Effective Inventory Management Research Articles

Procurement Procedures and Implementation of Kenya Ports Authority Projects, Mombasa County

The Kenya Port Authority aims at aligning its policies and projects to the government’s Big Four development agenda: Food Security, Manufacturing, Universal Healthcare and Affordable Housing. However, most projects have faced key challenges on time and costs management which have led to lack of project completion within the specified time and thus increases the costs involved. The intended benefits are partly or never realized due to many unsuccessful project implementations. The main objective of the research study was to investigate the influence of procurement procedures on the implementation of Kenya Ports Authority Projects, Mombasa County. The specific objectives of the study were to examine the influence of procurement planning, supplier selection, contract monitoring and control and inventory management. The study was guided by grey system theory and lean inventory theory. This study utilized descriptive research design. The unit of observation was the project managers and project team who are directly involved in the implementation of projects undertaken by KPA. Therefore, the targeted respondents were 100 comprising of 15 project managers and 85 project team members. A census of 100 respondents was carried out. Primary data was collected using questionnaires. Piloting of research instruments was carried out within the organization involving 10 respondents who were not involved in the final study. Content validity was employed in this study to determine how well the research instruments will measure what they were designed to measure. Cronbach’s alpha method was used to check for the internal consistency of the items in the questionnaire. Quantitative data was analyzed using descriptive statistics such as mean and standard deviation. In order to test the relationship between variables and the extent to which they are influence each other multiple regression analysis was applied. The study concluded that procurement planning increases efficiency and reduces procurement time which enables procurement arrangements to be more appropriate to the contracts being procured, delivering faster processing of activities and more efficient contract implementation. Supplier selection based on quality factors improves quality project implementation in Kenya Ports Authority. contract monitoring and control ensures that products are delivered as and when they are ordered. Procurement activities are assessed continuously to ensure that contracts are honored, and all purchasing processes have been followed. Effective inventory management allows the organization to fulfill orders on time and accurately. The study recommended that during the procurement process the organization should consider procurement objectives, the savings and benefits opportunities and the measures and targets and the approach to risk management. When selecting suppliers the organization should consider their reliability and quality of suppliers which needs to be consistent. The organization should standardize contract creation using a set of terms, conditions, and legal language that applies to a large subset of contracts. The organization should set minimum stock levels for each item that allows it to order new stock in before it runs out, understand its supply chain and minimize shrinkage.

Inventory Forecasting and Control Decisions for Effective Inventory Management in the South African Automotive Component Manufacturing Industry: Pre COVID-19 and Lockdown Period

The COVID-19 global health pandemic significantly affected the global economy and the automotive industry when lockdown measures were implemented. This study seeks to investigate the influence of inventory forecasting and control decisions on effective inventory management in the South African automotive component manufacturing (SAACM) industry. Using a positivistic paradigm with a quantitative research approach, data was sourced from 162 Automotive Component Manufacturers (ACMs) to establish whether their inventory forecasting and control decisions changed from prior COVID-19 to the lockdown period during the COVID-19 pandemic. The multiple regression analysis found statistically significant relationships between inventory forecasting decisions and effective inventory management prior to COVID-19, inventory control decisions and effective inventory management prior to COVID-19 and inventory forecasting decisions and effective inventory management during the lockdown period. Thus, regardless of the COVID-19 pandemic, inventory managers in ACMs in South Africa (SA) should use inventory forecasting methods such as demand forecasting, economic order quantity and materials requirement planning. They should further consider using an inventory information sharing system and inventory replenishment procedure to manage inventory more effectively.

ФОРМИРОВАНИЕ СИСТЕМЫ УПРАВЛЕНИЯ ТОВАРНЫМИ ЗАПАСАМИ В ДИСТАНЦИОННОЙ ТОРГОВЛЕ

The development of digital technologies is currently influencing the development of distance trading. One of the urgent tasks of a trade organization is the effective management of inventory. The article examines the potential for the development of an inventory management system in distance selling in the context of digitalization. Particular attention is paid to the time interval between the formation of consumer choice and the transfer of goods.

