scholarly journals Optimizing Inventory Carrying Cost Using Rank Order Clustering Approach for Small and Medium Enterprises (SMES)

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Ganesh Narkhede B ◽  
◽  
Ganesh Narkhede B ◽  
Neela Rajhans ◽  
◽  
...  
Keyword(s):  
Inventory Management ◽  
Small And Medium Enterprises ◽  
Rank Order ◽  
Cluster Formation ◽  
Quantity Discounts ◽  
Sufficient Degree ◽  
Inventory Strategy ◽  
Clustering Approach ◽  
Degree Of Control ◽  
Medium Enterprises

For any company, whether big enterprises or small and medium-sized enterprises (SMEs), inventory is one of the key assets. Therefore, inventory-related decisions directly influence the revenue generated by the firm. This work aims to find a sufficient degree of control over each inventory item and to mitigate the inventory management problems of SMEs. Rank Order Clustering (ROC) algorithm is used in this study for multi-item inventory item aggregation. The proposed framework is tested on a medium-sized gearmanufacturing firm that manufactures 40 different types of planetary and customized gear-boxes. The results demonstrate 47.64 % of cost-saving through the proposed methodology of cluster formation using ROC and quantity discounts. This approach helps to identify different assemblies to aggregate the component requirements and to formulate a particular inventory strategy to minimize inventory carrying costs for each component.

scholarly journals Factors influencing location choice and cluster pattern of manufacturing small and medium enterprises in cities: evidence from Khulna City of Bangladesh

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
S. M. Towhidur Rahman ◽  
Ahsanul Kabir
Keyword(s):  
Small And Medium Enterprises ◽  
Location Choice ◽  
Cluster Formation ◽  
Personal Factors ◽  
Location Selection ◽  
Community Factors ◽  
Multiple Case ◽  
Specific Factors ◽  
Medium Enterprises ◽  
Cluster Pattern

AbstractThis study aims to identify the location and cluster pattern of manufacturing SMEs in Khulna City and the reasons behind such pattern since localisation of SMEs has significant impact on the economic development of cities and regions. A walking survey was conducted to identify the SME location pattern and a semi-structured personal interview was conducted to collect data from manufacturing SME entrepreneurs to find why such patterns exist. A cross section qualitative multiple case study design and pattern matching technique was applied to answer the research questions. The finding of the study shows SME location choice was influenced by a combination of location specific, personal, and community factors whereby personal factors are prioritised over other factors in location selection. While the cluster formation or localisation of SMEs depends on only location-specific factors. The study findings are expected to contribute in urban planning and regional development through providing useful insights relating allocation of spaces for industry development.

scholarly journals The Development of Small and Medium Entreprises Clusters in Indonesia

2004 ◽  
Vol 6 (1) ◽  
pp. 29
Author(s):  
Tulus Tambunan ◽  
Hendrawan Supratikno
Keyword(s):  
Economic Growth ◽  
Development Policy ◽  
Success Factors ◽  
Small And Medium Enterprises ◽  
Empirical Studies ◽  
Critical Factors ◽  
Cluster Development ◽  
Critical Success ◽  
Clustering Approach ◽  
Medium Enterprises

Small and Medium Enterprises (SMEs) in Indonesia are very important not only for employment creation, but also as important sources of economic growth and foreign currencies generation through exports. Since the early 1980s, the Indonesian government has adopted SME development policy through a clustering approach. This paper reviews the existing empirical studies on development of SME clusters in Indonesia. This paper deals with two main questions. First, what are the critical success factors of development of an SME cluster. Second to what extent this policy has contributed to the dynamic of SME clusters in the country. The paper argues that in many cases, the development policy has not been so successful. In essence, most failures can be attributed to the fact that one or more critical factors for successful SME cluster development were either not existing or not addressed correctly. Neglecting cluster linkage to markets is one reason of the failure. Prerequisite for successful cluster development is the cluster's potential to access to growing market, either domestic or abroad.

