Product Demand Research Articles

Development Strategy for Seaweed Agroindustry on Household Scale in Mataram City

The research objectives are (1) Analyzing alternative strategies for developing agro-industry made from seaweed in Mataram City; and (2) Analyzing the strategic options that are prioritized in the development of seaweed agro-industry in Mataram City. The research method uses descriptive methods. Determining the research location was carried out using the Purposive Sampling technique. The main respondents in the research were seaweed agro-industry entrepreneurs. To strengthen data related to product quality and price instruments, data was added by interviewing buyers/consumers as additional respondents, so the assessment was more objective. Producer respondents were determined using the census method, while consumer respondents were determined using the Accidental Sampling method. The results of the research show (1) Alternative development strategies using SWOT, obtained by micro-businesses in quadrant Ⅲ (WO Strategy) include utilizing coaching for access to capital, modern technology, good packaging, expanding the market through social media, increasing the number of human resources, and promotions to overcome less strategic location. For small businesses in quadrant Ⅰ (SO Strategy) by utilizing experience and business permits to create employment opportunities, process agricultural products, promote quality products without preservatives, and use guidance from related agencies, (2) QSPM matrix analysis produces priority strategies for Micro business is expanding the market and promoting through social media to meet high product demand, while for small business it is increasing the added value of products with available raw materials and utilizing guidance from related agencies.

Activating the sustainable consumer:The role of customer involvement in corporate sustainability

AbstractTackling grand challenges and making sustainable development a reality through sustainable consumption crucially depends upon both companies’ activities as well as individuals’ consumption choices. In opinion polls, many consumers claim to favor sustainable products over conventional ones. However, a large gap remains between their stated purchasing intentions and actual decisions, posing a challenge for companies in predicting product demand and strategically managing their product portfolios. In this study, we develop a conceptual framework to demonstrate how companies can encourage sustainable consumption behavior among their customers by involving them in their corporate sustainability (CS) activities. We introduce psychological ownership as the underlying mechanism explaining how customer involvement in CS activities translates into changes in their consumption choices. We further argue that the link between customer involvement and psychological ownership depends on the type of a company’s CS—that is, whether CS is embedded in or peripheral to the company’s core business. The results from three experiments, including one field experiment conducted in collaboration with a fashion retailer and involving real customer purchase decisions, support our theorizing. The findings reveal the power of customer involvement as a marketing tool in promoting sustainable consumption.

The role of peatlands in carbon footprints of countries and products

Global peatland degradation is a major emission source (2 Gt CO2eq/yr), but it is often excluded from product carbon footprints or consumption-based emission inventories. The aim of this study was to analyze the impact of peatlands on the carbon footprints of countries and products. The analysis was performed by combining the updated Global Peatland Assessment emission inventory with the EXIOBASE 3 multiple-region input-output model and propagating the emissions from primary production to final demand of products. The structure of the network of production and consumption was analyzed by identifying the paths and nodes that had the highest embodied peatland emissions. Based on the analysis, the consumption-based emission inventory had hotspots in Indonesia, China, and Russia, where a few key sectors (construction, rice, and other foods) contributed to 31 % of the global peatland emissions. Unlike in many other environmental impact categories, Europe was a net exporter (104 Mt CO2eq/yr) of embodied peatland emissions and Africa & Middle East region was the largest net importer (64 Mt CO2eq/yr). The embodied peatland emissions are focused on agricultural and forestry products in the countries with the largest share of degraded peatlands. Products originating from peatlands can have multiple times higher carbon footprints than non-peatland farmed products. The societal cost of carbon (SCC) of peatland originating products can even be higher than the value of the products. Therefore including peatland emissions into product carbon footprints would create incentives for mitigating emissions. Local emission mitigation will require tailored combinations of rewetting, restoration, and paludiculture. As the current uses of peatlands are emission intensive and have low value added, the potential for cost-effective emission reduction is considerable.