Effective inventory management among Malaysian SMEs in the manufacturing sector towards organisational performance

Effective inventory management among Malaysian SMEs in the manufacturing sector towards organisational performance

Data-Driven Environmental Sustainability of Supply Chain for Medical Equipment

Sustainable supply chain of medical equipment is challenging due to the inherent importance of having health-related components in-stock whilst balancing the economic and environmental impact of that. In general, to minimize the environmental impact of inventory management, physical inventory space and transportation amount can be lowered. However, in medical equipment, shortage of medical materials, and supplies could cause delays in providing adequate healthcare services. Therefore, effective inventory management is required to maintain a steady supply while minimizing the environmental and economic impact of supply chain. This study is aimed at assessing the environmental and economic impact of inventory for a medical equipment manufacturer. Inventory classification of the medical equipment manufacturer is performed using ABC analysis. After inventory classification using ABC, safety stock analysis is performed. Obtained inventory results are analysed both in terms of environmental sustainability using Life Cycle Assessment (LCA) approach and in terms of economic impact calculating cost of holding safety stock. An LCA model is created based on transportation of procurements. Impact assessment is performed to define most environmentally and economically impactful suppliers of the medical equipment manufacturer.

THE INVENTORY MANAGEMENT PRACTICES AND ORGANIZATIONAL EFFICIENCY OF A CAR DEALER IN SAN PABLO, CITY

Effective inventory management is crucial for effective organizational functioning. The study focused on the inventory management practices used by a car dealer in San Pablo, City and their effectiveness, its importance and the challenges faced in applying inventory management practices by automotive dealers. The participants of the study are the employees of the company: Parts, Sales, Service, Finance and Admin Departments. Findings highlight the dealer’s employment of the first-in, first-out (FIFO) method for stock valuation, managed via bin cards and excel. The study concludes that adept inventory management can curtail costs. Recommendations involve refining strategies with precise record-keeping, a robust stock plan, and meticulous accounting for an efficient inventory system.

THE INVENTORY MANAGEMENT PRACTICES AND ORGANIZATIONAL EFFICIENCY OF A CAR DEALER IN SAN PABLO, CITY

Effective inventory management is crucial for effective organizational functioning. The study focused on the inventory management practices used by a car dealer in San Pablo, City and their effectiveness, its importance and the challenges faced in applying inventory management practices by automotive dealers. The participants of the study are the employees of the company: Parts, Sales, Service, Finance and Admin Departments. Findings highlight the dealer’s employment of the first-in, first-out (FIFO) method for stock valuation, managed via bin cards and excel. The study concludes that adept inventory management can curtail costs. Recommendations involve refining strategies with precise record-keeping, a robust stock plan, and meticulous accounting for an efficient inventory system.

Effective inventory management among Malaysian SMEs in the manufacturing sector towards organisational performance

Effective inventory management among Malaysian SMEs in the manufacturing sector towards organisational performance

APPLICATION OF DATA SCIENCE METHODS FOR DEMAND FORECASTING IN RETAIL

This scientific article examines the problem of forecasting demand in retail using data science methods. It is explained that traditional methods of demand forecasting do not give an excellent result, as machine learning, statistical models and data analysis become powerful tools, they need improvement, therefore this research is necessary and appropriate. The importance of accurate demand forecasting for effective inventory management, cost reduction, and customer service improvement is analyzed. The main methods of data science are considered, such as: machine learning, statistical models and data analysis. Real examples of the use of these methods in retail companies and their impact on increasing the accuracy of demand forecasting are also presented and clearly characterized for each company. Key steps in the forecasting process are described, including data collection and preparation, model selection, training, and performance evaluation. Examples of the use of machine learning algorithms, such as linear regression, decision trees, and neural networks, for demand forecasting in the retail sector are provided, and a comparison of these approaches is highlighted. The proposed price optimization procedure. This article shows that forecasting and analytics are integral to the effectiveness and competitiveness and flexibility of retailers in the market, and that the results of this study can be widely applied to further study the application of these methods, as well as to identify new methods. According to the scientific article, a conclusion was made that this research should be continued, and it will contribute to the effective functioning of retail companies and improve their competitiveness on the market. Recent achievements and prospects of using data science in demand forecasting are discussed.

