Inventory Practices Research Articles

Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment

This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.

Development and implementation of autoverification standardization in the adult and pediatric emergency department.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. This project utilized the guidance of the American Society of Health-System Pharmacists (ASHP) autoverification toolkit to refine our health system's approach to autoverification and contribute to the literature regarding appropriate use of autoverification technology in a pediatric and adult emergency department (ED). This single-center quality improvement study was conducted in an academic medical center ED that has 33 pediatric beds and 77 adult beds. A team consisting of clinical pharmacy specialists in emergency medicine, medication safety and informatics personnel, operational managers, and pharmacy leadership was identified to develop and implement autoverification best practices in the ED utilizing practices outlined within the ASHP autoverification toolkit. Before implementation of best practices, defined as the "preoptimization" state, autoverification took place for most medications available in the automated dispensing cabinets (ADCs). By anchoring the autoverification rule on ADC inventory, it was challenging to optimize both inventory practices and autoverification best practices. This project focused on redesigning the autoverification rules in the electronic health record, defined as the "postoptimization" state. In the postoptimization state, autoverification in the ED was updated to better align with regulatory standards. Autoverification metrics and the percentage of orders that autoverified vs required pharmacist verification were analyzed in the preoptimization and postoptimization states. This project utilized the guidance from ASHP's autoverification toolkit to refine our health system's approach to autoverification. High-alert medications (eg, insulin, extended-release opioids, digoxin) were taken off autoverification following implementation. Optimization of autoverification rules allows more orders for high-alert medications to be reviewed by a pharmacist.

Inventory Accounting Practices and Their Role in Financial Performance Evaluation

Inventory is a crucial asset for businesses, directly affecting their financial performance. However, despite its significance, many businesses struggle to implement inventory accounting practices in line with established standards. This study addresses the knowledge gap concerning the application of inventory accounting according to PSAK 14, focusing on Central Development Stores. The research employs a descriptive qualitative approach, gathering both primary and secondary data through interviews, documentation, and literature study. The findings reveal that the store employs a periodic physical inventory system and utilizes the FIFO (First In First Out) method for inventory valuation. The analysis indicates a partial compliance with PSAK 14, with a checklist percentage of 60%, highlighting areas for improvement. A notable discovery is that the store has not yet prepared financial statements, which could significantly impact its financial management practices. The novelty of this research lies in its practical evaluation of the implementation of PSAK 14 within a real business context, offering insights into common challenges. Implications suggest the necessity for the store to enhance its accounting practices to fully comply with PSAK 14, thus improving financial transparency and performance evaluation. Highlights: Partial Compliance: The store's accounting meets 60% of PSAK 14 requirements. Inventory Method: Uses Periodic system and FIFO for inventory valuation. Financial Reporting Gap: The store has not prepared financial statements yet. Keywords: Inventory, PSAK 14, Financial Performance, Inventory Valuation, Accounting Practices

Innovative Solutions for High School Inventory Management: An Information System Approach

This paper explores the development and implementation of an innovative information system designed to enhance inventory management in high schools. Traditional inventory methods, often reliant on manual tracking, are prone to errors and inefficiencies, which can hinder resource allocation and decision-making. By adopting an information system approach, schools can automate processes, improve data accuracy, and provide real-time access to inventory information. The study outlines key features such as real-time tracking, automated alerts, and role-based access controls, which are critical to the system's effectiveness. The results demonstrate significant improvements in workflow efficiency, data accuracy, and overall resource management. Despite the challenges associated with system implementation, including cost and training requirements, the benefits make this approach a compelling solution for modernizing high school inventory practices. The paper concludes with recommendations for broader adoption and further research to optimize these systems for educational settings.

The Influence of Asset Valuation, Asset Inventory, and Legal Audit on Asset Optimization

This study aims to fill a gap in the asset management literature by analysing the impact of asset inventory, legal audits, and asset valuation practices on asset optimization. The study involved 106 employees who participated in the completion of the questionnaire. The data was analysed using multiple regressions to test the proposed hypothesis. The results showed that asset inventory has a significant influence on asset optimisation. Asset inventory aims to ensure that company assets are recorded completely and accurately. A legal audit has a significant effect on asset optimisation. The implementation of legal audits to ensure regulatory asset control can increase asset optimisation. Asset valuation has a significant effect on asset optimisation. Consistent and proper asset valuation helps determine market value or tax needs. This research makes an empirical contribution to asset management theory, showing that planning, procurement, operation, and maintenance of assets are necessary to achieve optimisation. The findings provide a foundation for companies to balance policies and best practices to sustainably increase asset value. In addition, these findings are expected to be a reference for further research in the field of asset management and provide practical solutions for other companies facing similar problems.