Decentralised protocols for autonomous mobile robots in material handling: inductive learnings from centralised controller

As customer demands for personalised items continue to grow, flexible material handling systems are becoming indispensable in manufacturing to accommodate the increasing product variety and demand fluctuations. Consequently, the adoption of autonomous mobile robots (AMRs) is on the rise, driven by their capability to autonomously respond to changes in demand. However, the myopic nature of AMRs’ autonomous decision-making may limit their ability to consider a system-wide perspective. To address this challenge, we propose an AI-based platform that extracts knowledge from a centralised agent, which makes system-wide decisions offline, and integrates this knowledge into decentralised AMRs operating online. We develop a mixed-integer linear programming model, a genetic algorithm, and a scheduling algorithm to generate centralised solutions. Additionally, we design features to convert these solutions into training instances and employ artificial neural networks to derive knowledge from them. This knowledge is then integrated into each AMR as a protocol. Through experiments, we evaluate the performance of the developed protocol, demonstrating its superiority over other dispatching rules in minimising the total weighted tardiness of transportation requests and the total waiting time of transportation requests at storages.

Stem recovery and harvesting productivity of two different harvesting systems in final felling of Pinus patula

ABSTRACT With the current demand of wood products and industrial requirements, plantation owners need to re-think ways in which harvesting is carried out and identify the most efficient harvesting system that also utilizes small diameter parts of stem per industry’s product specifications. This paper contributes to the discussion by comparing the harvesting productivity and stem recovery between conventional harvesting (CH) and integrated harvesting (IH) systems. CH is harvesting which does not necessarily align with the log dimensional requirements of the customer and only recovers logs with big diameters while IH is based on harvesting and sorting logs according to need in a single operation so that as much volume and value as possible of the stands is realized. The study was carried out at the Sao Hill Forest Plantation in the Southern Highlands of Tanzania. The harvested compartment was purposively selected and divided into IH and CH plots. A total of 39 trees were randomly selected to be harvested from each plot. The findings portray a significant difference in the stem recovery of 75% for IH and 60% for CH. CH had an average cutting productivity of 16 m3/h as compared to 14 m3/h for IH while skidding productivity was 15.3 m3/h and 24.9 m3/h for IH and CH, respectively. In this study, the IH system resulted in higher production in terms of volume recovered, meaning IH had a significant impact on the production and stem recovery of the standard stem-wood for use in different industries.

Analisis Peramalan Permintaan dalam Memaksimalkan Manajemen Rantai Pasok Menggunakan Metode Moving Average

Forecasting demand for screen-printed clothing products at the UMKM "D'mitz Screen Printing" in Sobrah Village, Wungu District, Madiun Regency helps with production control planning to maximize supply chain management for screen-printed clothing products. To predict future product demand, it is very important for UMKM to forecast market demand. Forecasting future demand is very important to avoid sales prediction errors that can cause waste, such as increased production costs due to sales predictions being too large, or stock outs due to sales predictions being too small, which results in customers having to wait longer to get the goods they want. Based on this problem, the UMKM "D'mitz Screen Printing" carried out a demand forecasting analysis for screen printed clothing with the aim of reducing waste and maximizing value. Forecasting demand for screen printed clothing for the next five months using time series analysis and moving average methods. Forecasting results for the period March 2022 to February 2023 show sequential forecasting values of 3266.67; 3300; 3250; 3283.33; 3233.33; 3316.67; 3333.33; 3372.22; 3305.56; and 3272.22. From the Mean Absolute Error (MAE) and Mean Square Error (MSE) calculations that have been carried out, the MAE value is 94.44 and the MSE value is 16018.593.

Inventory Prediction Using a Modified Multi-Dimensional Collaborative Wrapped Bi-Directional Long Short-Term Memory Model

Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22.