Влияние сложности продукции на устойчивое развитие промышленных предприятий

The article considers the influence of product complexity (expansion of the product range for ensuring 
 a balanced product portfolio capable of meeting consumer requirements, used technological equipment and raw materials components characterized by non-standard characteristics, complexity of technological processes, etc.) on operational efficiency (production time, cost, supply chain, effective inventory management, etc.) and, as a consequence, on ensuring the sustainable development of industrial enterprises, taking into account the peculiarities of production and economic activity. The concepts of product complexity and operational efficiency are studied in detail. It is demonstrated that the product complexity has a stable relationship with the increasing costs, time spent on the produced unit, providing the warehouse balance of finished products, amount of labor, downtime of the main technological equipment associated with the production of a large assortment of goods. Relationship between the increased complexity of the product and its quality is less pronounced. Control over the quality indicators is important at all phases of the production process. The negative influence of the product complexity on most performance indicators is a phenomenon existing in all industries, which is a warning to manufacturers expanding the product line. The criteria for measuring the complexity of products were identified. A methodological approach is presented that allows the use of integrated complexity assessment technologies including the use of analysis tools from other scientific fields. It has been inferred that further investigating the solutions of the problem is reliable on the basis of different approaches.

A Methodology for Clustering Items with Seasonal and Non-seasonal Demand Patterns for Inventory Management

Item clustering has become one of the most important topics in terms of effective inventory management in supply chains. Classification of items in terms of their features, sales or consumption volume and variation is a prerequisite to determine differentiated inventory policies as well as parameters, most common of which is service levels. Volume classification is easily obtained by well-known Pareto approach while coefficient of variance is usu-ally used for variation dimension. Hence, it is not always applicable to classify items under different product families with different demand patterns in terms of variation. In this paper, we propose two algorithms, one based on statistical analysis and the other an unsupervised machine learning algorithm using K-means clustering, both of which differ-entiate seasonal and non-seasonal products where an item’s variation is evaluated with respect to seasonality of the product group it belongs to. We then calculate the efficiency of two proposed approaches by standard deviation within each cluster and absolute difference of percentage of volume and item numbers. We also compare the outputs of two algorithms with the methodology which is based on coefficient of variance and is currently in use at the company which is a leading major domestic appliance manufacturer. The results show that the statistical method we propose generates superior outputs than the other two for both seasonal and non-seasonal demand patterns.

Classification of medicines and materials in hospital inventory management: a multi-criteria analysis

In the hospital environment, to achieve an optimum level of operations and service, it is necessary to develop adequate inventory management system. Stocks can be managed, amongst other ways, through inputs classification, which is generally carried out based on a single criterion, such as monetary value, demand or criticality, which does not fully address the complexity of a hospital's inventory management system. Thus, the present study proposes a multi-criteria decision support model to help classify the stock of medicines and materials, enabling a more effective inventory management system for hospitals. Methodologically, the study followed 3 stages: (1) preliminary phase; (2) modelling and choice phase; and (3) finalization phase. Each stage had a set of specific steps that were followed. The first stage identified the actors of the process, objectives, criteria and alternatives, establishing 5 criteria and 48 alternatives; the second stage was the choice and execution of the multi-criteria decision method to solve the problem. It was decided to use the Flexible and Interactive Tradeoff method for the sorting problematic. Finally, in the third stage, the sensitivity analysis for the developed model and the validation of the results with decision makers were carried out. In the study, 48 medicines and materials were included to validate the proposed model; however, the model could be used for more items. From the total of 48 medicines and hospital medical materials selected for the study, the classification of 34 of these alternatives to a single class was obtained through modelling and the other 14 alternatives were destined to two possible classes; moreover, the sensitivity analysis performed showed robust results. The items classified in class W should receive special attention by the stock manager; therefore, they should be monitored weekly. Items classified in class B should be monitored biweekly and finally, items classified in class M, should be monitored monthly. The classification of medicines and materials developed according to the inventory demands allowed more efficient purchasing decisions, optimizing the stock of materials and medicines at the hospital while optimizing the inventory manager's activities, saving time. Consequently, the proposed model can support the development of other multicriteria models in different hospital scenarios.

