Contribution of robust optimization on handling agricultural processed products supply chain problem during Covid-19 pandemic

This research aims to show how decision sciences can make a significant contribution on handling the supply chain problem during Covid-19 Pandemic. The paper discusses how robust optimization handles uncertain demand in agricultural processed products supply chain problems within two scenarios during the pandemic situation, i.e., the large-scale social distancing and partial social distancing. The study assumes that demand and production capacity are uncertain during a pandemic situation. Robust counterpart methodology is employed to obtain the robust optimal solution. To this end, the uncertain data is assumed to lie within a polyhedral uncertainty set. The result shows that the robust counterpart model is a computationally tractable through linear programming problem. Numerical experiment is presented for the Bandung area with a case on sugar and cooking oil that is the most influential agricultural processed products besides the main staple food of the Indonesian people, rice.

Supply Chain Replenishment Decision for Newsvendor Products with Multiple Periods and a Short Life Cycle

Traditionally, the newsvendor problem is a single-period model for a retailer and can be applied in the replenishment decision for a product with a short life cycle. However, many fashionable commodities are seasonal; not all of these products must be sold within a single period of a selling season, and they can be replenished once in each cycle. This study develops a novel multi-period model to determine multiple ordering replenishment decisions for a product over a short selling season. This study not only demonstrates the profit function for a retailer, but also provides those for both the manufacturer and the entire channel in a supply chain problem. The proposed multi-period ordering model provides explicit insights into how the ordering decisions of the retailer are affected in a specific period by considering unsold inventory or unsatisfied demand from a previous period. A numerical analysis and the simulation results illustrate the feasibility of the proposed model.

SUPPLY CHAIN ANALYSIS OF SOYBEAN RAW MATERIALS ON TOFU BUSINESS GROUP AT MEROBOK CENTRAL LOMBOK

This research is motivated by the problem of raw material shortages that experienced by Tofu business group at Merobok. The research aims are to find out the mapping and to identifiy supply chain problem of soybean raw materials on Tofu business group at Merobok Central Lombok. Type of this research is descriptive study with qualitative approach. Data collecting technique was used in-depth interview, direct observation, and documentation. Then performed qualitative analysis by using Triangulation procedure. As for the results of this research concluded that Tofu business group apply Few Supplier strategy that is choose three supplier consisting of Ingin Maju farmers (ten persons of farmers), H. Abidi as a merchant, and Mr. Sahlan as a collector at Gemel village. However, Tofu business group more intensive to purchase raw materials from Mr. Sahlan (collector). Tofu business group including of weak buyers of soybean raw materials because unable to dominate supplier and Tofu business group has less purchasing power than other buyers and unable to search other suppliers because they have less available capital Keywords:Supply Chain, Tofu Business Group, Soybean Raw Materials, Mapping, and Supplier

A multi-objective optimization of closed-loop supply chain problem with vehicle routing

Product recovery has become significant business strategies to increase a competitive edge in business and also in the society. Parts from discarded products due to rapid advancement and post-consumer products before & after end-of-life (EOL) are recovered to reduce landfill waste and to have become a part of circular economy. Product recovery is made possible with the help of Closed-loop supply chain (CLSC). This paper concentrates on multi-period, multi-product, and multi-echelon Closed Loop Green Supply Chain (CLGSC) network. A bi-objective (cost and emission) Mixed Integer Linear Programming (MILP) model has been formulated for the network and has been optimized using Goal Programming approach and Genetic Algorithm. Results are discussed for providing some managerial insights of the model.

Protecting Brand Value through Anticounterfeiting Supply Chain Strategies

Counterfeiting is a problem many companies do not want to acknowledge or talk about. However, ignoring the problem is likely to have the effect of encouraging counterfeiters to go unchecked. A multilayered strategy that adopts several approaches and engages the entire organization is needed to address the counterfeiting problem. This chapter provides a set of recommendations to address the issue of counterfeiting. Initially, the size of the problem must be estimated and the return on investment approximated. This can help define the need for a team of experts to work in this area, leading to a set of performance metrics that are aligned with business objectives and outcomes. Next, the key focal product segments should be targeted and a system for identifying products through product trademark registration with customs authorities should be completed. In the end, combating counterfeiting is not a supply chain problem, it is not a legal problem, nor is it a packaging and covert marking problem. It is a global problem—one that impacts all organizations, large and small. All business functions need to be part of the discussion, not just a single brand security function. Failure to approach counterfeiting in this manner will simply allow the problem to continue to grow.