Allocating Inventory Risk in Retail Supply Chains: Risk Aversion, Information Asymmetry, and Outside Opportunity

Problem definition: Recent global crises have caused unprecedented economic uncertainty and intensified retailers’ concerns over inventory risks. Mitigating inventory risks and incentivizing retailer orders is critical to managing retail supply chains and restoring their norms after severe impacts. We study the allocation of inventory risk using contracts in a retail supply chain with a risk-neutral manufacturer and a risk-averse retailer. We consider two factors that affect the effectiveness of contracting: (1) asymmetric risk aversion information—retailers’ attitudes are typically diverse and unknown to the manufacturer, and (2) uncertain outside opportunity—retailers typically face a volatile external business environment. Methodology/results: With a game-theoretic model that captures the interaction among risk aversion, information asymmetry, and outside opportunity, we derive the contracting equilibrium under two widely adopted risk allocation schemes—push (i.e., the retailer bears the inventory risk) and pull (i.e., the manufacturer bears the inventory risk) contracts. Contrary to the conventional wisdom that pull contracts are more effective in risk mitigation, we show that push contracts may induce larger expected order quantities and achieve the highest supply chain efficiency due to the interaction of asymmetric risk aversion information and risky outside opportunities. We also find that the manufacturer may obtain higher profits with push contracts when both the heterogeneity in the retailer’s risk attitude and the risk of the outside opportunity are sufficiently high. In addition, when the risk of the outside opportunity is in a medium range, the push contract allows the manufacturer to fully eliminate the information rent and achieve the supply chain’s first-best outcomes. We further evaluate the effects of product profitability and demand uncertainty and generalize the retailer’s risk measure to any coherent risk measure. Managerial implications: Our analysis highlights the importance of modeling asymmetric risk aversion information and risky outside opportunities in analyzing supply chain contracting. When considering these practical factors, allocating more inventory risks to a risk-averse retailer may be better than a risk-neutral manufacturer. Our results provide novel insights into the selection of proper contract types for managing inventory risks in retail supply chains. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0624 .

The Role of Indian Rural Industries led by Coir in Building Value and their Performance - An Analysis

India is an agricultural country. One third of population depends on the agricultural agro based industry sector directly or indirectly. Agriculture plays a vital role in the manufacture sector, agricultural products demand linking with manufacturing sector. Indian Agricultural product are exporting to other countries like USA, Australia, Gulf countries etc., One among the exporting Agricultural Product is Coconut and its by products. The coconut industry occupies an extremely important position in the economy of India. Among the extraordinary number of products derived from the coconut palm, copra, coconut oil, fresh coconuts, desiccated coconut, coir and shell products have high demand in local and International markets. India is one of the top producers and exporters of coir and coir products in international market. This article analysis export performance of coir industry in India from 2017-18 to 2021-2022.

The Impact of Forest Products Demand on Deforestation in Rwanda: A Case Study of Burera, Gakenke, Gicumbi, Musanze, and Rulindo Districts

The world’s forests play an integral role in not only the well-being of their inhabitants but also in the overall health of other lifeforms on the planet. Some of the major benefits of forests include climate stability, ecological benefits, biodiversity and economic importance. Deforestation is one of the major challenges to gain the above benefits in the long run. It involves the permanent end of forest cover to make that land available for residential, commercial or industrial purposes. The purpose of this study was to assess the impact of forest products demand on deforestation in Rwanda. A case study of Burera, Gakenke, Gicumbi, Musanze, and Rulindo Districts. The study adopted a cross-sectional survey research design as its framework to guide the process of data collection. The target population was households living closer to the forest products demand. The study adopted a purposive sampling approach to select five districts. Twelve (20) households were randomly selected from each sector making a total sample size of 120 respondents. Descriptive analysis was done using SPSS version 20, and regression using STATA version 12. The results of the study indicated that the forest products used by the households were firewood, stakes, timbres, charcoal, building scaffolds, fodder, poles, and medicine. The results of the logit model showed that land size, family income, household size, occupation, education level, and fodder were statistically significant and positively influenced forest product demand in the study area at (p< 0.01). The result of the regression analysis also revealed that eight variables out of nine were positively significant and influenced deforestation in the study area. Agricultural activities, urbanization, forest fires, and overpopulation were statistically significant at the P ≤ 0.01 level. Despite the roles provided by forests all over the world, the results of this study showed deforestation effectively causes climate change, wildlife extinction & habitat loss, decline in the life quality of people (100%). After these results of the study, the best solution to overcome deforestation is to curb the felling of trees by enforcing a series of rules and laws to govern it. Supporting research, extension and training for more sustainable grazing systems, including silvopastoral techniques that can simultaneously increase livestock production and protect the soil against nutrient depletion, compaction and erosion.

