project duration. Prod Oper Manag 23:1462–1479

    Google Scholar 

  • Choo AS, Nag R, Xia Y (2015) The role of executive problem solving in knowledge accumulation and manufacturing improvements. J Oper Manag 36:63–74

    Google Scholar 

  • De Mast J (2013) Diagnostic quality problem solving : A conceptual framework and six strategies. Qual Manag J 20:21–36

    Google Scholar 

  • De Mast J, Lokkerbol J (2012) An analysis of the six sigma DMAIC method from the perspective of problem solving. Int J Prod Econ 139:604–614

    Google Scholar 

  • Donaldson RM (1972) Systematic problem solving. Manag Rev-Condensed from J Syst Manag, 48–50

  • Easton GS, Rosenzweig ED (2012) The role of experience in six sigma project success: an empirical analysis of improvement projects. J Oper Manag 30:481–493

    Google Scholar 

  • Eisenhardt KM (1989) Building theories from case study research. Acad Manag Rev 14:532–550

    Google Scholar 

  • Elbanna S (2012) Slack, planning and organizational performance: evidence from the Arab Middle East. Eur Manag Rev 9:99–115

    Google Scholar 

  • Evans JSBT, Stanovich KE (2013) Dual-process theories of higher cognition: advancing the debate. Perspect Psychol Sci 8:223–241

    Google Scholar 

  • Flood RL (2010) The relationship of systems thinking to action. Syst Pract Action Res 23:269–284

    Google Scholar 

  • Frei F (2007) Conventional wisdom “ Don’t bring me problems - bring me solutions!”. Harv Bus Rev 3

  • Furlan A, Galeazzo A, Paggiaro A (2019) Organizational and perceived learning in the workplace: A multilevel perspective on employees’ problem solving. Organ Sci 30:280–297

    Google Scholar 

  • Galliers R, Mingers J (1997) Organization theory and systems thinking: the benefits of partnership. Org 4:269–278

    Google Scholar 

  • Garvin DA (1993) Building a learning organization. Harv Bus Rev:78–91

  • Gibbert M, Ruigrok W, Wicki B (2008) What passes as a rigorous case study? Strateg Manag J 29:1465–1478

    Google Scholar 

  • Gray PH (2001) A problem-solving perspective on knowledge management practices. Decis Support Syst 31:87–102

    Google Scholar 

  • Hodgkinson GP, Sadler-Smith E (2018) The dynamics of intuition and analysis in managerial and organizational decision making. Acad Manag Perspect 32:473–492

    Google Scholar 

  • Itabashi-Campbell, R., Perelli, S., & Gluesing, J. (2011) Engineering problem solving and knowledge creation: an epistemological perspective. Proceedings of the 1st International Technology Management Conference 777–789

  • Jaaron AAM, Backhouse CJ (2017) Operationalising "double-loop" learning in service organisations: A systems approach for creating knowledge. Syst Pract Action Res 30:317–337

    Google Scholar 

  • Jick TD (1979) Mixing qualitative and quantitative methods: triangulation in action. Adm Sci Q 24:602–611

    Google Scholar 

  • Kahneman D (2011) Thinking, fast and slow. Allen Lane, London

    Google Scholar 

  • Keller J, Sadler-Smith E (2019) Paradoxes and dual processes: A review and synthesis. Int J Manag Rev 21:162–184

    Google Scholar 

  • Kolbe, L. M., Bossink, B., & De Man, A.-P. (2019) Contingent use of rational, intuitive and political decision-making in R&D. Manag Decis

  • Longenecker, Scazzero, Stansfield (1994) Quality improvement through team goal setting, feedback, and problem solving. Int J Qual Reliab Manag 11:45–52

    Google Scholar 

  • Lyles M (1981) Formulating strategic problems: empirical analysis and model development. Strateg Manag J 2:61–75

    Google Scholar 

  • Lyles MA (2014) Organizational learning, knowledge creation, problem formulation and innovation in messy problems. Eur Manag J 32:132–136

