Question: why we do supply network coordination? after doing supply netwrok coordination what we get?

sub: supply chain managment

2.4. Supply network coordination Unlike the empirical research approach to SCM that we have reviewed in the previous sections, there have been several literature survey articles that focus on the mathematical modelling approach to SCM. As such, we chose not to review the research base that attempts to model and optimize supply chain problems, and 144 1. 1. Chen and A. Parulraj instead refer interested readers to the focused reviews written by Thomat and Griffin (1996), Beamon (1998), Erengue el al. (1999), and Sahin and Robinson (2002). The characteristies of the modelling approach including the scope of problems, decision variables, and methodologies, however, will be briefly described here to complete our coverage on diverse streams of SCM research. A sizeable number of researchers have adopted the mathematical modelling approach in their study of SCM. Under this approach, a common goal is to optimize the planning and coordination of the three fundamental supply chain stages: procurement, production, and distribution. Each of the three stages may be further comprised of multiple facilities in various locations in different countries. It is. thus, understandable that the majority of researchers have opted to model a much narrower scope of supply chain problems, as the three stages may span many functional depariments within and across firms and the complexity has made the formulation of supply chain models challenging and the optimal solution problematic, if not impossible. For example, researchers have concentrated on the optimal ordering policies in buger-vendor coordination using economic order quantity (EOQ) and quantity discount inventory models (e.g. Goyal 1988, Lau and Lau 1994). Various forms of production-inventory-distribution coordination have aho been widely studied, though many problems in these areas, formulated as dynamic programming, integer programming or non-linear programming. are extremely difficult to solve (e.g. Ernst and Pyke 1993, Chandra and Fisher 1994, Lee et al. 1997). Not surprisingly, researchers of this approach have spent more energy in developing algorithms and heuristic procedures for the problems they formulated than in understanding what initiatives and activities constitute the new management philosophy of SCM or shaping the notion of SCM. Most of the supply chain modelling research is an extension or integration of the traditional problems of (1) production planning and inventory control and (2) distribution and logisties. Depending on the scope of supply chain issues researehers chose to address, the decision variables used in their models could include demand variability, production scheduling, inventory levels, number of echelons (stages). distribution centres, manufacturing plants, number of products types, etc. The methodology adopted in the modelling approach is then determined by the decision variables considered and the objective of the study. The supply chain problems are formulated cither as deterministic analytical models, if the decision variables are known with certainty, or as stochastic analytical models, when at least one of the decision variables is unknown and is assumed to follow a particular probability distribution. Simulation methods have also been adopted for analysing more complex problem settings that include a larger number of decision variables where optimal solutions may not be possible. Apart from the growing stream of supply chain optimization models, the study of the 'bullwhip eflect' is a noticeable contribution of the modelling approach in providing additional insights into supply chain dynamics. Anchored on Forrester's visionary work (Forrester 1958, 1961), Lee et al. (1997) refines the supply chain's natural tendency to amplify, delay, and oscillate demand information, and demonstrates that rational independent decision-making, increased arder lead-time, and simultaneous ordering by retailers increase demand amplification. While many more studies have explored the causes of the bullwhip effects, the magnivde oullwhip effects reduction through information sharing and physical coordinatio s still not well understood. Understanding supply chain management 145 The mathematical modelling approach is excellent in providing insight and understanding in well-defined supply chain settings involving few decision variables and highly restrictive assumptions. This approach is, however, deficient when applied to more reulistie and, thus, more complex supply chain situations. Therefore, the result of much supply chain modelling research is mathematical rigor that suffers from unrealistic assumptions and lack of generatity. It should also be noted that most principles behind the mathematieal models are shaped by the empirical studies in (1) strategic purchasing. (2) supply management and (3) logisties integration claborated above