Many industries are facing big challenges to design supply chains in a way to maximize the profit and meet the heightened expectations of the customer. This new era entirely relies on the dynamic advantages of competition and the role played by the collaboration policy. A global economy and increasing demand have put a huge pressure on supply chain partners to build a collaboration policy based on price, order quantity, and advertising. Companies are adopting the idea of ”shaking hands” to obtain more profit instead of taking risks through competition. Cooperative (co-op) advertising is a significant policy of centralized supply chain management (SCM) to boost the revenues generated by the supplier, manufacturer, and retailers. The uncertain costs associated with the supply chain management also create obstacles in economic analysis and feasibility. These uncertainties are associated with the basic costs of all supply chain partners, which are represented using a signed distance formula. This paper develops the concept of co-op advertising among the supplier, manufacturer, and retailers with a variable demand driven by selling price and advertising costs, where all basic costs are considered as fuzzy. The profit is optimized by considering variable cycle time, shipments, pricing and advertising costs for the decision support system of the supply chain management. The optimal results of the co-op advertisement ensured an increase in the revenue of whole supply chain.
|Journal||Applied Soft Computing Journal|
|Publication status||Published - 2020 Mar|
Bibliographical noteFunding Information:
This work was supported by the Ulsan National Institute of Science and Technology through the Development of 3D Printing-based Smart Manufacturing core Technology research Fund under Grant 1.190032.01 . Appendix The list of notation for the manufacturer, retailer, and suppiler are provided as follows: Indices j index used to indicate retailers, j = 1 , 2 , … , n Decision variables T cycle time for multi-retailers (year) z 1 integer multiple for the manufacturer’s cycle time (number) z 2 integer multiple for the retailer’s cycle time (number) y 1 shipments of raw material received by the supplier (numbers) y 2 shipments of semifinished parts received by the manufacturer (numbers) p j selling price of the retailer j ($/unit) b r j advertisement cost invested by the retailer j ($/year) b m j advertisement cost invested by the manufacturer for the retailer j ($/year) b s j advertisement cost invested by the supplier for the retailer j ($/year) Supplier’s parameters C s production cost of the semifinished product ($/unit) P s production rate of the semifinished products (unit/year) h s ́ raw material’s holding cost ($/unit/year) h s finished product’s holding cost of the supplier or raw material holding cost of the manufacturer ($/unit/year) M s maximum inventory (unit) Q s lot size (unit/year) A s setup cost ($/setup) O s ordering cost ($/order) F fixed transportation cost ($/shipment) V variable transportation cost ($/shipment) C f c s cost of fixed carbon emission ($/shipment/year) C v c s cost of variable carbon emission ($/unit/year) T C s total cost ($/year) Manufacturers’ parameters P m production rate of semifinished products (unit/year) h m finished product holding cost ($/unit/year) M m maximum inventory (unit) Q m lot size (unit/year) A m setup cost ($/setup) O m ordering cost ($/order) F fixed transportation cost ($/shipment) V variable transportation cost ($/shipment) C f c m cost of fixed carbon emission ($/shipment/year) C v c m cost of variable carbon emission ($/unit/year) C m production cost ($/unit) T C m total cost ($/year) Retailer’s parameters h r holding cost ($/unit/year) M r j maximum inventory of the j th retailer (unit) a j initial demand of the retailer j (units/year) O m ordering cost ($/order) n number of retailers (number) T C r total cost ($/year) d r j demand of retailer j (unit/year) D ( p , b ) demand rate varies with selling price (p) and advertisement cost (b) (units/year) Other parameters T C total cost of the supply chain ($/year) T P total profit of the supply chain ($/year) M total advertisement budget incorporated for the whole supply chain ($/year) B total advertisement cost for the supply chain ($/year) k scaling parameter x ̃ fuzzy number Δ 2 scaling factor depending the selling price of the product (constant) Δ 1 selling price scaling factor (constant) Δ ( ⋅ ) 1 minimum scaling factor of the associated cost ( ⋅ ) Δ ( ⋅ ) 2 maximum scaling factor of the associated cost ( ⋅ ) δ percentage share of advertisement by supplier ρ percentage share of advertisement by manufacturer σ percentage share of advertisement by retailer α a positive constant (constant) β positive constant (constant) v scaling factor depending upon the demand (constant) k 1 positive constant for the local advertisement of the supplier (constant) k 2 constant for manufacturer advertising (constant) k 3 positive constant for local retailer advertising (constant)
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