    Google Scholar 

  • Lyles MA, Mitroff I (1980) Organizational problem formulation: an empirical study. Adm Sci Q 25:102–119

    Google Scholar 

  • Lyles MA, Thomas H (1988) Strategic problem formulation: biases and assumptions embedded in alternative decision-making models. J Manag Stud 25:131–145

    Google Scholar 

  • Laureiro-Martinez, D., & Brusoni, S. (2018) Cognitive flexibility and adaptive decision-making: evidence from a laboratory study of expert decision-makers. Strategic Manag J 39:1031–1058

  • MacDuffie J (1997) The road to "root-cause": shop-floor problem solving at three auto assembly plants. Manag Sci 43:479–502

    Google Scholar 

  • Marksberry P, Badurdeen F, Gregory B, Kreafle K (2010) Management directed kaizen: Toyota’s Jishuken process for management development. J Manuf Technol Manag 21:670–686

    Google Scholar 

  • Marksberry P, Bustle J, Clevinger J (2011) Problem solving for managers: A mathematical investigation of Toyota’s 8-step process. J Manuf Technol Manag 22:837–852

    Google Scholar 

  • Mintzberg H, Raisinghani D, Theoret A (1976) The structure of "unstructured" decision processes. Adm Sci Q 21:246–275

    Google Scholar 

  • Mitroff II, Featherirjgham TR (1974) On systematic problem solving and the error of the third kind. Behav Sci 19:383–393

    Google Scholar 

  • Morrison B (2015) The problem with workarounds is that they work: the persistence of resource shortages. J Oper Manag 39–40:79–91

    Google Scholar 

  • Okli J, Watt J (2018) Crisis decision-making: the overlap between intuitive and analytical strategies. Manag Decis 56:1122–1134

    Google Scholar 

  • Priem RL, Rasheed AMA, Kotulic AG (1995) Rationality in strategic decision processes, environmental dynamism and firm performance. J Manag 21:913–929

    Google Scholar 

  • Powell TC, (1995) Total quality management as competitive advantage: A review and empirical study. Strategic Management Journal 16 (1):15–37

    Google Scholar 

  • Repenning NP, Sterman JD (2002) Capability traps and self-confirming attribution errors in the dynamics of process improvement. Adm Sci Q 47:265–295

    Google Scholar 

  • Schroeder RG, Linderman K, Liedtke C, Choo AS (2008) Six sigma: definition and underlying theory. J Oper Manag 26:536–554

    Google Scholar 

  • Schwenk CR (1995) Strategic decision making. J Manag 21:471–493

    Google Scholar 

  • Smith GF (1989) Defining managerial problems: A framework for prescriptive theorizing. Manag Sci 35:963–981

    Google Scholar 

  • Spear SJ (2004) Learning to lead at Toyota. Harv Bus Rev

  • Staats BR, Brunner DJ, Upton DM (2011) Lean principles, learning, and knowledge work: evidence from a software services provider. J Oper Manag 29:376–390

    Google Scholar 

  • Sterman JD (2000) Business dynamics: systems thinking and modeling for a complex world. McGraw-Hill, Irwin

    Google Scholar 

  • Stuart I, McCutcheon D, Handfield R, McLachlin R, Samson D (2002) Effective case research in operations management: A process perspective. J Oper Manag 20:419–433

    Google Scholar 

  • Spear S, Bowen HK (1999) Decoding the DNA of Toyota production system. Harv Bus Rev 77: 96–106

  • Tarka P (2017) Managers’ beliefs about marketing research and information use in decisions in context of the bounded rationality theory. Manag Decis 55:987–1005

    Google Scholar 

  • Toledo JC, Gonzalez RVD, Lizarelli FL, Pelegrino RA (2019) Lean production system development through leadership practices. Manag Decis 57:1184–1203

