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The development of technologies such as the Internet of Things has transformed traditional physical products into smart connected products (SCPs) that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. SCPs raise a new set of strategic choices for creating value and pricing products, how relationships with business partners such as channels are redefined, and what role companies should play as industry boundaries are expanded. This study develops an inventory model that considers optimal pricing, production, and intelligent policies for SCPs. In this model, customer demand is assumed to increase as the selling price decreases and the effort to improve product intelligence (i.e., intelligent effort) increases. In addition, a two-level trade credit is included in the SCPs supply chain channel. The manufacturer often receives a permissible delay-in-payment (trade credit) from the supplier while also offering a delayed payment to end customers to attract more sales. Trade credit is particularly important for SCPs as it can act as a payment plan to reduce the product’s price barrier. This study aims to determine the optimal selling price, lot size, and level of intelligent effort while maximizing the manufacturer’s profit under a two-level trade credit. The optimal solution is clarified, numerical examples are provided, and a sensitivity analysis is performed to illustrate the theoretical results and solution approach. The results reveal that considering the level of intelligent effort as a decision can benefit the manufacturer. Notably, as the intelligent effort coefficient increases by 55%, the total profit increases by 65.8%.

Blockchain and smart contract technology is advancing rapidly and, as many companies and industries are stepping up to adopt the technology, it is a worthy subject of research. This study presents ...

In practice, a vendor may offer a retailer an advance-cash-credit (ACC) payment scheme. This implies the buyer needs to pay a fraction of the purchasing cost before he receives the item (advance payment), some cash when the item is arrived (cash on delivery), and then the remaining purchasing cost after a credit period (credit payment). This paper studies the effect of payment schemes on the supply chain network design. The network consists of an outside supplier, multiple distribution centers (DCs), and multiple retailers. The DCs receive an upstream ACC payment from the supplier. This study attempts to determine the optimal location, allocation, as well as the inventory cycle time while minimizing total cost. A continuous approximation is used in the model development and the time value of money concept is considered, since different payment schemes related to time change the cash value. Numerical studies are adopted to demonstrate the solution approach and the effects of changing parameters on decisions and total cost. This study provides novel management implications for supply chain network designers’ reference. The results show that the number of DCs to be opened, the joint replenishment cycle time of DC, and the total cost increase as the interest earned decreases and the interest charged increases.

We explore the supply chain network design problem for a retailer selling perishable products when the supplier provides trade credit and the deterioration rate can be reduced by freshness-keeping ...

This study examines the effects of dynamic discounting based credit payment on a supply chain network design problem. Dynamic discounting based credit payment is a supply chain finance policy wherein the supplier provides a credit period to a distribution center (DC) with a discount applied if the DC pays the supplier before the end of the credit period. This study also considers the time value of money and applies discounted cash flows to formulate a model that determines the DC’s optimal replenishment cycle, selling price, and influence area while maximizing the present value of the total profit. The continuous approximation approach is applied to formulate a mathematical model of the problems, and an algorithm based on non-linear optimization is established to solve the problem. A numerical example and a sensitivity analysis are provided to present the proposed model and solution approach and to illustrate the effect of each cost on the decisions and profit.

Given the diversity of styles and sizes in apparel, marker planning which aims to arrange and move all the pattern parts of garments onto a long thin paper before the cutting process is a very important process for the apparel industry. In order to decrease the wastage of fabric after the cutting process, the marker layout essentially needs to be as compact as possible. In this paper, hybrid heuristics are proposed to conduct and achieve an optimized marker layout and length. First, a moving heuristic is presented as a new packing method to arrange and move the patterns without overlapping; here, an initial marker is presented to calculate the length. This heuristic considers multiple rotated angles and flipping positions of the patterns in order to obtain more diverse arrangements. With different arrangements, there is a higher chance of achieving an optimized marker layout and length. To further improve the solution received from the moving heuristic, soft computing algorithms are taken into account, including the genetic algorithm (GA), simulated annealing (SA), and hybrid genetic algorithm-simulated annealing (HGASA) to achieve a shorter length in the marker layout. The best marker length can be obtained from HGASA, which can save almost 28% of the length. Special cases with specific combinations in rotated angles are considered so that the industry can make informed choices on the algorithms suitable to address the marker planning problem.

This research develops two models to consider retailer sales promotion and manufacturer trade promotion under demand uncertainty. The objective of the first model is to determine the retailer’s optimal promotional effort and order quantity while maximizing the retailer’s profit under exogenous trade promotion. The second model extends the first to consider the manufacturer’s endogenous trade promotion decisions. For these models, three different trade promotion policies (off-invoice, scan-back, unsold-discount) have been compared to identify the policy that can increase the manufacturer’s and the retailer’s profits. For the model with exogenous trade promotion, the retailer’s promotional effort level, order quantity, and profit are highest under the off-invoice trade promotion policy. With respect to the manufacturer’s endogenous trade promotion decisions, the retailer’s promotional effort level, order quantity, and profit, and the manufacturer’s profit are higher under the off-invoice policy than under the scan-backs policy. When comparing the three different trade promotion policies, we also find that the wholesale price is a key factor that influences a manufacturer’s profit. Our research sheds light on the importance of trade promotion policy in supply chain management.