Collaborative dynamic scheduling in a self-organizing manufacturing system using multi-agent reinforcement learning

Personalized product demands have made the production mode of many varieties and small batches mainstream. Self-organizing manufacturing systems represented by multi-agent-based manufacturing systems (MAMS) are pursued by manufacturing enterprises to support collaborative dynamic scheduling among various pieces of manufacturing equipment. In contrast to traditional scheduling methods including meta-heuristic algorithms and dispatching rules, reinforcement learning (RL), whether in the form of single-agent RL (SARL) or multi-agent RL (MARL), combines high response and robust performance in dynamic environments. However, the SARL-based methods suffer from the curse of dimensionality in the action space, while current MARL-based methods fail to achieve the optimization of collaborative scheduling decisions among heterogeneous manufacturing equipment. These factors directly hinder the effective resolution of dynamic scheduling in the MAMS. Therefore, we present a novel MARL-based approach that solves the dynamic flexible job-shop scheduling problem in the MAMS (DFJSP-MAMS), with the objective of minimizing the mean tardiness. Here, various items of heterogeneous manufacturing equipment in the MAMS, including the warehouse, machines, buffers, and vehicles, are encapsulated as agents. Moreover, a partially observable Markov game is constructed to specify the collaborative scheduling process among these agents and transfer the DFJSP-MAMS into a MARL task. Furthermore, an action space consisting of several weight variables is designed for each agent to offer the weight selection of dispatching rules. Subsequently, a policy network is built for each agent to determine the weights and scheduling decisions. Finally, the multi-agent deep deterministic policy gradient algorithm is employed to train these agents for the optimization of collaborative scheduling decisions among various pieces of heterogeneous equipment. Experiment results demonstrate that the proposed approach outperforms single dispatching rules, the SARL-based method, and other MARL-based methods under 36 distinct manufacturing environment conditions.

THE EFFECT OF COVID-19 ON THE VALUE CHAIN OF PADDY AND CARROT VARIETY OF IARI

The arrival of COVID-19 was no less than any apocalypse for the global economy. The growth of global GDP spiralled down to a very low point. The condition in India was no exception to it. Almost all the sectors of the economy were severely affected. Notwithstanding this, agriculture emerged as a bright spot in the dark horizon. Even though the growth rate was positive, there were serious impediments in the commodity value chain and agricultural marketing. Paucity in input supply, restriction in movement, closure of market, reduced demand of food products brought challenges that existed never before. In this backdrop, a study was conducted to find out the effects of COVID-19 on the value chain and marketing of Paddy Commercial variety Pusa Basmati 1121 and Carrot Commercial variety Pusa Rudhira. The research design was ex-post facto in nature and a total number of 160 farmers and 50 stakeholders including exporters, commission agents, wholesalers, and millers were consulted for the study. The locale of the study was Soodhna (U.P), Sangohi (Haryana), Khwajpur (U.P) and Nizampur (New Delhi). From extant review of literature, pilot study and discussion with experts, several constraints faced by the value chain stakeholders were identified and rank ordered using Rank Based Quotient Method (R.B.Q). Results showed that in case of farmers "difficulty in getting buyer, price fluctuation and wastage" were among the top constraints, and for stakeholders such as millers, wholesalers and retailers, "unavailability of labour and price fluctuations" were the causes of concern. Moreover, for exporters "hike in freight charges" was the most severe constraint. The findings of the study could highlight the vulnerable areas in the commodity value chain which ought to get due attention in policy making and better preparedness for future events of similar nature.