REDUCING INVENTORY LEVELS IN A SUPERMARKET BY THE SIX SIGMA METHOD

One of the most active sectors in an increasingly globalized world, retail sector has become a rapidly developing sector in Turkey as well. Companies in this sector must follow an inventory policy that provides them with minimum cost and maximum profit in order to compete and survive in market. For this reason, effective management of inventory is crucially important in the retail sector, as in other sectors. In this study, Six Sigma method was used in order to reduce the inventory levels in a large supermarket operating both wholesale and retail in Turkey. Six Sigma is a management tool that emphasizes continuous improvement as a culture and increases the efficiency of processes. In this study, the causes of high inventory levels were determined by following the steps of the Six Sigma method, and the collected data were analyzed. At the end of the implementation, some improvement actions were planned to reduce inventory levels, and a monitoring system was established to ensure sustainability. There was a deficiency in inventory tracking and demand forecasting processes. The main reason for this was seen to be the purchasing organization. It was determined that sometimes more than necessary purchases were made. Six sigma studies on supermarkets in Turkey are limited. This study sets an example for the use of the Six Sigma method, which is a systematic process improvement method for the retail industry.

DEVELOPING SOFTWARE TO SOLVE CERTAIN PROBLEMS OF INVENTORY MANAGEMENT

In modern conditions of intensifying competition in consumer markets, stable operation of enterprises is ensured by the implementation of strategies for maximum satisfaction of consumer demand for goods and related services. Planning such strategies requires improvements in various enterprise systems, in particular, the inventory management system based on the rationalization and optimization of product flows, use of appropriate models and methods for these tasks. Every company strives to satisfy the customer at the highest possible level. This task requires a significant investment in inventory. At the same time, an equally important task is to find new opportunities to reduce all types of costs and increase profits, therefore, from the point of view of finance, the ideal state is when there is no stock while the production needs of the company are met in full. But such a situation is absolutely impossible in real conditions. In order to work as efficiently as possible and reach acceptable financial indicators, it is necessary to effectively manage inventory stock at the enterprise. An essential feature of inventory modeling process is uncertainty in real conditions, which is associated with the inaccuracy or incompleteness of information about demand, supply, time delays of ordered goods, product spoilage and other parameters of the logistics system. This necessitates an effective inventory management mechanism in conditions of uncertainty. Solving this problem requires calculation of exact date and quantity of each subsequent order. Most modern software packages for inventory management are quite difficult to understand or require large financial expenses. In addition, most practical applications are designed for deterministic parameters of demand and production schedule. In fact, demand has a high level of uncertainty, which requires improved algorithms using stochastic theory of inventory management. Thus, a number of issues related to this field still remain unresolved or incompletely resolved. Most publications consider the classic toolkit with some well-known modifications. However, classic models are quite difficult to apply in in real life because the ideal conditions are difficult to achieve in practice. The models do not take into account the limitations imposed by internal and external factors. Based on this, we have created software that would perform the required task. The final product of the research is a software product that enables warehouse operators and managers to optimize drafting a schedule of production inventory deliveries in conditions of demand uncertainty, as well as controlling deliveries based on an economic-mathematical model of inventory management in conditions of demand uncertainty. The software is able to determine the optimal period of delivery of several types of resources, total costs for storage and ordering, the effect of the obtained savings, speeding up and simplifying the dispatcher's work.

Inventory management of perishable products with fixed shelf life for a single echelon system

Perishable goods play an important role in our daily lives as well as in the retail industry. For products such as bakery goods, inventory optimization is very much important as it has a shorter shelf life and uncertain demand due to dynamic consumer behaviour. Baked products are as popular as they've ever been, however, consumer demand is shifting in fascinating ways. Consumers are more conscious about their health than ever before and their demand for fresh foods has skyrocketed. There lies the importance of requirement of an effective inventory management system in the bakery sector to provide fresh products reducing the wastage. Mathematical formulation is framed for a single echelon system considering only the retailer with bread as an example. Two methods of inventory management are being compared, in the first one, the entire inventory is available in rack on the beginning of the day, whereas in the second case a reservation policy is being introduced. The best base-stock level of bread to be kept by the retailer for the above two cases are tested with two ordering policies namely order-up-to-S and (s, S) policies. Demand streams are generated following uniform distribution, as it gives equal probability over a given range.

Application and Importance of Machine To Machine Communication in Manufacturing Industry

In this modern world technologies have become one of the integral parts of the businesses as this has allowed the companies to stay in the running in the competitive business world. Machine to machine communication technology helps the business to boost their productivity and efficiency of the entire business operation. Apart from that with the assistance of this technology companies ensure effective inventory management and better shipment track that in turn facilitates competitive advantages to the companies. Not only that this also helps the companies to run a cost effective business by managing their resources effectively. This entire study showcases the importance and application of machine to machine communication in the manufacturing industry with the help of right methods and techniques. Secondary data has been gathered from reliable and authentic sources and qualitative research design and inductive approach has also ensured that the research is on the right track. From the results it is evident that M2M communication technology has a great impact on the business and this makes the business into smart business. Keyword : Business, Efficiency, M2M Communication, Productivity.