    Google Scholar 

  • Tucker AL (2016) The impact of workaround difficulty on frontline employees’ response to operational failures: A laboratory experiment on medication administration. Manag Sci 62:1124–1144

    Google Scholar 

  • Tucker AL, Edmondson AC (2003) Why hospitals don’t learn from failures. Calif Manag Rev 45:55–72

    Google Scholar 

  • Tucker AL, Edmondson AC, Spear S (2002) When problem solving prevents organizational learning. J Org Change Manag 15:122–137

    Google Scholar 

  • Tyre, M. J. (1995) Systematic versus intuitive problem solving on the shop floor : does it matter ? Massachusetts Institute of Technology Sloan School of Management, Working Paper

  • Ufua DE, Adebayo AOL (2019) Exploring the potency of rich pictures in a systemic lean intervention process. Syst Pract Action Res:1–13

  • Volkema RJ (1986) Problem formulation as a purposive activity. Strateg Manag J 7:267–279

    Google Scholar 

  • Volkema RJ, Gorman RH (1998) The influence of cognitive-based group composition on decision-making process and outcome. J Manag Stud 35:105–121

    Google Scholar 

  • Werder AV (1999) Argumentation rationality of management decisions. Organ Sci 10:672–690

    Google Scholar 

  • Yin R (2009) Case study research: design and methods. Sage, Thousand Oaks

    Google Scholar 

  • Zollo M, Winter SG (2002) Deliberate learning and the evolution of dynamic capabilities. Organ Sci 13:339–351

    Google Scholar 

Download references

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Correspondence to Matin Mohaghegh.

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Appendix

Appendix

In order to simulate the behavior modes, the causal diagrams were converted into a formal system dynamic model, based on a stock and flow diagram. According to system dynamic methodology, stocks are accumulations and altered by inflows and outflows. Stocks create delays by accumulating the difference between these inflows and outflows (Sterman 2000).

Fig. 6
figure 6

IPS Stock and flow diagram

Equations Used for IPS Adoption Simulation:

The subsequent listing shows the way the most considerable stocks, inflows, and outflows are calculated for the IPS simulation. For the stock “Prevalent Problems”, two inflow rates and two outflow rates are defined, so the formula is as follows:

  1. (1)

    Prevalent Problems = INTEGRAL (Problem Initiation Rate + Problem Recur Rate - Problems Fundamentally Solved Rate - Problems Temporarily Solved Rate); in other words, the total number of prevalent problems is calculated as the difference between the problems that enter the system (the sum of problem initiation rate and problem recur rate) and the problems that are either temporarily or fundamentally solved (the sum of problems fundamentally solved and the problems temporarily solved).

Units: # Problems; the unit is the number of problems in the system.

Initial Value = 10; As an assumption, the initial number of problems in the system is 10.

  1. (2)

    Problems Temporarily Solved = INTEGRAL (Problems Temporarily Solved Rate – Problem Recur Rate)

Units: # Problems

Initial Value = 10

  1. (3)

    Problems Temporarily Solved Rate = (Workarounds * Fraction of Workarounds Solving the Problems Temporarily)/ Time to Solve.

Fraction of Workarounds Solving the Problems Temporarily =1; We assume that all the problems in IPS are solved temporarily.

Units: Problems/ Week

  1. (4)

    Problem Recur Rate = DELAY (Latent Problems/ Time to Recur, 1); In IPS, problems are temporarily solved, and the major causes become hidden first and re-appear again with a delay.

Units: Problems/ Week

  1. (5)

    Latent Problems = INTEGRAL (Creation Rate of Negative Consequences – Elimination Rate of Negative Consequences)

Units: # Problems

Initial Value = 10

  1. (6)

    Workarounds = INTEGRAL (Workarounds Creation Rate)

Units: # Solutions.

Initial Value = 10

Time to Create Workarounds = 0.5 weeks; In IPS adoption, generally, problem-solvers do not spend a lengthy time to jump to a solution.

