ارائۀ مدلی برای شناسایی و تعیین خوشه‌های همکاری افقی در هلدینگ‌های صنعتی

موفقیت و اثربخشی هلدینگ‌ها، به ارزش‌آفرینی شرکت‌های تابع و هم‌افزایی بین ستاد و واحدهای آن وابسته است. راه دستیابی به هم‌افزایی در هلدینگ‌ها برقرارکردن روحیۀ همکاری بین اجزاست. در دهه‌های اخیر، همکاری، محور اصلی بهبود عملکرد زنجیره تامین قلمداد شده است و ارتباطات بین ‌سازمانی به‌عنوان واقعیتی انکارنشدنی و یکی از چالش‌های سازمانی مطرح است. با توجه به ماهیت پیچیده و پویای بازارها علاوه بر همکاری عمودی و درونی به همکاری افقی نیز نیاز است؛ به‌ویژه تسهیم ظرفیت بین شرکت‌های تولیدکننده‌ای که در سطوح مشابه رقیب نیستند، سودآوری و پایداری را برای آنها به ارمغان می‌آورد. هدف این پژوهش، ارائۀ مدلی برای شناسایی و تعیین خوشه‌های همکاری افقی در هلدینگ‌های صنعتی است. با توجه به بافت داده‌ها، مدلی کمّی بر مبنای شباهت کسینوسی و الگوریتم k میانگین برای خوشه‌بندی محصولات تولیدی مشابه در یک هلدینگ دارویی استفاده شده است. محصولات تولیدی در 8 خوشه جای گرفته و هر خوشه به یک شرکت نسبت داده شده است. علاوه بر این، فاصلۀ کسینوسی اجزای درون خوشه‌ها پیش و پس از اجرای خوشه‌بندی با یکدیگر مقایسه شده است. نتایج نشان می‌دهد میانگین فاصلۀ محصولات پس از خوشه‌بندی بسیار کاهش یافته؛ به‌گونه‌ای که این فاصله به کمتر از نصف رسیده است. این امر، افزایش شباهت محصولات قرارگرفته در هر خوشه را نشان می‌دهد و می‌توان استدلال کرد که خوشه‌بندی انجام‌شده، کیفیت مناسب دارد؛ از این‌رو، همکاری افقی، که ابزار مهمی برای مدیریت کسب‌وکار برای بهبود توانایی رقابت سازمان است، محقق می‌شود و شکاف بین منابع موجود شرکت و الزامات مورد نیاز آیندۀ آن را پر می‌کند و با ارائۀ دسترسی سازمان‌ها به منابع بیرونی با ایجاد هم‌افزایی و ترویج یادگیری و تغییر سریع، رقابت‌جویی سازمان‌ها را افزایش می‌دهد.

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

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 subcriteria 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 longerlasting 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 costeffectiveness, 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 shortterm perspective. Therefore, such cooperation may be unstable. The desired time horizon is longterm in clustering. Hence, the designated partners can consistently continue their cooperation in the long run.