ارائۀ مدلي براي شناسايي و تعيين خوشه‌هاي همكاري افقي در هلدينگ‌هاي صنعتي

Proposing a Model for Identifying and Determining the Horizontal Collaborative Clusters in Industrial Holdings

موفقيت و اثربخشي هلدينگ‌ها، به ارزش‌آفريني شركت‌هاي تابع و هم‌افزايي بين ستاد و واحدهاي آن وابسته است. راه دستيابي به هم‌افزايي در هلدينگ‌ها برقراركردن روحيۀ همكاري بين اجزاست. در دهه‌هاي اخير، همكاري، محور اصلي بهبود عملكرد زنجيره تأمين قلمداد شده است و ارتباطات بين ‌سازماني به‌عنوان واقعيتي انكارنشدني و يكي از چالش‌هاي سازماني مطرح است. با توجه به ماهيت پيچيده و پوياي بازارها علاوه بر همكاري عمودي و دروني به همكاري افقي نيز نياز است؛ به‌ويژه تسهيم ظرفيت بين شركت‌هاي توليدكننده‌اي كه در سطوح مشابه رقيب نيستند، سودآوري و پايداري را براي آنها به ارمغان مي‌آورد. هدف اين پژوهش، ارائۀ مدلي براي شناسايي و تعيين خوشه‌هاي همكاري افقي در هلدينگ‌هاي صنعتي است. با توجه به بافت داده‌ها، مدلي كمّي بر مبناي شباهت كسينوسي و الگوريتم K ميانگين براي خوشه‌بندي محصولات توليدي مشابه در يك هلدينگ دارويي استفاده شده است. محصولات توليدي در 8 خوشه جاي گرفته و هر خوشه به يك شركت نسبت داده شده است. علاوه بر اين، فاصلۀ كسينوسي اجزاي درون خوشه‌ها پيش و پس از اجراي خوشه‌بندي با يكديگر مقايسه شده است. نتايج نشان مي‌دهد ميانگين فاصلۀ محصولات پس از خوشه‌بندي بسيار كاهش يافته؛ به‌گونه‌اي كه اين فاصله به كمتر از نصف رسيده است. اين امر، افزايش شباهت محصولات قرارگرفته در هر خوشه را نشان مي‌دهد و مي‌توان استدلال كرد كه خوشه‌بندي انجام‌شده، كيفيت مناسب دارد؛ از اين‌رو، همكاري افقي، كه ابزار مهمي براي مديريت كسب‌وكار براي بهبود توانايي رقابت سازمان است، محقق مي‌شود و شكاف بين منابع موجود شركت و الزامات مورد نياز آيندۀ آن را پر مي‌كند و با ارائۀ دسترسي سازمان‌ها به منابع بيروني با ايجاد هم‌افزايي و ترويج يادگيري و تغيير سريع، رقابت‌جويي سازمان‌ها را افزايش مي‌دهد.

Purpose: Sharing capacity among the manufacturing companies, which are not competitors at the same level, can increase their profit and sustainability. This study aims to propose a model for identifying and determining the horizontal collaborative clusters in industrial holdings. Design/methodology/approach: To achieve the mentioned clusters in the pharmaceutical industry, first, the affecting criteria were determined based on the literature review, and according to the opinions of managers, supervisors and pharmaceutical holding experts. Then, the related dimensions of each sub-criteria were specified and the common products of the companies were grouped into eight clusters. In this step, each drug was assigned to a cluster of which, the characteristic vector had the shortest distance from it. In the following steps, the obtained clusters were assigned to the companies. For this purpose, the similarity of clusters and companies were measured and each cluster was assigned to the most similar company. Finally, the quality of clustering was measured using cosine similarity, by calculating the distance between the company's products in pairs. The average value of such distances was assumed as the representative value of the product distance in the clusters. Findings: Pharmaceutical products were categorized into eight clusters, and each one was assigned to a company. Also, the components’ cosine distance inside each cluster was compared, before and after the clustering. The results indicated that the mean distance of products decreased after clustering. Since the similarity of the products in each cluster was increased, it seems that the clustering was sufficiently qualified. Research limitations/implications: One of the main limitations of this study was the lack of a consistent standard in the information provision of companies. Another limitation was the lack of transparent information provided by companies and the time required to collect data. Also, there was a lack of related studies on how to implement such a method. The lack of cooperation of some experts in completing the questionnaire brought other difficulties and led to a longer-lasting research time. As a future study opportunity, it is suggested to perform a feasibility study on the horizontal collaboration in industrial holdings according to different variables such as holding size (small, medium, large), type of industry (manufacturing and commercial), type of sector (private and public) and legal status (Individual ownership, partnership), market demand and market size. Such study results in determining the extent to which, horizontal cooperation can bring synergy in different situations. Practical implications: Horizontal collaboration is an effective tool for business management to improve the competitiveness of the organization. It also fills the gap between the company's existing resources and future requirements. It also increases the competitiveness of organizations by providing external resources to them through creating synergy, promoting learning, and rapid change. Social implications: Holdings create synergies by establishing cooperation between their subsidiaries. The result will be higher production quality and more cost-effectiveness, which will lead to customer satisfaction. Originality/value: There are a variety of ways for determining a business partner, including game theory and clustering. While in several studies, cooperation among several factories through game theory negotiations has led to relative efficiency, decisions have been made based on a short-term perspective. Therefore, such cooperation may be unstable. The desired time horizon is long-term in clustering. Hence, the designated partners can consistently continue their cooperation in the long run.