Discovering the dynamics of smart business networks

Abstract

This paper describes a simple diffusion process whereby to discover the dynamics of emergent smart business network structures and their performance in view of business collaboration patterns over time. They include the destabilizing effects of business relationship tie-up’s or break-downs, and are discussed various partner evaluation, filtering and self-preference strategies. Three real life cases of business network dynamics are discussed based on data from the high tech sector. Lessons learnt from such cases are reported regarding overall smart network dynamic parameters with respect to local interaction strategies.

Appendix: numerical case

This simple three-party numerical case illustrates the notions introduced in this paper, and the corresponding results. Because dynamics are involved, even with this small number of partners and 8 business interactions, the results are too bulky to be published “in extenso”.

$Assumptions\!\!:$ A three-party 2-resource category example is taken, with non-time dependent resource allocations; five successive stages are considered:

r(1,2,,1) = 2,000; r(1,3,.,1) = 700; r(2,1,.,1) = 8,000; r(2,3,.,1) = 2,500; r(3,1,.,1) = 2,500; r(3,2,.,1) = 3,000

r(1,2,.,2 = 1,000; r(1,3,.,2) = 6,000; r(2,1,.,2) = 900; r(2,3,.,2) = 2,000; r(3,1,.,2) = 4,500; r(3,2,.,2) = 700

The initial (t = 0) self-fulfillment grades are:

Fulfill(1,2,0) = 0,2; Fulfill(1,3,0) = 0,2; Fulfill(2,1,0) = 0,1; Fulfill(2,3,0) = 0,1; Fulfill(3,1,0) = 0,7; Fulfill(3,2,0) = 0,9

The initial partnership degrees are set equal to d(i, j, 0) = 5,000. The fulfillment costs $C{\textit{(}}j,i,t{\textit{)}}$ are set proportional, with a coefficient of 0,4, to net revenues accrued by task allocation. The set-up costs Setup-costs (d(i, j, t)) are set to be proportional to $d\textit{(}i,j,t\textit{)}$ with a fixed coefficient of 0,08.

${Results}$

For diffusion process constants $k1=0,8$ and $k2= 0,3,$ the following dynamic outcomes are observed:

• a. The interaction profits $p\textit{(}j,i,t\textit{)}$ have very fast dynamics; the profits of business between (1, 2), (1, 3) remain positive, as well as up to and including t = 2 between (2, 3). The interaction profits between (2,1) run at a loss until halted after t = 2.This means that between partners 1 and 2, there is on-going business, but it onlylasts one way.

• b. Regarding cumulated profits $P{\textit{(}}i,t{\textit{)}}$(see Table 3), only partner “1” runs constant profits, while partner “2” has swings until getting out at t = 3 from any interaction, and partner “3” starts with a loss, and gets out from t = 1.

• c. The partner selection rule exhibits swings as one would expect (see Table 4); for periods from t = 1 until t = 3, partner “1” outsources preferentially to partner “2”, to stop it at t = 4.

• d. The degree of cooperation $d{\textit{(}}j,i,t{\textit{)}}$ grows fastest between (1, 3), while the interactions between (1, 2) and (2, 1) have comparable evolutions.The interactions (3, 1) and (3, 2) fall fast, showing that partner “3” is progressively outsmarted by “1” and “2”.

Table 3 Evolution of the cumulated profits $P\textit{(}i,t\textit{)}$ from the smart business network interaction for each partner; when they are equal to zero, this means the partner has opted out

Table 4 Local selection rule of Sect. 4.4 giving the values of [c (k) * d (i,k t)];