ابزار گرافیکی برای برنامه ریزی تولید در صنایع متوسط ​​و کوچک (SMIs) مبتنی بر تجزیه و تحلیل تنگنا

Small medium industries (SMIs) routinely face supply variations in their production cycle. Such firms are typically characterised by limited resources and insufficiency of funds. Thus, SMIs need simple solutions to cope with the production planning issues. Pinch analysis has been proven as a strategy for planning of efficient use of scarce resources. Recently, it has been extended to various production planning problems. In this paper, a simple novel graphical approach is proposed to address two common production planning problems in SMI's, which are warehouse space allocation and production capacity planning. Two industrial case studies are shown in this paper to illustrate the proposed approach.

Most companies, especially small medium industries (SMIs), face seasonal demand and production problem, which lead to the emergence of lean and peak periods. Such variation in demand and supply is normally beyond the manufacturer's control. In current practice, the approach to the problem is to plan based on production capacity, machine capacity or storage space, in order to accommodate the varying demand. Production planning issues are often addressed through the use of mathematical model or systematic quantitative decision support techniques. For example, linear optimisation models are used for multi-site production environment [1] and [2]; while Gunther et al. [3] integrates production planning and worker training considering machine and worker availability, operation sequence and multi-period planning horizon. Besides, analytical approaches where uncertainties are described as probability distributions have also been presented [4]. Various models and techniques for production and distribution are recently reviewed by Fahimnia et al. [5]. However, most of the techniques require specialised technical knowledge that may not always be available in the staff roster of SMIs. Therefore, simple, intuitively appealing techniques are required to aid in production planning and coordination especially in SMIs. Furthermore, in many cases, graphical displays may be useful to facilitate decision-making (as evidenced, for instance, by the ubiquitous use of Gantt charts). Pinch analysis techniques have been widely used as systematic design tools in the chemical process industry over the past decades. The techniques were initially developed for the design of heat-recovery systems for industrial energy conservation [6]. This methodology was later extended into various mass integration techniques [7], [8] and [9] for efficient use of mass separating agents for pollution prevention. Later, pinch analysis techniques were developed for general resource conservation networks, focussing on efficient use of process water [10], [11], [12], [13], [14], [15] and [16] as well as industrial gases [11], [14], [16], [17] and [18]. Aside from the traditional applications, pinch analysis has also been extended into a variety of non-conventional areas such as financial management [19], supply chain management [20] and [21], “emergy” analysis [22], carbon-constrained energy planning [23] and [24], carbon capture and storage [25], [26], [27] and [28], short-term scheduling of batch processes [29] and human resource planning [30]. However, there has been very limited work on the use of pinch analysis in the area of production planning. An earlier attempt to use pinch analysis approach for production supply chain has been developed by Singhvi and Shenoy [20]. The authors later extended the work to cover for multiple products manufacturing [31]. In these earlier works, graphical targeting tools are proposed to determine the minimum production rate for known customer demand across a given planning horizon. Subsequently, Ludwig et al. [32] extended the techniques for production with seasonal demand, where different production strategies were evaluated based on cost parameters. The work also presented a compromise between inventory and capacity adaptation costs. Foo et al. [21] in turn proposed an algebraic-equivalent tool to determine the minimum production rate, and incorporated the consideration of maximum and minimum inventory limits. In these previous works, the main objective is to determine the production rate based on the seasonal forecast for the planning horizon. However, none of the above techniques address the minimisation of outsourcing of resources, nor do they explore opportunities for operational changes. The underlying principle of pinch analysis is the use of information about stream “quantity” with data about the stream “quality” of those systems to optimise the overall system. In the context of pinch analysis, “quality” refers to any quantitative index that imposes directionality on the transfer of streams. Depending on the application, stream quality may be defined by key process variables such as temperature for heat recovery [6] and concentration for material recovery [16]. In many management extensions of pinch analysis, time is used as the quality index [20], [21], [29], [30], [31] and [32]. On the other hand, “quantity” refers to the amount in the transfer streams; for instance, enthalpy for heat recovery [6], and mass flowrate in mass recovery [16], etc. Once the quantity and quality aspects of a system are identified, graphical or algebraic targeting tools are used to determine the system performance based on first principles, e.g. mass and energy balances. In all cases, the targeting tools provide good insights (as compared to other tools such as optimisation techniques), which can be used by the users to identify areas for improvement for a given problem. In this paper, two novel graphical tools for production planning are proposed. These newly proposed tools can be used to optimise warehouse space and production capacity for production machinery, which are common challenges in most SMIs. The problem arises when a company/facility seeks to maximise use of its internal resources, such as storage space or the capacity of its machinery, while minimising outsourcing requirements. This goal is equivalent to reducing idle space and/or excess machines capacity. Note that the problem is more serious in SMIs as compared to large industries due to the typical lack of access of such firms to adequate financing for expansion of facilities. Thus, SMIs often simply rent readily available industrial space for storage, and likewise purchase off-the-shelf equipment that are readily available in the market. Therefore, proper production planning is extremely important for SMIs to survive in the market. Furthermore, production planning in such companies is often done in a relatively ad hoc manner by production staff, who may not have the adequate technical training to use sophisticated planning techniques. Therefore, simple graphical tools that are appealing and intuitive in understanding the key planning issues are needed. With these newly developed tools, SMIs will be able to handle the production planning problems more effectively.

Novel graphical pinch targeting tools have been developed for production planning problems. These methods are then illustrated with two actual industrial case studies, where it is shown that the newly developed tools provide good quantitative understanding of production planning problems. They help to maximise the use of company resources, such as inventory or equipment, by minimising idle company resources, which at the same time reduces the need for outsourcing storage or production. The graphical format is also useful for management meetings, technical presentations, communication and visualisation purposes by non-engineers, decision makers or stakeholders, to whom the details of optimisation is hard to manage. They provide visual insights that may not be readily grasped when using mathematical programming tools. The proposed techniques are user-friendly and may be done by using simple spreadsheet software. Future work should explore the potential use of pinch analysis in parallel with detailed planning techniques, where it can play the same role as simplified auxiliary models for providing decision-making insights [35].