Demand For Items Research Articles

A two-stage approach for modeling, performance evaluation, and decision support of disassembly processes

ABSTRACT This paper tackles an issue of disassembly lot-sizing within the framework of stochastic batch demand for multi-products featuring a two-level structure. The suggested methodology relies on a batch deterministic and stochastic Petri net as a modeling tool to address such problems in the context of stochastic processes. The primary objective is to determine the optimal sequencing of disassembly for each product type to meet stochastic batch demands of root items across multiple criteria. A novel mathematical model is introduced to minimize the cumulative costs associated with disassembly operations and extra costs, as well as holding costs for diverse products. In a subsequent stage, a decision support tool, namely the preference ranking organization method for enrichment evaluation method, is put forward for prioritizing different scenarios. For assessing the efficiency of the suggested approach, a real case study from the manufacturing industry is simulated. The simulation results prove the suggested approach’s capability to adapt to dynamic demands for leaf items throughout the planning horizon.

Taste and Habit of Food and Consumer Behavior in the USA in the Early Years of Covid-19 Pandemic

The USA, a highly advanced economy in the world, had experienced increase in household demand for specific food items (restaurant and cafeteria supply, in particular) in the early years of Cobid-19 pandemic. People of America depended largely on the tastes and habits of food items in their household consumption. Even with increasing prices the demand for restaurant foods showed increasing trends. This means that the consumption of food away from home (FAFH) increased and food at home (FAH) fell in demand in 2020 and 2021 (Covid- 19 pandemic). Fundamental law of demand in economics (Ceteris Paribus) for normal goods demand will fall if price of the same increases and vice versa. In the present study, rise in prices of normal goods (restaurant and cafeteria foods) had also shown increase in their demand. This perhaps shows paradoxical to the law of demand in economic theory.  Methodology: This is a study based on secondary sources of information including research works, official publications and those from academic institutions. Analysis of data generated and narratives have been used to find the contemporary food economy of the USA. The demand for food (both FAFH and FAH) has been given importance in the situation of food inflation in the USA in early years of Covid- 19 pandemic. Finally, the equilibrium of the household food consumption is shown geometrically (Fig-1).  Findings and Results: It is found from the study that while the price of food away from home (FAFH) was increasing, the demand for those was also increasing in 2020 and 2021 in the USA. Food being a ‘normal good’ might have shown a fall in demand when price was increasing. This is perhaps, paradoxical in view of the law of demand in economic theory.

Automatic generation of physics items with Large Language Models (LLMs)

High-quality items are essential for producing reliable and valid assessments, offering valuable insights for decision-making processes. As the demand for items with strong psychometric properties increases for both summative and formative assessments, automatic item generation (AIG) has gained prominence. Research highlights the potential of large language models (LLMs) in the AIG process, noting the positive impact of generative AI tools like ChatGPT on educational assessments, recognized for their ability to generate various item types across different languages and subjects. This study fills a research gap by exploring how AI-generated items in secondary/high school physics aligned with educational taxonomy. It utilizes Bloom's taxonomy, a well-known framework for designing and categorizing assessment items across various cognitive levels, from low to high. It focuses on a preliminary assessment of LLMs ability to generate physics items that match the Bloom’s taxonomy application level. Two leading LLMs, ChatGPT (GPT-4) and Gemini, were chosen for their strong performance in creating high-quality educational content. The research utilized various prompts to generate items at different cognitive levels based on Bloom's taxonomy. These items were assessed using multiple criteria: clarity, accuracy, absence of misleading content, appropriate complexity, correct language use, alignment with the intended level of Bloom's taxonomy, solvability, and assurance of a single correct answer. The findings indicated that both ChatGPT and Gemini were skilled at generating physics assessment items, though their effectiveness varied based on the prompting methods used. Instructional prompts, particularly, resulted in excellent outputs from both models, producing items that were clear, precise, and consistently aligned with the Application level of Bloom's taxonomy.

A choice-based approach to dynamic capacitated multi-item lot sizing with demand uncertainty

With the purpose of planning and implementing pricing decisions on a tactical level as well as production decisions on an operational level, we consider – in an integrated form – the capacitated multi-item lot sizing problem with uncertain item demands and price-dependent discrete choice demand. The model is embedded into an overarching rolling horizon procedure allowing for adaptations to changes in demand and cost parameters. We first formulate the static problem version as a nonlinear mathematical program with underlying multinomial logit demand and subsequently linearize it to make it viable for mathematical programming solvers. Uncertainty of demands is taken into account by Monte Carlo simulation. More specifically, we generate random demand scenarios and utilize them as input data for the sample average approximation problem version. We further endow the problem setting with possibilities to incorporate pricing policy requirements such as restricting the number of price adaptations or defining periods without price adaptations. Overall, the developed approach yields a powerful tool for balancing item demands via pricing in a way favorable for adhering to available production capacities and thereby striking a balance between revenues and costs. Computational results confirm that adapting prices to time-dependent demand and cost parameters is exploited effectively to maintain a deliberately controlled production environment. Moreover, the integrated pricing and production setting allows to study the effect of pricing policy restrictions and demand uncertainties upon attainable profits.