Optimising Inventory Management Strategies for Cost Reduction in Supply Chains: A Systematic Review

This systematic review investigates and synthesises the complexities of optimising inventory management strategies for cost reduction in supply chains. The research utilised a systematic review methodology to comprehensively analyse inventory management strategies for cost reduction in supply chains, employing key search terms across databases such as PubMed, Scopus, and IEEE Xplore. A total of 50 journals were initially identified, followed by a rigorous screening process based on pre-defined criteria, including 23 relevant published articles written in English. Examining global supply chain dynamics and sector-specific challenges in local inventory management, the review proposes three key strategies: Just-in-Time (JIT) and lean inventory practices, advanced analytics and predictive modelling, and collaborative supply chain partnerships. Drawing on empirical evidence and sector-specific examples, the review demonstrates the effectiveness of these strategies in mitigating costs and enhancing overall supply chain efficiency. The policy implications underscore aligning business practices with proposed strategies, offering policymakers insights for formulating guidelines and incentives. Contributions to knowledge include a comprehensive framework for businesses to navigate contemporary supply chain challenges. At the same time, suggestions for further studies advocate for deeper explorations into emerging technologies and regional variations to refine and expand inventory management strategies.

Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores

In today's competitive grocery retail environment, effective inventory management coupled with precise sales prediction is pivotal for success. This research undertakes an in-depth exploration into a machine learning framework tailored for "Sales Forecasting and Inventory Optimization in Grocery Retail." Utilizing historical sales records, meteorological data, and consumer behavior metrics, the proposed model endeavors to predict nuanced seasonal demand fluctuations. The primary value proposition of this approach lies in its capacity to curtail wastage, ensuring the balance between overstocking and inventory stockouts. By recognizing and adjusting for seasonal dynamics, the system offers enhanced demand fulfillment during high-demand periods. The subsequent refinement of the replenishment cycle fosters superior operational efficacy. As an outcome, businesses witness cost reductions, augmented profit margins, and elevated consumer contentment due to consistent product availability. As the broader grocery sector undergoes transformation, embedding sophisticated machine learning strategies can be a linchpin for sustained adaptability and competitiveness. This research further underscores the role of emergent technologies, such as machine learning within the IoT spectrum, in reshaping supply chain and communication paradigms. The endeavor here is to curtail lifecycle expenses in the supply chain by refining inventory practices. The research introduces a Deep Inventory Management (DIM) technique, employing the LSTM paradigm of deep learning. Through DIM's innovative approach, time-series challenges are reimagined as supervised learning tasks, enabling efficient model training. Preliminary tests indicate DIM's prediction accuracy hovers around 80%, translating to an impressive 25% reduction in inventory expenses when juxtaposed with prevailing methods, alongside rapid anomaly detection in inventory activities

The Effect of Integrated Warehouse Operation Efficiency on Organizations Performance

In the contemporary business landscape, warehouse efficiency has emerged as a cornerstone of organizational prowess, serving both as a centre of expertise and a strategic tool for competitive advantage. A well-functioning warehouse not only facilitates swift fulfilment of customer needs but also significantly enhances overall business performance. This study seeks to investigate the impact of warehouse attributes on efficiency, focusing particularly on layout and operations. Through a comprehensive literature review, we explore the role of warehouse design and operational practices in driving efficiency gains. Our analysis reveals that the configuration of a warehouse plays a pivotal role in determining its operational effectiveness, with optimized layouts facilitating rapid response to customer demands. Furthermore, effective warehouse operations are identified as critical components in enhancing overall efficiency, working synergistically with layout design to bolster performance. Concurrently, inventory management has emerged as a paramount concern for organizations worldwide, directly influencing operational success. Efficient inventory practices not only enhance customer service but also mitigate stock-related challenges.. By synthesizing insights from both warehouse efficiency and inventory management domains, this study aims to provide a comprehensive understanding of the strategies employed by organizations to optimize operational performance and drive sustainable growth. Keywords: Seamless integration of inventory practices, Efficient warehouse operations, Technological advancements in inventory control, Inventory tracking systems, Optimization of holding costs Technology-driven warehouse management