Purpose Vaccination is widely conceded as a pivotal measure to achieve community immunization and eradicate COVID-19. The purpose of this paper is to understand vaccination behavior based on the association between the theory of planned behavior (TPB) and health belief model (HBM). Design/methodology/approach A self-administered questionnaire was designed to collect data from 438 participants through social media in Vietnam and later analyzed using SPSS and AMOS to examine the research model. Findings The results indicated that attitude toward vaccination is positively influenced by perceived risk and perceived benefits. Additionally, background constructs of TPB (i.e. social norms, attitude and perceived behavioral control) and HBM (i.e. perceived risk, response efficacy, perceived benefits and knowledge about COVID-19) are underlying motivations for individual intention to receive vaccine. Moreover, age is a demographic predictor of vaccination intention. Finally, vaccination intention facilitates COVID-19 vaccination. Practical implications The findings will assist health-care bodies and authorities to understand public perceptions, attitude and behaviors and encourage their participation in vaccination campaigns. Originality/value To date, this is the first investigation of how disease knowledge and perceived benefits drive vaccination intention in Vietnam. Moreover, this study may be initially successful in adding current health-care behavior literature by combining TPB with HBM. With most studies on vaccination intention at the early stage of COVID-19 outbreak in developed nations, this work provides new insights by testing vaccination behavior in the fourth wave of COVID-19 outbreak and in a developing nation.

With the rising sense of environmental consciousness, the development of renewable energy and rapid technological innovation have become drivers, as well as posed challenges, for the power supply chain network. This study addresses the smart power supply chain network design problem considering two players: the electric power company and the users. Using distributed generations (DGs) such as solar, wind, and biomass, among others, users can generate their own renewable energy. Here, differential pricing and buy-back policies maximize benefits for both the companies and users. Under the buy-back contract, users who own DGs can generate renewable electricity, determine the electricity they need, and buy from or sell their share to the electric company. The continuous approximation approach is used to model resolutions for smart power supply chain network problems. Algorithms based on non-linear optimization are proposed to solve the smart power supply chain network design problems for two cases: centralized and decentralized models. Finally, a numerical analysis illustrates the solution procedures and examines the effects of dynamic parameters on decision-making. The results show that the centralized model obtains a higher profit than the decentralized model. Further, the results of the numerical analysis can serve as references for business managers or administrators.

Research has been conducted on approaches using social media information to improve demand forecasting accuracy in business-to-customer industries. However, such social media information is not applicable to business-to-business (B2B) industries, as a result of a lack of end-consumer evaluations. This raises a few interesting questions, including whether there may be any external information that could be used to improve B2B demand forecasting, and whether practical approaches may be possible to collect and utilize useful external business information. In this study, we develop an innovative and intelligent demand forecasting approach and apply it to a B2B server company based in the United States. We first implemented time series and machine learning models based on sales data and selected the best-fitting model as a baseline, and then used a web crawler and Google Trends to collect related market signals as external information indices for the server industry, which were finally incorporated into the selected baseline model to adjust forecasting results to account for demand fluctuations. Experimental results demonstrate that the baseline model achieved an out‐of‐sample mean squared error (MSE) of 19.77 without considering the collected external information indices, and 11.87 when external information was incorporated. Therefore, our proposed approach significantly improved forecasting accuracy, demonstrating an improvement of 63.1% in terms of MSE, 44.1% in terms of mean absolute error, and 61.2% in terms of root mean square percentage error. Thus, this study sheds light on the value of external information in demand forecasting for B2B industries.

In the modern global economy, trade credit financing is typical in business transactions for both sellers and buyers. The seller offers a credit period to attract new buyers or stimulate demand, and the buyer takes the opportunity to accumulate revenue. To obtain this benefit, the seller prefers trade credit policies that are dependent on the quantity ordered, referred to as order-linked trade credit. The buyer can obtain the benefits from a fully delayed payment if their order is sufficiently large. Similarly, the seller can sell many products while granting a credit period. Otherwise, the buyer receives only partial trade credit, and the seller can take the opportunity of both cash and credit payments. In this study, an economic order quantity (EOQ) inventory model for deteriorating products, under default risk control-based trade credit, is formulated using a discounted cash flow approach. The seller offers to the buyer order-linked trade credit with price-and credit-period-dependent demand. The optimal selling price, credit period policies, and replenishment cycle time are determined simultaneously, while maximizing the present value of the seller's total profit. Moreover, this research provides numerical examples and sensitivity analysis to illustrate the theoretical results, solution procedure, and gain managerial insights. 200 words.

The costs of renewable energy technologies are highly sensitive to financing terms. Power companies are willing to invest in renewable energy generation because of clean and sustainability concerns. However, renewable energy generation poses a potentially high risk, both in generation and consumption. In this study, we consider how a power company makes its decisions if a credit bank willing to lend to it for renewable energy generation. The repayment ability of the company depends on sales revenue under demand uncertainty. We formulate a mathematical model to help the company determine the optimal electricity price, renewable energy capacity, and amount of loan while maximizing the total expected profit. A searching algorithm is proposed to solve the problem. Several numerical examples are provided to illustrate the proposed model and solution approach. Managerially, our study suggests that increasing renewable energy investments through financing institutions requires careful attention to pricing, capacity, and financing policies.

Cut order planning (COP) is important role in managing the costs of fabric and setup for starting a new section in the manufacturing process. An efficient COP solution can significantly reduce the total cost of manufacturing in the apparel industry. With the development of artificial intelligence (AI) techniques for optimization problems in industrial applications, two heuristics are combined in this study: simulated annealing-based genetic algorithm (SA-GA) and tabu-search-based genetic algorithm (TS-GA) algorithms to solve the COP problem. Subsequently, we compare three metaheuristics: simulated annealing, genetic algorithm, and tabu search. The COP problem is formulated as a mixed-integer programming model. The objective is to determine the length of used fabrics and the number of opened sections while minimizing the total cost. The optimal parameters of each technique are determined using the design of experiment approach. An ANOVA test was performed to compare the performances of all presented algorithms. Results show that these AI techniques are applicable to the apparel industry and can reduce the total cost. The results obtained by our proposed methods are close to the optimal solution, in which the standard deviation of the related cost is less than 10%. The results of these algorithms are applicable to large-size problems.