Optimal advertising structure in the presence of a platform owner’s entry

As consumer demand for mobile electronic items (e.g., mobile phones, tablets, or wearables) rises, advertising competition among electronics manufacturers becomes more intense. Moreover, platform owners that offer platforms for electronics manufacturers may use private labels to enter the manufacturers’ market, which further prompts the manufacturers to advertise in response to platform entry. We study how a platform owner’s entry affects advertising and price competition for a strong manufacturer and a weak manufacturer who are differentiated in production costs and advertising efficiency, and reveal the impacts of platform entry and two typical revenue models on the optimal advertising structures of supply chain members in the platform-based market. In the absence of entry, we discover that a relatively larger production cost advantage induces only the strong manufacturer to advertise while a relatively higher advertising efficiency advantage induces only the weak manufacturer to advertise in the wholesale model. In the bundling model, we show that the platform is always beneficial from the advertising of the manufacturer who can generate higher marginal revenue. With entry, our analysis reveals how the production cost of the platform and the differences in advertising efficiency between the manufacturers affect the optimal advertising structure. In particular, we find that as the production cost increases, the platform first prefers only the weak manufacturer to advertise and then both manufacturers to advertise. In practice, our findings have significant managerial implications for platform owners and electronics manufacturers engaged in advertising competition.

Sustainable Hybrid Manufacturing and Refurbishing Systems with Substitution

This study considers the planning decisions in a production facility that receives returned products as a contribution to sustainability through the reduction in waste from used products. It involves three processes: the refurbishing and disassembly of returned items and the manufacturing of new items. The process is driven by the demands of new items and collected secondhand items. In this study, we consider a downward substitution, in which new items could be used to meet the demand for secondhand items. The objective is to determine the best production planning schedule to satisfy all demands while minimizing the total costs of production and inventory. We propose and analyze different configurations distinguished by their level of integration and whether they allow substitution. Integration is mainly achieved through the sharing of the inventory of returns between the refurbishing line and the disassembly line, which responsible for feeding the manufacturing process with “as good as new” parts. The best configuration is identified based on the total cost and environmental impact. Four heuristics based on combining relax-and-fix and fix-and-optimize approaches are proposed. Numerical experiments have shown that the heuristics are very efficient, achieving gaps of less than 1% from the optimum in short CPU times for most instances. Numerical experiments have also shown that integration through the sharing of returns inventories leads to considerable cost and environmental benefits.

The Utilization of Time Series Data Forecasting Techniques on Hospitals Pharmaceutical Inventory

Good inventory management is essential in the hospital industry to overcome inventory problems. Ineffective inventory prediction methods can lead to shortages or excess stock inventory. Ultimately, this can impact the budget and availability of pharmaceutical items in the hospital. Previous traditional prediction methods often show inaccuracies. This research utilizes ARIMA (Autoregressive Integrated Moving Average) and FB Prophet methods to predict the demand for pharmaceutical items in hospitals. In an attempt to evaluate the effectiveness of both methods, an experiment was conducted on five pharmaceutical items. The results showed that the ARIMA method produced better performance compared to the FB Prophet method, with the smallest error of 0.07310.

Capacitated multi-item multi-echelon lot sizing with setup carry-over under uncertain demand

This study focuses on the lot sizing problem with setup carry-over in multi-item multi-echelon capacitated production systems under uncertain customer demand for end items, as well as components. We investigate budget-uncertainty robust optimization and scenario-based stochastic programming, to address the uncertainty in customer demand. Three modeling strategies are proposed within the stochastic programming framework, exploring different decision stages for setup carry-over and production quantities. In our examination of the robust model, we explore different robustness parameters, specifically the uncertainty budget and the variation interval. Extensive numerical experiments are conducted to compare the average and worst case performance of the models on out-of-sample scenarios. We fit conditional inference trees to the evaluation results and predict the suitability of robust and stochastic approaches for the test instances based on their problem characteristics. The findings provide valuable insights, potentially enabling decision makers to estimate the most appropriate approach based on certain characteristics of the lot sizing problem that is addressed. Moreover they highlight the importance of choosing appropriate robustness parameters for robust optimization models and emphasize the value of flexibility in carry-over and quantity decisions.

