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Foreseeing the Dynamics of Strategy: An Anticipatory Systems Perspective

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Abstract

The paper explores firms as complex anticipatory systems which construct dynamic strategic configurations based on anticipation of their future possible states within the competitive environment. We argue that firm’s performance depends on (a) its strategy making process based on anticipation, and (b) its managerial capabilities which effectuate the anticipatory process in the following four stages: search across anticipated “what-if” resource configurations, the articulation and conversion of their meaning, and the finding and evolution of strategic patterns and courses of action for environmental fit. We performed an in-depth exploratory study with a group of senior managers in a pharmaceutical firm to uncover diverse anticipatory capabilities. The study was based on the development and re-assessment of a product market strategy for a new drug launch without and with the use of a simulation-based learning environment. The results show the existence of heterogeneous anticipatory process, which we name search-articulate-find-evolve of alternative resource configuration sets, determining the managerial dynamic capabilities related particularly to managerial cognition and decision making. We propose anticipation enhanced by modelling and simulation can improve managers’ mental processes and help them to overcome cognitive limitations when dealing with real-world complexities.

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Notes

  1. Due to confidentiality agreements, we cannot present the value of constants in the model.

  2. Some of the constants linked in the causal loop diagram appeared not linked in the stock and flow diagram because we tried to observe the creation of the link, and test their verbal description, through the manipulation of the variables in the model interface (see next page). In other words, the stock and flow diagram reflects the interactive learning environment approach.

  3. The model involves multiple drugs so the equations listed show the subscripts. In some variables, we are not able to show their values so we employed “N.A.” for those cases.

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Authors and Affiliations

  1. Strateggo Ltd., Han Asparuh 8, 1643, Sofia, Bulgaria

    Rossen Kazakov

  2. Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK

    Martin Kunc

Authors
  1. Rossen Kazakov
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  2. Martin Kunc
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Correspondence to Martin Kunc.

Appendix

Appendix

A General Description of the Model

See Fig. 5.Footnote 1,Footnote 2

Fig. 5
figure 5

Stock and flow model

Full size image

Model Interface

The Model Interface included six key pay-off (dependent/output) variables: Prescribers, Currently treated patients, Total sales volume per, Total sales value per, Gross profit per and Net profit per each subscripted drug in the model (here given in blank due to confidentiality reasons), identified by the managers.

The sliders represent the (independent/input) variables the participants explored under different combinations leading to the formation of ARCS included: Drug price factor per, Drug price per, Manf cost per, percentage of gross profit to Marketing Budget, visit per doctor, motivation budget per doctor, percentage reaching real therapy.

The associations between diverse input variables, as well as with output variables, were defined during the discussions with managers but the user interface was employed to test their associations.Footnote 3

Model Equations—Vensim Listing

Total patients on treatment[drug] = Average patients per doctor[drug]*Prescribers[drug]

Adoption from WOM[drug] = doctors contact rate[drug]*adoption fraction[drug]* (Prescribers[drug] + Potential prescribers[drug] + Target specialist doctors[drug])

adoption rate[drug] = (Adoption from sales force[drug] + Adoption from WOM[drug] + Adoption from additional motivation [drug])

“% of gross profit to MB”[drug] = N.A.

“% treated by NHIF” = 0.7

new people with diabetis = new diabetics per Q

switch treatment rate[drug] = 0.5,0.3,0.3,0

switching patients[drug] = INTEG (switch treatment[drug]-switch allocation[drug],)

rate of patients on oral antidiabetics[drug] = (People with diabetis not treated[drug]*percentage reaching real therapy*percentage of patiens on… *” % treated by NHIF”)*allocation of patient flows by doctors[drug]

People with diabetis not treated[drug] = INTEG (new people with diabetis-rate of patients on oral antidiabetics[drug],)

Currently treated patients[drug] = INTEG (rate of patients on oral antidiabetics[drug] + reinitiation rate[drug] + switch allocation[drug]-rate of nonpersistence[drug]-switch treatment[drug],initial patients on treatment[drug],)

Marketing budget[drug] = ” % of gross profit to MB”[drug]*Gross profit per[drug]

allocation of patient flows by doctors[drug] = Drug attractiveness[drug]*Prescribers[drug]

time to switch[drug] = 3

switch treatment[drug] = switch treatment rate[drug]*Currently treated patients[drug]/time to switch[drug]

reinitiation rate[drug] = SUM(patients reinitiating treatment[drug!])*allocation of patient flows by doctors [drug]

switch allocation[drug] = SUM(switchig patients[drug!])*allocation of patient flows by doctors[drug]/time to switch[drug]