The Impact of CSR Implement Modes on Sustainable Supply Chain Pricing and Green Decision Making

Green transformation is an effective way for supply chains to achieve sustainable development. In the heterogeneous substitute product coexistence environment, it is valuable to explore the motivation behind CSR implementation as well as the advantages and differences associated with different members’ CSR implementations. In this study, we consider that the manufacturer produces both green products and ordinary products that are substitutes for each other and construct a CSR-absence scenario (Model N), a retailer implementing CSR scenario (Model R), and a manufacturer implementing CSR scenario (Model M). Applying a game-theory-based method, we obtain the optimal pricing, green decision, and supply chain profit under different scenarios. We analyze the effects of consumers’ green preference, green product function coefficient, and CSR level on decision making and performance. Through comparison and numerical analysis, we find the following: (1) Although the CSR behaviors of the manufacturer and the retailer squeeze the market demand space of the ordinary products, the greenness of the green product increases, and the retail price decreases. This not only enhances the consumer surplus of green products but also increases the profit and social welfare of the supply chain system. (2) The eco-friendliness of green products, product demand, and social welfare levels are the highest when the retailer implements CSR, followed by when the manufacturer implements CSR, and are the lowest in the CSR-absence scenario. In addition, the following retailer can enhance both its and the manufacturer’s profits through implementing CSR within a reasonable range. (3) The numerical analysis results show that, when the functional attributes of the green products meet a certain level, supply chain members implementing CSR does not affect consumers’ choice of green products.

The principle of cascadic prosilience—Assessment of territorial recovery strategies after disrupted regional infrastructure systems in Iraq

This paper presents an assessment approach to fostering socioeconomic re-development and resilience in Iraqi regions emerging from the destruction and instability, in the aftermath of the war conflict in Iraq. Focusing on the intricate interplay of logistics infrastructure and economic recovery, the present study proposes a novel framework that integrates general resilience insights, data analytics, infrastructure systems, and decision support from Data Envelopment Analysis (DEA). We draw inspiration also from historical cases on “creative destruction” or “Blessing in Disguise” (BiD) phenomena, like the post-WWII reconstruction of Rotterdam, so as to develop the notion of stepwise or cascadic prosilience, analyzing how innovative logistics systems may in various stages contribute to economic rejuvenation. Our approach recognizes the multifaceted nature of regional resilience capacity, encompassing both static (conserving resources, rerouting, etc.) and dynamic (accelerating recovery through innovative strategies) dimensions. The logistics aspect spans both the supply side (new infrastructure, ICT facilities) and the demand side (changing transportation flows and product demands), culminating in an integrated perspective for sustainable growth of Iraqi regions. In our study, we explore several forward-looking strategic future options (scenarios) for recovery and reconstruction policy factors in the context of regional development in Iraq, regarding them as crucial strategic elements for effective post-conflict rebuilding and regeneration. Given that such assets and infrastructures typically extend beyond a single city or area, their geographic scope is broader, calling for a multi-region approach. By leveraging the extended DEA approach by an incorporation of a super-efficiency (SE) DEA approach so as to better discriminate among efficient Decision-Making Units (DMUs)—in this case, regions in Iraq—our research aims to present actionable and effective insights for infrastructure investment strategies at regional-governorate scale in Iraq, that optimize efficiency, sustainability and resilience. This approach may ultimately foster prosperous and stable post-conflict regional economies that display—by means of a cascadic change—a new balanced prosilient future.

Research on Product Demand Forecasting and Personalized Recommendation Based on Data Analysis

This paper aims to explore the design and implementation of product demand forecasting and personalized recommendation system based on data analysis. Through the collection and analysis of a large number of user behavior data, an accurate demand prediction model is constructed, and based on this, a personalized recommendation algorithm is implemented. The paper first introduces the research background and purpose, and then elaborates the research method, process and results. The method based on data analysis can effectively predict the product demand and provide users with personalized recommendation services, so as to improve user satisfaction and market competitiveness of enterprises.