A Data Warehouse Integration Methodology in Support of Collaborating SMEs

2015 ◽  
pp. 222-242
Author(s):  
Marius Octavian Olaru ◽  
Maurizio Vincini
Keyword(s):  
Graph Theory ◽  
Competitive Advantage ◽  
Data Warehouse ◽  
Experimental Evaluation ◽  
Small And Medium Enterprises ◽  
Heterogeneous Data ◽  
Point Of View ◽  
Strategic Information ◽  
Clustering Approach ◽  
Medium Enterprises

Collaborative business making is emerging as a possible solution for the difficulties that Small and Medium Enterprises (SMEs) are having in recent difficult economic scenarios. In fact, collaboration, as opposed to competition, may provide a competitive advantage to companies and organizations that operate in a joint business structure. When dealing with multiple organizations, managers must have access to unified strategic information obtained from the information repositories of each individual organization; unfortunately, traditional Business Intelligence (BI) tools are not designed with the aim of collaboration so the task becomes difficult from a managerial, organizational, and technological point of view. To deal with this shortcoming, the authors provide an integration, mapping-based, methodology for heterogeneous Data Warehouses that aims at facilitating business stakeholders' access to unified strategic information. A complete formalization, based on graph theory and the RELEVANT clustering approach, is provided. Furthermore, the authors perform an experimental evaluation of the proposed method by applying it over two DW instances.

Small and medium enterprises and industrial clusters in BRICS countries

2020 ◽  
pp. 246-270
Author(s):  
Mario Biggeri
Keyword(s):  
Poverty Reduction ◽  
Small And Medium Enterprises ◽  
Cluster Formation ◽  
Innovation System ◽  
Industrial Clusters ◽  
Important Variable ◽  
Territorial Development ◽  
Brics Countries ◽  
Sustainable Human Development ◽  
Medium Enterprises

Industrial clusters have a prominent role in the innovation system and territorial development of many developed and emerging countries, including BRICS. They represent an important variable not only in terms of GDP growth but also in employment creation and poverty reduction, becoming potential drivers for the country’s development. Given the complexity of the process underlying cluster formation, this chapter analyses the evolution of industrial clusters of small and medium enterprises in BRICS countries in the last two decades. Particular focus is dedicated to the main endogenous and exogenous drivers of change and how these concurred in shaping different outcomes in each country, also in terms of sustainable human development.

scholarly journals Aplikasi Pengelolaan Persediaan Bahan Pada Usaha Kecil Menengah Pakaian Jadi

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Antonius Susanto ◽  
Cecilia Esti Nugraheni ◽  
Maria Widyarini
Keyword(s):  
Inventory Management ◽  
Mobile Application ◽  
Driving Forces ◽  
Small And Medium Enterprises ◽  
Acceptance Test ◽  
Material Requirements Planning ◽  
Reorder Point ◽  
Materials Used ◽  
Medium Enterprises ◽  
A Company

Clothing is one of the basic or primary needs. With the influence of clothing fashion that continues to grow, many Small and Medium Enterprises (SMEs) have sprung up engaged in the clothing sector. SMEs are one of the driving forces of the Indonesian economy. SMEs engaged in the clothing sector are businesses that produce apparel. The materials used in the manufacture of clothing also vary, so that inventory management and material requirements planning are essential processes in the manufacture of clothing. In this study, a simple mobile application was developed that can be used for material inventory management for SMEs. The primary function of this application is to record materials (inventory) and plan production needs using the Reorder Point (ROP) method. The Reorder Point (ROP) method is an order limit point helpful in knowing when a company holds an order. Program development using Android Studio IDE with Flutter framework and Dart programminglanguage. The results of functional testing on this application are 90% achieved following the expected results. The user acceptance test concludes that this application has the potential to facilitate SMEs in managing the inventory of material.

scholarly journals ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING

2018 ◽  
Vol 2 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Beta Estri Adiana ◽  
Indah Soesanti ◽  
Adhistya Erna Permanasari
Keyword(s):  
Small And Medium Enterprises ◽  
Cluster Formation ◽  
Optimal Number ◽  
Customer Segmentation ◽  
Marketing Strategies ◽  
Monetary Analysis ◽  
Data Mining Approach ◽  
Rfm Model ◽  
Customer Services ◽  
Medium Enterprises

Intense competition in the business field motivates a small and medium enterprises (SMEs) to manage customer services to the maximal. Improve of customer royalty by grouping cunstomers into some of groups and determining appropriate and effective marketing strategies for each group. Customer segmentation can be performed by data mining approach with clustering method. The main purpose of this paper is customer segmentation and measure their loyalty to a SME’s product. Using CRISP-DM method which consist of six phases, namely business understanding, data understanding, data preparatuin, modeling, evaluation and deployment. The K-Means algorithm is used for cluster formation and RapidMiner as a tool used to evaluate the result of clusters. Cluster formation is based on RFM (recency, frequency, monetary) analysis. Davies Bouldin Index (DBI) is used to find the optimal number of clusters (k). The customers are divided into 3 clusters, total of customer in first cluster is 30 customers who entered in typical customer category, the second cluster there are 8 customer whho entered in superstar customer and 89 customers in third cluster is dormant cluster category.

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