Impact of inventory management on SME performance: a systematic review

PurposeEffective inventory management is crucial for SMEs due to limited resources and higher risks like cash flow, storage space, and stockouts. Hence, the aim is to explore how technology and know-how can be integrated with inventory practices and impact operational performance.Design/methodology/approachThe basis of the analysis was collecting papers from a wide range of databases, which included Scopus, Web of Science, and Google Scholar. In the first phase of the process, a search string with as many as nine related keywords was used to obtain 175 papers. It further filtered them based on their titles and abstracts to retain 95 papers that were included for thorough analysis.FindingsThe study introduced innovative methods of measuring inventory practices by exploring the impact of know-how. It is the first of its kind to identify and demonstrate how technical, technological, and behavioral know-how can influence inventory management practices and ultimately impact the performance of emerging SMEs. This study stands out for its comprehensive approach, which covers traditional and modern inventory management technologies in a single study.Research limitations/implicationsThe study provides valuable insights into the interplay between technical, technological, and behavioral know-how in inventory management practices and their effects on the performance of emerging SMEs in Industry 5.0 in the light of RBV theory.Originality/valueThe RBV theory and the Industry 5.0 paradigm are used in this study to explore how developing SMEs' inventory management practices influence their performance. This study investigates the effects of traditional and modern inventory management systems on business performance. Incorporating RBV theory with the Industry 5.0 framework investigates firm-specific resources and technological advances in the current industrial revolution. This unique technique advances the literature on inventory management and has industry implications.

The application of six sigma to improve textile factory inventory costs

In the textile industry, the inventory management is pivotal for sustained success in a competitive business environment. This research, informed by an investigation into data from previous research conducted in textile factories, systematically explores the application of Six Sigma principles to address challenges in inventory management. The study utilizes the Define, Measure, Analyse, Improve, Control (DMAIC) framework to investigate and enhance inventory practices. Initiating with the Define phase, the research articulates existing issues through the project charter, setting the stage for the subsequent phases. In the Measure phase, Pareto analysis identifies key contributors to the inventory costs, including materials such as "Cotton 1/30" and "Cotton 1/24," which collectively constitute 75% of inventory costs. It is noteworthy that these materials are crucial components used in production processes, as revealed by insights from the previous research. This important discovery forms the basis for the Enhancement phase, in which the research strategically applies a Just In Time (JIT) system, resulting in a 50% reduction in the level of inventory for "Cotton 1/30" and a 70% reduction for "Cotton 1/24". Two Production Withdrawal Kanban cards as well as seven Production Instruction Kanban cards for "Cotton 1/30" and "Cotton 1/24" are required for the execution. This strategic shift aligns with market demand and results in a significant 55% decrease in holding costs.

Leveraging on Vendor Managed-Inventory Practice for Sustainable Performance: An Empirical Evidence from Selected Polytechnics in Southwest, Nigeria

This study is on leveraging on vendor managed inventory practice for sustainable performance: an empirical evidence from selected polytechnics in Southwest Nigeria. The study used descriptive survey research design and a total of one thousand and thirty-six (1,036) management staff from federal, state and private polytechnics in south west Nigeria constitute the study population. A sample size of five hundred and forty-four (544) determined through multi-stage and stratified sampling technique was used for data analysis. Primary data were obtained through questionnaire while logit regression was employed for the inferential statistical to determine the effects and statistical significance of the relationships between the study variables. The Statistical Package for Social Sciences (SPSS) version 24.0 was applied to process the data and the hypothesis was tested at p-value of = 0.05 level of significance while descriptive statistical analysis was used to identify frequencies and percentages. The finding of the study showed that vendor managed inventory practice cannot be underestimated in determining performance of polytechnics in south west Nigeria. The study recommended that management of polytechnics in South-West Nigeria are advised to first consider the cost of implementing their own inventory system before engaging the service of vendor to manage inventory. This is to enable them minimize the cost of keeping stock and maximize efficiency.