“% allocation of patients treatment”[drug] = Prescribers[drug]/Total doctors

Total doctors = 200

abandoning fraction[drug] = 0.3, 0.3, 0.4, 0.2

abandoning rate[drug] = abandoning fraction[drug]/time to abandon[drug]

adoption fraction[drug] = 0.1, 0.1, 0, 0.1

Adoption from additional motivation[drug] = motivation adoption fraction[drug]*Potential prescribers[drug]

Adoption from sales force[drug] = effect of sales force[drug]*Potential prescribers[drug]

average number of packs per patient on oral[drug]=

time to abandon[drug] = 3, 3, 3, 3

Average patients per doctor[drug] = Currently treated patients[drug]

Complience factor[drug] = 0.9, 0.5, 0.3, 0.5

doctors contact rate[drug] = 2, 2, 0, 2

contact rate per month[drug] = number of visited doctors per month[drug]*visits per doctor[drug]

“market share (volume)”[drug] = average number of packs per patient on oral[drug]*Total patients on treatment[drug]

Drug attractiveness[drug] = (Drug price factor[drug] + reputation factor[drug] + Complience factor[drug] + Side effects[drug] + Tolerability[drug])/5

Drug price factor[drug] = 0.3, 0.9, 1, 1

effect of sales force[drug] = Function effect of contact rate on sales force effectiveness[drug](contact rate per month [drug])

Function effect of contact rate on sales force effectiveness[drug]

“Function effect of drug attr on  % allocation of patients”[drug]

function effect of motivation budget on motivation adoption fraction[drug]

Target specialist doctors[drug] = INTEG (abandoning rate[drug]-target rate[drug],initial specialist doctors[drug])

time to inform[drug] = 1, 1, 1, 1

Tolerability[drug] = 0.9, 0.7, 0.5, 0.7

initial prescribers[drug] = 160, 30, 10, 5

initial specialist doctors[drug] = 200, 200, 200, 200

price per visit[drug] = 36, 20, 16, 16

number of visited doctors per month[drug] = 200, 200, 200, 200

visits per doctor[drug] = 2, 2, 2, 2

motivation adoption fraction[drug] = function effect of motivation budget on motivation adoption fraction[drug](motiv budget per doc per patient[drug])

motiv budget per doc per patient[drug] = N.A.

reputation factor[drug] = 0.9, 0.6, 0.3, 0.7

sales force budget for vists[drug] = contact rate per month[drug]*price per visit[drug]

Side effects[drug] = 0.1, 0.2, 0.3, 0.2

target fraction[drug] = 0.2, 0.85, 0.95, 1

target rate[drug] = Target specialist doctors[drug]*target fraction[drug]/time to inform[drug]

Potential prescribers[drug] = INTEG (target rate[drug]-adoption rate[drug],)

Prescribers[drug] = INTEG ((adoption rate[drug]-abandoning rate[drug]),initial prescribers[drug])

Total sales volume per[drug] = Average pack per patient[drug]*Currently treated patients[drug]

Total market oral antidiabetics[drug] = average number of packs per patient on oral[drug]* Currently treated patients[drug]

Average pack per patient[drug] = 3, 3, 3, 3

drug price[drug] = N.A.

fraction of non persistent patients[drug] = 0.5, 0.5, 0.5, 0.5

gross margin per[drug] = drug price[drug]-Manf cost per[drug]

Gross profit per[drug] = gross margin per[drug]*Total sales volume per[drug]

initial patients on treatment[drug] = N.A.

Manf cost per[drug] = N.A.

Net profit per[drug] = Gross profit per[drug]-(Marketing budget[drug] + ”R&D budget”[drug])

new diabetics per Q = N.A.

patients reinitiating treatment[drug] = fraction of non persistent patients[drug]*Non persistent patients[drug]

percentage of patiens on…= N.A.

“R&D budget”[drug] = N.A.

Total sales value per[drug] = drug price[drug]*Total sales volume per[drug]

percentage reaching real therapy = N.A.

rate of nonpersistence[drug] = N.A.

Non persistent patients[drug] = INTEG (rate of nonpersistence[drug]-reinitiation rate[drug],)

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Kazakov, R., Kunc, M. Foreseeing the Dynamics of Strategy: An Anticipatory Systems Perspective. Syst Pract Action Res 29, 1–25 (2016). https://doi.org/10.1007/s11213-015-9349-0

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