  1. (7)

    Creation Rate of Negative Consequences = (Workarounds * Fraction of Workarounds Creating Negative Consequences)/ Time for Negative Consequences + (Tendency for IPS * Fraction of IPS Tendency for Negative Consequences Recreation)/ Time for Negative Consequences); when a problem is temporarily solved, it brings gratification and self-confidence for the problem-solver and creates a higher tendency for IPS adoption for further problems.

Units: Problems/ Week

  1. (8)

    Additional Resources for IPS in Problem Saturation = IF THEN ELSE (Efforts to Solve the Problems >30, Available Resources, 0); Additional available resources are assigned only when the total number of problems exceeds a certain level (30 in our example). Otherwise, in a normal situation, top-managers stay with current human resources and employ already-made solutions.

Units: People

Available Resources = 10

  1. (9)

    Pressure to Solve the Problems as soon as possible due to Time Pressure = 10; IPS is characterized by an urgency to solve the problems immediately. This occurs due to time pressure. From a scale of 1 to 10, we consider 10 as the maximum value for this pressure.

Units: Dmnl/ Problems.

Fig. 7
figure 7

APS Stock and Flow Diagram

Equations Used for APS Adoption Simulation:

  1. (1)

    Prevalent Problems = INTEGRAL (Problem Initiation Rate – Problems Fundamentally Solved Rate)

Units: Problems.

Initial Value = 10

  1. (2)

    Ideas = INTEGRAL (Idea Generation Rate – Idea Elimination Rate)

Units: Solutions

Initial Value = 0.

  1. (3)

    Structured Corrective Actions = INTEGRAL (Structured Corrective Action Creation Rate)

Units: Solutions

Time to Create Structured Corrective Actions = 2 Weeks; In APS adoption, problem-solvers spend more time (compared to IPS) to comprehensively define the problem and reach fundamental solutions.

Initial Value = 0

  1. (4)

    Resource Allocation for APS = IF THEN ELSE (Efforts to Solve the Problems >0, Available Resources, 0); In APS adoption, unlike IPS, human resources are assigned for structured actions once a problem is initiated.

Units: People.

  1. (5)

    Working Collaboratively = Resource Allocation for APS * Collaboration Factor * Pressure to Solve the Problems Fundamentally; in APS adoption, unlike IPS, top managers encourage team-based problem-solving by stressing collaboration.

Units: Dmnl

  1. (6)

    Pressure to Solve the Problems Fundamentally = 10; APS adoption is characterized by top managers’ awareness and attention to solve the problem fundamentally using structured corrective actions rather than forcing the employees to employ short-term remedies for IPS. So, on a scale of 1 to 10, we consider 10 as the maximum value for this pressure.

Units: Dmnl

  1. (7)

    Knowledge = Ideas * (Idea-Knowledge Factor)

Units: Dmnl

  1. (8)

    Idea-Knowledge Factor = 0.5, This is constant, and we assume that half of the total ideas generated regarding the problem structure (i.e. the precise problem, and its potential causes) by the team can be converted to rich and valuable knowledge as the foundation for structured corrective actions.

Units: Dmnl/ Solutions

  1. (9)

    Problems Fundamentally Solved Rate = (Structured Corrective Actions * Fraction of Structured Corrective Actions Solving the Problems Fundamentally) / Time to solve the Problems Fundamentally; in APS, no problems are temporarily solved. Instead, we assume that all the problems (100%) are fundamentally solved by the structured actions employed.

Units: Problems/ Week

  1. (10)

    Fraction of Structured Corrective Actions Solving the Problems Fundamentally = 1.

Units: Dmnl

Examples of Interview Questions

Table 4 These are examples of how we conducted our interview

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Mohaghegh, M., Größler, A. The Dynamics of Operational Problem-Solving: A Dual-Process Approach. Syst Pract Action Res 33, 27–54 (2020). https://doi.org/10.1007/s11213-019-09513-9

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