Implementation Analysis of Inventory Accounting at CV. Vivace

Inventory management is of paramount importance for trading companies, necessitating meticulous oversight to prevent potential losses. Adhering to inventory accounting principles is crucial, as failure to do so can result in critical errors. PSAK 14, established by the Indonesian Institute of Accountants (IAI), provides a comprehensive framework for these principles. Companies, such as CV Vivace in the entertainment industry, must scrutinize their inventory practices in strict accordance with PSAK 14 to ensure accuracy and compliance. This research adopts a multifaceted approach, incorporating informant interviews, direct observation, and a thorough review of prior studies. The primary objective is to assess CV Vivace's inventory practices, emphasizing the unique challenges faced by companies in the entertainment sector. The study delves into aspects like income generation and methodologies for inventory valuation, setting itself apart from previous research. While CV Vivace aligns with PSAK 14, there is room for improvement. Transitioning to automated systems can enhance efficiency, reducing reliance on manual data entry and mitigating the risk of fraudulent activities. This research anticipates further discussions and in-depth exploration to provide a comprehensive understanding of PSAK 14-related matters, shedding light on the intricacies of inventory management within the entertainment industry.

Bond Strength of Two Resin Cements with Leucite-reinforced Ceramic Using Different Bonding Agents.

To compare the bond strength of two resin cements to leucite-reinforced ceramics using three different boding agents and evaluate the compatibility of bonding agents. Twenty extracted sound human molars were sectioned horizontally 2-3 mm above the cementoenamel junction (CEJ). CAD/CAM ceramic blocks for inLab were also sectioned to create 4 mm thick and bonded to the dentin. The adhesive groups assigned were divided into four adhesive groups: Group I: Variolink II dual-cure resin cement and Scotchbond Multi-Purpose Plus adhesive, group II: Multilink Automix dual-cure resin cement and Multilink primers, group III: Multilink Automix and Clearfil SE bond 2 (CSE2) adhesive, group IV: Multilink Automix and CSE2 with light curing after adhesive application. Five specimens of each group were sectioned perpendicular to obtain six microsticks of 1 × 1 mm width from each sample. Microtensile bond strength data were expressed in MPa. Fracture modes (FrMs) analyzed for the surfaces were divided into six patterns. Microtensile bond strength data were statistically analyzed with one-way ANOVA and Tukey post hoc tests (α = 0.05). T-test was performed at the 5% significance level to analyze groups III and IV with and without light curing. Group I showed the highest μTBS average of 13.67 MPa, group IV showed 12.26 MPa, group III showed 12.15 MPa, and group II showed the lowest average of 10.84 MPa. No significant differences were found between the bonding agents. However, the six types of failure modes, although all observed, were characterized by the adhesive system: Type I: adhesive failure of laminated dentin and ceramic; type II: adhesive failure of laminated ceramic; type III: adhesive failure of laminated dentin; type IV: cohesive failure of luting agent; type V: cohesive failure of dentin, and type VI: mixed failure of adhesion and cohesion. As a result, the FrM most commonly observed was the adhesive failure at the luting cement-ceramic block interface. The combination of resin cements and bonding agents did not significantly affect the bond strength of CAD/CAM ceramic restorations and dentin. Several universal bonding agents are currently available for direct and indirect bonding, and using the same bonding agent for direct and indirect restorations could simplify inventory and benefit routine clinical practice. How to cite this article: Aida N, Koi K, Amaya-Pajares SP, et al. Bond Strength of Two Resin Cements with Leucite-reinforced Ceramic Using Different Bonding Agents. J Contemp Dent Pract 2023;24(11):859-863.

Influence of Supply Chain Integration Practices on Operation Performance at Trans-Nzoia County Government Treasury, Kenya

Supply chain integration practices are considered a powerful weapon to gain competitive advantages and linking performance measurement systems to supply chain integration practices can lead to increased success of supply chain initiatives. This study sought to analyze the influence of supply chain integration practices on operation performance at Trans-Nzoia County Government Treasury, Kenya. Porter's Value Chain Theory and Systems Theory guided the study. This study employed a descriptive research design. Data collection involved a diverse group of participants, totalling 1207 individuals, from which a sample size of 121 respondents was determined. Descriptive data analysis was conducted using SPSS version 28, enabling the examination of both quantitative and qualitative information through coding. The research instrument, a five-point Likert scale questionnaire, demonstrated high reliability through its internal consistency, with a Cronbach's Alpha correlation coefficient exceeding 0.7. To assess the data both descriptively and inferentially, statistical techniques, including means, standard deviations, Pearson's correlation, and regression analysis, were employed. The findings highlighted the significant impact of supply chain integration practices, encompassing efficient consumer response practices, customer relationship management practices, vendor-managed inventory practices, and enterprise resource planning practices on operation performance at Trans-Nzoia County Government Treasury. The regression analysis revealed that when considered together, these practices explained 73.4% of the variance in operation performance. Furthermore, a positive correlation was observed between these supply chain integration practices and operation performance. Based on these insightful findings, the study recommended a more substantial incorporation of supply chain integration practices, particularly in efficient consumer response practices, customer relationship management practices, vendor-managed inventory practices, and enterprise resource planning practices. These implications held value for various stakeholders, including policymakers, government entities, researchers, and academics. By providing actionable insights, this research facilitated informed decision-making, ultimately contributing to the enhanced operation performance at Trans-Nzoia County Government Treasury, Kenya.

Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach

This study delves into optimizing sustainable inventory management practices through the integration of advanced data analytics methodologies. In response to the complex dynamics of modern supply chains, where inventory control significantly impacts sustainability goals, this research aims to address the intricate interplay between decentralized decision-making, government policies, and strategic choices within supply chain networks. Employing models such as Game Theory and Gated Recurrent Unit (GRU), alongside statistical analyses, our investigation explores the transformative potential of informed decision-making frameworks. Through a comprehensive evaluation of inventory data, including statistical analyses, visual representations, and model evaluations, we illuminate the nuanced relationships and dependencies prevalent within inventory control strategies. Our findings underscore the significance of data-driven decision-making in optimizing inventory practices, mitigating risks, and fostering sustainability within supply chains. The insights gleaned from this study advocate for the continued application of advanced data analytics to pave the way for resilient, environmentally conscious, and economically viable supply chain practices.

Strategic Optimization Technique for Achieving Sustainable Inventory Management in Medium-Sized Enterprises

Medium-sized enterprises face the dual challenge of optimizing inventory practices while aligning with sustainable objectives. Effective inventory management in medium-sized enterprises remains a critical factor in balancing operational efficiency and sustainability. This study addresses the challenge by investigating strategic optimization approaches for inventory control. The background explores the complexities of inventory management, emphasizing the need for enhanced techniques in medium-sized enterprises. The problem statement underscores the necessity of innovative methodologies to navigate the intricate landscape of inventory control and sustainability. To address these challenges, advanced methodology based on LightGBM is applied to forecast demand, assess vendor purchase costs, optimize stock levels, and evaluate predictive performance. Empirical findings, showcased through visualizations, revealed insights into the distribution of purchase costs among vendors, daily sales velocity by product, and comparisons between current and recommended stock levels for top products. Comparative analyses demonstrated LightGBM's superior predictive performance over baseline models, highlighting its potential as a valuable tool in decision-making for inventory management.

Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores

In today's competitive grocery retail environment, effective inventory management coupled with precise sales prediction is pivotal for success. This research undertakes an in-depth exploration into a machine learning framework tailored for "Sales Forecasting and Inventory Optimization in Grocery Retail." Utilizing historical sales records, meteorological data, and consumer behavior metrics, the proposed model endeavors to predict nuanced seasonal demand fluctuations. The primary value proposition of this approach lies in its capacity to curtail wastage, ensuring the balance between overstocking and inventory stockouts. By recognizing and adjusting for seasonal dynamics, the system offers enhanced demand fulfillment during high-demand periods. The subsequent refinement of the replenishment cycle fosters superior operational efficacy. As an outcome, businesses witness cost reductions, augmented profit margins, and elevated consumer contentment due to consistent product availability. As the broader grocery sector undergoes transformation, embedding sophisticated machine learning strategies can be a linchpin for sustained adaptability and competitiveness. This research further underscores the role of emergent technologies, such as machine learning within the IoT spectrum, in reshaping supply chain and communication paradigms. The endeavor here is to curtail lifecycle expenses in the supply chain by refining inventory practices. The research introduces a Deep Inventory Management (DIM) technique, employing the GradientBoostingRegressor paradigm of machine learning. Through DIM's innovative approach, time-series challenges are reimagined as supervised learning tasks, enabling efficient model training. Preliminary tests indicate DIM's prediction accuracy hovers around 85%, translating to an impressive 15% reduction in inventory expenses when juxtaposed with prevailing methods, alongside rapid anomaly detection in inventory activities

Bandits atop Reinforcement Learning: Tackling Online Inventory Models with Cyclic Demands