Ergonomic evaluation of assembly line work activity in the automotive industry: a case study

The automotive industry is growing worldwide with new technologies and seeking maximum cost savings. This sector is essential for the economic development of the country, with modern industries that invest in technological innovations and control of the activities of the production process. With this, the workstations become increasingly dynamic and characterized in the logic of productivity. Therefore, this study aimed to evaluate the work activity of operators of an assembly line of automotive components, from the ergonomic analysis. Through a descriptive and exploratory field study, the work activity of 30 operators of an assembly line of metallic components of the automotive industry was evaluated. Using instruments such as sociodemographic questionnaire; Nordic questionnaire; open interview, with collective questions about the development of the function, listed in an ergonomic demand items (EDIs) ranking; and complementary application of the NIOSH and OCRA methods. As a result, all respondents were male, with an average age of 30.13 ± 8.57 years, most had completed high school (76.7%), with an average working time in the company of almost two years (21.93 ± 12 months). Regarding relationships with coworkers, it was observed that the sector has a great relationship during the activities, with both colleagues and the boss. This study demonstrated a lower frequency of musculoskeletal symptoms in operators when compared to other studies. Symptoms that caused work leave were neck (3.3%), shoulders (3.3%), upper (6.7%) and lower back (6.7%), and hips/thighs (6.7%), ankle/foot (6.7%) were reported.

Research on Design Methods for Interactive Spaces in Schools for Children with Intellectual Disabilities Considering User Needs

To scientifically enhance user perception in decision-making for designing interactive spaces in schools for children with intellectual disabilities, we propose an innovative design model that integrates the Kano model, Analytic Hierarchy Process (AHP), and Axiomatic Design (AD) theories based on user needs. Initially, multi-method research was used to gather the original user requirements which were then refined through data cleaning to establish the initial user needs. The Kano model was then employed to categorize these initial user needs. AHP was then used to construct a hierarchical analysis model for the interactive spaces in schools for children with intellectual disabilities, creating a judgment matrix to accurately calculate demand weight values at each level. Subsequently, AHP was used to select the most important demand items. The independence axiom of AD theory was used to achieve a “Z”-shaped mapping between the functional requirements (FRs) and design parameters (DPs) for the interactive spaces in schools for children with intellectual disabilities. This mapping was analyzed using a matrix approach to assess the design rationality and optimize solutions, thereby transforming user needs into design parameters. Finally, the design parameters were used to create interactive spaces through computer-aided design, and the resulting design plans were evaluated. Experimental results indicate that this design scheme effectively translates subjective concepts into specific design parameters through a qualitative and quantitative approach. This significantly enhances the user needs of interactive spaces in schools for children with intellectual disabilities and provides a scientific basis for the architectural design of these schools.

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.

A sustainable manufacturing–remanufacturing inventory model with price and green sensitive demand for defective and usable items

A sustainable manufacturing–remanufacturing inventory model with two production phases is presented in this study. In this two phases model, the first phase is used for manufacturing whereas the second phase is used for both manufacturing and remanufacturing. Keeping in mind about environment and price of products, it is presumed that market demand rate is influenced by the greenness of item and unit selling price simultaneously. Remanufacturing is performed for non-serviceable items i.e., defective items which are produced from imperfect production and used items that are purchased from the market. But the products for which remanufacturing cost is more than manufacturing cost are scraped off. In the production process carbon releases from setting up, manufacture and holding of the perfect items. To mitigate the carbon emission carbon tax regulation is imposed. Next, concavity of the profit function is driven through the pseudo concavity. Profit is maximized by minimizing the economic, environmental, and social effect. The models are illustrated by a numerical example and a sensitivity analysis is also elaborated for fixed parameters. Special cases are provided to compare the result with previous literature. The results shows that manufacturer wishes to engage in remanufacturing as remanufacturing cost continuously increases with respect time. Another important finding is that the manufacturer can choose remanufacturing as the cost is lower than manufacturing for saving environment. Finally, mathematical findings are listed and managerial implications are recorded.