Fight inventory shrinkage: Simultaneous learning of inventory level and shrinkage rate

In 2020, inventory shrinkage eroded $61.7 billion profit in the U.S. retail industry. Unfortunately, fighting inventory shrinkage to protect retailers' already slim profits is challenging due to unknown shrinkage rates and invisible inventory levels. While the latter has been studied in the literature, the former has not. To deal with this challenge, we introduce two new features to the Bayesian inventory models: (1) interleaving customer and theft arrival processes that contribute to actual sales and shrinkages, respectively, and (2) learning of both inventory level and shrinkage rate. We first derive the learning formulae using the triple‐censored sales data (invisible lost sales, shrinkages, and “lost shrinkages”) and then use them to construct a POMDP (partially observable Markov decision process) model for making inventory and loss prevention decisions. For a different level of information deficiency, we analyze the model property and design heuristic order policies to capture the benefit of learning. Through a numerical study, we show that our estimated shrinkage rate converges quickly and monotonically to the actual value. For products with high shrinkage rates (5–12%), our heuristic policy can help seize 82–94% of the ideal profit retailers could earn under full information. We note that feature (1) of our model is crucial. It not only reflects the actual arrival order but also allows us to learn the unknown shrinkage rate, which, in turn, can prevent serious underordering and vicious inventory cycles and can increase the profit by 108% in some cases. Our approach thus enables both effective inventory management and early identification of ineffective loss prevention strategies, reducing shrinkage, and increasing sales and profit.

Vendor-managed inventory (VMI) deteriorating item model taking into account carbon emissions

This paper investigates the main activities that lead to carbon emissions in a deteriorating items supply chain. This study applies a vendor-managed inventory (VMI) model for deteriorating items with a single-setup-single-delivery (SSSD) policy and a single-setup-multiple-delivery (SSMD) policy. We provide constructive managerial insights to both researchers and industries in their inventory management decisions in minimizing the total deteriorating items cost and the total carbon emissions. A numerical example is developed to optimize the delivery quantity and the number of deliveries per production cycle. The results enable companies to identify an effective inventory management critical to reducing carbon emissions, especially for deteriorating items.

Inventory Management Practices in Manufacturing Industries during the COVID 19: A review

This paper focuses on effective inventory management procedures and techniques that can be incorporated in manufacturing sectors. Inventory acts as a backbone of every business operation. It serves as a link between distributors and production line, but because of the COVID -19 pandemic and lockdown imposed, it has disrupted industry operations and also seen disruptions in the availability of raw materials due to lack of supplies from outside vendors. There is a huge impact on production of goods due to delay in supply of raw materials. These disruptions have made companies to have a clear and efficient management of their inventories. This study focuses on several research that suggest various methods and techniques which industries adopt such as ABC, JIT, FSN etc. to manage their inventory in an effective manner and to suggest the best methods that can be used in the tough times of pandemic. Also, this study focuses on different case studies that were carried out in various industries and tries to conclude a best strategy that can be followed during these tough times of the pandemic to maintain a better and optimum inventory without disturbing the company’s production and supply of finished goods.

EVALUASI PENGENDALIAN INTERNAL SISTEM PENJUALAN TUNAI MENGGUNAKAN METODE CONTROL MATRIX

Cash sales are one of the important activities in a company to ensure the continuity of the company's business. For this reason, an internal control system is needed that can make cash sales activities more effective and efficient. The purpose of this study is to determine the implementation and evaluation of the cash sales internal control system at the Sana Farma 2 Pharmacy. The method used is a qualitative method. The technique used in data collection is interviews, observation, and documentation which is strengthened by a questionnaire. The data analysis tool used is the control matrix based on the COSO (Committee of Sponsoring Organizations) framework. The results of the research show that the implementation of the cash sales internal control system at the Sana Farma 2 Pharmacy has fulfilled several internal control objectives, although it is not optimal because there are still several elements that have not been implemented. We recommend that Apotek Sana Farma 2 correct the shortcomings of the internal control system for more effective inventory management.
 Keywords: Internal control system, cash sales, control matrix, COSO.