Motivated by a long-standing gap between inventory theory and practice, we study online inventory models with unknown cyclic demand distributions. We design provably efficient reinforcement learning (RL) algorithms that leverage the structure of inventory problems to achieve optimal theoretical guarantees that surpass existing results. We apply the standard performance measure in online learning literature, regret, which is defined as the difference between the total expected cost of our policy and the total expected cost of the clairvoyant optimal policy that has full knowledge of the demand distributions a priori. This paper analyzes, in the presence of unknown cyclic demands, both the lost-sales model with zero lead time and the multiproduct backlogging model with positive lead times, fixed joint-ordering costs and order limits. For both models, we first introduce episodic models where inventory is discarded at the end of every cycle, and then build upon these results to analyze the nondiscarding models. Our RL policies HQL and FQL achieve [Formula: see text] regret for the episodic lost-sales model and the episodic multiproduct backlogging model, matching the regret lower bound that we prove in this paper. For the nondiscarding models, we construct a bandit learning algorithm on top that governs multiple copies of the previous RL algorithms, named Meta-HQL. Meta-HQL achieves [Formula: see text] regret for the nondiscarding lost-sales model with zero lead time, again matching the regret lower bound. For the nondiscarding multiproduct backlogging model, our policy Mimic-QL achieves [Formula: see text] regret. Our policies remove the regret dependence on the cardinality of the state-action space for inventory problems, which is an improvement over existing RL algorithms. We conducted experiments with a real sales data set from Rossmann, one of the largest drugstore chains in Europe, and also with a synthetic data set. For both sets of experiments, our policy converges rapidly to the optimal policy and dramatically outperforms the best policy that models demand as independent and identically distributed instead of cyclic. This paper was accepted by J. George Shanthikumar, data science. Funding: X.-Y. Gong was partially supported by an Accenture Fellowship. The work of X.-Y. Gong and D. Simchi-Levi was partially supported by the MIT Data Science Lab. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2023.4947 .

A Primer on Clustering of Forest Management Units for Reliable Design-Based Direct Estimates and Model-Based Small Area Estimation

This study employs clustering analysis to group forest management units using auxiliary, satellite imagery-derived height metrics and past wall-to-wall tree census data from a natural, uneven-aged forest. Initially, we conducted an exhaustive exploration to determine the optimal number of clusters k, considering a wide range of clustering schemes, indices, and two specific k ranges. The optimal k is influenced by various factors, including the minimum k considered, the selected clustering algorithm, the clustering indices used, and the auxiliary variables. Specifically, the minimum k, the Euclidean distance metric, and the clustering index were instrumental in determining the optimal cluster numbers, with algorithms exerting minimal influence. Unlike traditional validation indices, we assessed the performance of these optimally defined clusters based on direct estimates and additional criteria. Subsequently, our research introduces a twofold methodology for Small Area Estimation (SAE). The first approach focuses on aggregating forest management units at the cluster level to increase the sample size, thereby yielding reliable design-based direct estimates for key forest attributes, including growing stock volume, basal area, tree density, and mean tree height. The second approach prepares area-level data for the future application of model-based estimators, contingent on establishing a strong correlation between target and auxiliary variables. Our methodology has the potential to enhance forest inventory practices across a wide range of forests where area-level auxiliary covariates are available.

Sustainable manufacturing practices and sustainable performance: Evidence from Ghana’s food manufacturing sector

This study investigated the combined effects of inventory, green, supply and lean manufacturing practices on the sustainable performance (environmental [EP], social [SP] and economic [EcP] performances) of food manufacturing enterprises in emerging economies like Ghana. The study adopted the quantitative approach and explanatory research design. Primary data was obtained from 247 owners and managers of food manufacturing enterprises in Ghana through structured questionnaires. Twelve hypotheses were developed and tested using the partial least square-structural equation modelling (PLS-SEM) analytical tool. The study’s results revealed that inventory and green manufacturing practices directly influenced sustainable performance (SP, EP, and EcP), while lean manufacturing practices had an insignificant influence on economic performance. Supply management, on the other hand, significantly improved only economic performance. The outcome of the study provides valuable information for food manufacturers in Ghana and sub-Saharan Africa. The outcome will provide more robust support for food manufacturers to invest heavily into sustainable supply chain management practices such as inventory, green and lean while establishing stronger relationships with key suppliers to guarantee a consistent supply of raw materials; thereby, promoting sustainable performance. This study contributes to the current literature on sustainable supply chain management and sustainable performance with concrete evidence from food manufacturing enterprises within an emerging economy.