Cosmetovigilance in India: Industry Expert Perspective

ABSTRACTMany cosmetic and cosmeceutical products and brands are available in the Indian market. The number of new and innovative cosmetic product categories available in India are on the rise for last two decades. The cosmetic industry ranges from local small-scale businesses to multi-crore international branded ventures. Most of the local ones are often not under any rules or regulations. There is a dearth of research on cosmetovigilance despite the growing popularity and demand for cosmetic items among consumers of cosmetics. “The term “cosmetovigilance” refers to the processes involved in assessing and keeping track of unprompted reports of unfavorable outcomes during or following regular or reasonably anticipated use of a cosmetic product.”Objective: The purpose of the study is to understand the cosmetovigilance system from industry experts’ perspectives with special reference to regulatory aspects in the Indian context.Method: This is a cross-sectional qualitative and quantitative study; the data was collected through a structured questionnaire and through a questionnaire guide from selected industrial regulatory experts (targeted respondents). The purposive sampling and convincing methods were used for the study. Descriptive statistics were used to analyze the data.Result: Around 73% of regulatory experts said that companies should follow the regulatory guidelines for ensuring the safety and labeling of cosmetics. Around 66.6% responded that they have not come across any ACE reporting form by CDSCO. From the point of view of consumer safety, the establishment of cosmetovigilance practises and a reporting form is essential. Nevertheless, the companies try their best to control the safety aspects of cosmetic products.

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

Customers can access hundreds of reviews for a single product in online marketplaces. Buyers often use reviews from other customers that share their type---such as height for clothing or skin type for skincare products---to estimate their values, which they may not know a priori. Customers with few relevant reviews may hesitate to purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so buyers can confidently estimate their values. Simultaneously, sellers may use reviews to gauge the demand for items they wish to sell. In this work, we study this pricing problem in an online setting where the seller interacts with a set of buyers of finitely many types, one by one, over a series of T rounds. At each round, the seller first sets a price. Then, a buyer arrives and examines the reviews of the previous buyers with the same type, which reveal those buyers' ex-post values. Based on the reviews, the buyer decides to purchase if they have good reason to believe their ex-ante utility is positive. Crucially, the seller does not know the buyer's type when setting the price, nor even the distribution over types. We provide a no-regret algorithm that the seller can use to obtain high revenue. When there are d types, after T rounds, our algorithm achieves a problem-independent Õ ( T 2/3 d 1/3 ) regret bound. However, when the smallest probability q min that any given type appears is large, specifically when q min ∈ Ω( d −2/3 T −1/3 ), the same algorithm achieves a [EQUATION] regret bound. We complement these upper bounds with matching lower bounds in both regimes, showing that our algorithm is minimax optimal up to lower-order terms. This is a summary of work that won the Exemplary AI Track Paper Award at EC'24.

Expanding the Lognormal Response Time Model Using Profile Similarity Metrics to Improve the Detection of Anomalous Testing Behavior

AbstractThe Lognormal Response Time (LNRT) model measures the speed of test‐takers relative to the normative time demands of items on a test. The resulting speed parameters and model residuals are often analyzed for evidence of anomalous test‐taking behavior associated with fast and poorly fitting response time patterns. Extending this model, we demonstrate the connection between the existing LNRT model parameters and the “level” component of profile similarity, and we define two new parameters for the LNRT model representing profile “dispersion” and “shape.” We show that while the LNRT model measures level (speed), profile dispersion and shape are conflated in model residuals, and that distinguishing them provides meaningful and useful parameters for identifying anomalous testing behavior. Results from data in a situation where many test‐takers gained preknowledge of test items revealed that profile shape, not currently measured in the LNRT model, was the most sensitive response time index to the abnormal test‐taking behavior patterns. Results strongly support expanding the LNRT model to measure not only each test‐taker's level of speed, but also the dispersion and shape of their response time profiles.

Shortage Policies for a Jump Process with Positive and Negative Batch Arrivals in a Random Environment

We study a continuous-review stock management of a retailer for a single item in a limited storage (buffer) in a random environment. The stock level fluctuates according to two independent compound Poisson processes with discrete amounts of items (batches) that enter and leave the storage facility. The storage facility is controlled by a three-parameter base-stock replenishment policy. All items exceeding the storage capacity are transferred to an unlimited foreign facility. In addition, a restricted backlogging possibility is permitted; additional demands for items are lost sales. We further assume a random shelf life, the possibility of total inventory collapse, and a random lead time. Applying Markov theory, we derive the optimal control parameters minimizing the long-run expected total cost. A sensitivity analysis is conducted focusing on the comparison between the pure lost-sales policy and a partial backordering policy. Accordingly, we identify cases where one policy is cost effective compared to the other, particularly with respect to the batch patterns (sign, rate, average, and variability), and the associated costs.