Ohmic baking of gluten free sponge cake: Analysis of technological and quality characteristics

The demand of gluten-free bakery products is worldwide increasing. Simultaneously, ohmic heating is an alternative heating technology that still deserves further research and development. Therefore, the aim of this work was assessing different technological and quality characteristics of gluten free sponge cake baked by ohmic heating. A custom ohmic system was designed and built and different electric field strengths (1500, 2000 and 2500 V/m) were tested. Conventional baking was performed for comparison purposes. The conventional baking time was 24 min, whilst the ohmic heating time ranged from 3.51 min at 2500 V/m to 7.93 min at 1500 V/m. The ohmic system achieved significantly higher energy efficiencies, reaching nearly 60%, compared to only 4.1% for conventional baking. Additionally, specific energy consumption was significantly lower for ohmic heating. Weight loss was higher for ohmically processed samples, showing larger number of pores in their inner spongy structure. Ohmic heating resulted in a larger volume expansion and a cake with lower density. The remaining quality characteristics, such as color, hardness, cohesiveness, and elasticity, were not significantly different. Results demonstrated that ohmic heating is a suitable technology for baking gluten-free sponge cakes and that it is a promising technology for fast industrial baking of such products.

Demand bidding vs. demand response for industrial electrical loads

We investigate a novel optimal demand bidding model for industrial loads based on an extended optimal power flow problem for the day-ahead market. The objective function accounts for generation costs and revenue resulting from the sale of products made by the bidder using electricity. As a bidding entity, the industrial load participates in electricity price formation, and is described using a model whose parameters can be determined mostly from public information. Constraints specific to industrial loads, such as satisfying product demand, are also included. The model is validated using simulations of a modified IEEE 24-bus reliability test case with a chlor-alkali plant as the load entity. We find that the proposed participation model, which is simple enough to include in grid dispatch software, performs well relative to other demand-side management strategies such as price-based demand response and cooperative scheduling of industrial load by the grid operator.

Predictive Analytics on Shopee for Optimizing Product Demand Prediction through K-Means Clustering and KNN Algorithm Fusion

Predictive Analytics on Shopee for Optimizing Product Demand Prediction through K-Means Clustering and KNN Algorithm Fusion

Nexus between regulatory pressure, eco-friendly product demand and sustainable competitive advantage of manufacturing small and medium-sized enterprises: the mediating role of eco-innovation

AbstractAlthough previous research endeavours have extensively explored the environmental pressure-performance relationship, their findings have been inclusive. Therefore, this paper examines the direct and indirect (through the mediating role of eco-innovation) impact of regulatory pressure and eco-friendly product demand on sustainable competitive advantage. The research model was examined using Structural Equation Modeling methodology, analyzing data gathered from 183 senior managers within Egyptian SMEs. Several findings have been yielded, which are: (1) eco-innovation is positively motivated by regulatory pressure and eco-friendly product demand; (2) the direct link between environmental pressures (regulatory pressure, eco-friendly product demand) and sustainable competitive advantages is not statistically significant; and (3) eco-innovation acts as a mediator in the relationship between these environmental pressures and sustainable competitive advantage. This research theoretically contributes to the institutional theory of the firm and competitive advantages theory. Particularly, the findings of this research theoretically emphasize that eco-innovation plays a prominent role in alleviating and translating environmental pressures exerted on manufacturing SMEs into sustainable competitive advantages. Furthermore, the research provides practical implications, policy recommendations, limitations, and further research avenues.

A simulation-optimization approach for economic assessment of an imperfect serial production management under uncertainty

ABSTRACT One of the critical quandaries in real-world production management systems is uncertainty in the product demand, which is closely associated to the product price. This uncertain behavior of demand affects production decisions in a system. In this context, this study develops a decision support framework for an imperfect serial production management system under uncertainty. The proposed framework is analyzed considering price-dependent uncertain demand and random defective rate in the system, thus considering both internal and external uncertainties of the system. Three distinct production models are formulated considering different types of uncertain relations among the product demand and market price. Closed form solutions of the decisions are obtained through hybrid of simulation and analytical optimization and are tested through an experimental case analysis. @RISK of Palisade Suite is utilized for simulation and to capture the uncertainty in defective proportion and demand elasticity parameters by creating several possibilities.