A prescription for efficiency: optimizing the surgical item inventory management practices in a healthcare facility

PurposeThe primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.Design/methodology/approachThis study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.FindingsIt was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.Research limitations/implicationsThis study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.Practical implicationsThis study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.Originality/valueThis study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

Learning-Augmented Online Algorithm for Two-Level Ski-Rental Problem

In this paper, we study the two-level ski-rental problem, where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user aims to minimize the total cost (i.e., the sum of the rental, single purchase, and combo purchase costs) by balancing the trade-off between the expensive upfront costs (for purchase) and the potential future expenses (for rent). We first design a robust online algorithm (RDTSR) that offers a worst-case performance guarantee. While online algorithms are robust against the worst-case scenarios, they are often overly cautious and thus suffer a poor average performance in typical scenarios. On the other hand, Machine Learning (ML) algorithms typically show promising average performance in various applications but lack worst-case performance guarantees. To harness the benefits of both methods, we develop a learning-augmented algorithm (LADTSR) by integrating ML predictions into the robust online algorithm, which outperforms the robust online algorithm under accurate predictions while ensuring worst-case performance guarantees even when predictions are inaccurate. Finally, we conduct numerical experiments on both synthetic and real-world trace data to corroborate the effectiveness of our approach.

Navigating Changes in Patient Drug and Non-Drug Item Demands in Community Pharmacies Amidst the COVID-19 Pandemic

During the COVID-19 pandemic, healthcare systems worldwide faced unprecedented challenges, with pharmacists playing a crucial role on the frontlines. They encountered a surge in patient requests for drugs and non-drug items related to the treatment and prevention of the disease. This cross-sectional survey aimed to assess changes in demand for selected drugs and non-drug items from the perspective of pharmacists in Jordan in the period from March to June 2021, and to explore the factors influencing this demand. An online questionnaire targeting pharmacists working in community pharmacies was developed, validated, and disseminated using social media (e.g., WhatsApp, Facebook, and Messenger). The study collected 390 responses from pharmacists working in community pharmacies. The findings revealed significant increases in demand for prescription drugs such as antibiotics (97.4%), antithrombotics (84.1%), and antivirals (66.2%), often without prescriptions. Non-prescription items, specifically minerals and vitamins, were highly sought after (100%). Demand also rose for non-drug items such as thermometers (89.0%), oximeters (85.1%) and oxygen concentrators (68.2%). Changes in drug requirements were consistent across Jordan’s districts. However, an association was observed between the increase in demand for antivirals and herbal supplements and chain pharmacies (p-value 0.037 and p-value <0.005, respectively). In conclusion, COVID-19 led to a significant upsurge in the demand for pharmaceutical products and devices, placing immense pressure on community pharmacies. The public's reaction to the pandemic, to combat and manage the disease, was consistent across Jordan, regardless of social, financial, and spatial differences among the population. The study highlights the importance of adequately preparing and educating pharmacists to provide accurate information and counseling to patients in such circumstances. Therefore, health authorities must ensure that pharmacists have access to the latest treatments and management protocols and provide clear guidance on using home treatment devices to the public.

Prediction of Intermittent Demand Occurrence using Machine Learning

Demand forecasting plays a pivotal role in modern Supply Chain Management (SCM). It is an essential part of inventory planning and management and can be challenging at times. One of the major issues being faced in demand forecasting is insufficient forecast accuracy to predict the expected demand and fluctuation in actual vs. the predicted demand results in fore-casting errors. This problem is further exaggerated with slow-moving and intermittent demand items. Every organization encounters large proportions of items that have small ir-regular demand with long periods of zero demand, which are known as intermittent demand Items. Demand for such items occur sporadically and with considerable fluctuation in the size of the demand. Forecasting of the intermittent demand entails the prediction of demand series that is characterized by the time interval between demand being significantly greater than the unit forecast period. Because of this there are multiple periods of no demand in the intermittent demand time series. The challenge with these products with low irregular demand is that these items need to be stocked and replenished at regular interval irrespective of the demand cycle, thus adding to the cost of holding the inventory. Since the demand is not continuous, Traditional Forecasting models are unable to provide reliable estimate of required inventory level and replenishment point. Forecast errors would resulting in obsolescent stock or unfulfilled demand. The current paper presents a simple yet powerful approach for generating a demand forecasting and replenishment process for such low volume intermittent demand items to come up with a recommendation for dynamic re-order point, thus, improving the inventory performance of these items. Currently, the demand forecast is generally based on past usage patterns. The rise of Artificial Intelligence/Machine Learning (AI/ML) has provided a strong alternative to solve the problem of forecasting Intermittent Demand. The intention is to highlight that machine learning algorithm is more efficient and accurate than traditional forecasting method. As we move forward to industry 4.0, the digital supply chain is considered as the most essential com-ponent of the value chain wherein the inventory size is controlled, and the demand predicted.