中小企业精益生产路径探析(中英文翻译).doc

Quantifying Energy Savings from Lean Manufacturing Productivity The rational for claiming energy saving comes from comparing the baseline energy us to the post-event energy use. In the baselin scenario, production is increased by addin manufacturing equipment or by extendin operating hours. As such, in the baselin scenario, energy use increases from existing us due to additional equipment or operating hours. Alternately, in the post-event scenario, Lea Manufacturing techniques enable production gains without increasing operating hours o adding manufacturing equipment. Hence in th post-event scenario, energy use is not muc different that existing energy use, and much les than in the baseline scenario. Currently, claiming energy savings fro productivity improvements is not widespread among energy efficiency programs or Lea consultants. Indeed, little has been published o how energy savings should be quantified fro these improvements. In this paper we present brief review of the existing algorithms used t calculate energy savings from productivity improvements. Furthermore, we propose tw new approaches to calculating energy saving each with examples. EXISTING APPROACHES TO CALCULATING PRODUCTIVITY RELATED ENERGY SAVINGS Department of Energy Industrial Assessment Centers The Department of Energys (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Industrial Technologies program sponsors 26 Industrial Assessment Centers (IAC) located at universities throughout the nation. The IACs provide energy, waste and productivity assessments free of cost to qualifying small and medium sized manufacturing facilities. Each IAC must report expected energy, waste and productivity savings from its assessment recommendations. IACs may claim “effective” energy savings from productivity recommendations. The approach to calculating energy savings is based on comparing pre and post-implementation energy intensity and multiplying the difference by post-implementation production rates (Papadaratsakis, et al., 2003.). While this approach has been published in detail, we will present an abbreviated example here. For example, a production process currently uses 10 kWh/unit. After implementation of Lean Manufacturing recommendations, the process only uses 8 kWh/unit, and production has been increased to from 1,000 to 1,200 units/year. Equation 1 presents the effective energy savings from this example: (1,200 units/year) x (10 8) kWh/unit = 2,400 Kissock (2005) of the University of Dayton IAC developed a similar but more detailed method. While Papadaratsakis et al. only considered unitized energy use, Kissock discussed unitized energy use (energy intensity) and net energy use. Kissock also used inverse modeling of regression models based on real data to determine the unitized energy for production quantity-dependent, temperature-dependent and operating hours dependent components of energy use. Kissock also recognized the influence of product demand on energy savings, showing different net and unitized energy savings dependent whether production quantity actually increasingly or not. Northeast Utilities Process Reengineering Improvement for Manufacturing Efficiency (PRIME) Northeast Utilities (NU) sponsors the PRIME program through two of its member utilities, Connecticut Light & Power (CL&P) and Western Massachusetts Electric Company (WMECO). The PRIME program is solely focused on productivity improvement to achieve energy savings. Lean Manufacturing consultants are contracted to provide three to four day “Lean events”. As the Lean consultants do not necessarily have energy efficiency expertise, energy savings are calculated with a generalized algorithm. The NU savings algorithm relies on a small number of easily obtained and understandable inputs, which influence savings calculations and will vary significantly from site to site. Other factors that are not easily obtained may be timelier and costly assessment to calculate, may not vary significantly, and are thus substituted with generic assumptions. NUs algorithm requires the following inputs: 1. Annual plant-wide electricity use 2. Percent affected electricity use: Many of the PRIME events do not target the entire manufacturing facility, but perhaps just one production line. As such, determining how much of the total annual electricity use is attributable to the production lines in question is an important estimate, typically based on production values or sales. 3. Pre- and post-event production quantity. In addition to these three inputs, standard assumptions applied to each site include: 1. 10-year measure life: This value is used to onvert annual savings as determined by the savings algorithm to lifetime savings. 2. 85%/10%/5% energy distribution assumption: The savings algorithm assumes that of the affected electricity use, 5% is attributable to equipment with no claimable savings. 10% of electricity is attributed to operating-hour dependent manufacturing equipment. The remaining 85% of affected electricity use is attributed to production quantity dependent manufacturingequipment. 3. Savings on incremental production: The algorithm assumes that 6% energy savings are achieved only on incremental energy use of production-dependent equipment. The existing, baseline and post-event electricity use is calculated for independent, hours-dependent and production-dependent components. Office electricity use is assumed constant for the existing, baseline and post-event scenarios. Hours-dependent electricity use is assumed to increase proportionally with production from the existing to baseline scenarios. However, in the post-event scenario,hours-dependent electricity use is assumed to beequivalent to the existing scenario. Finally,production-dependent electricity use increasesproportionally with production from the existing to baseline scenarios. However, post-event electricity use is calculated assuming that incremental production is 6% less energy intense than in the baseline scenario. NEW APPROACHES TO CALCULATING PRODUCTIVITY RELATED ENERGY SAVINGS Lean Manufacturing Techniques and Improvement Types There are a variety of techniques referred to as Lean Manufacturing. A Lean Manufacturing project may utilize any number of these techniques, with the different techniques affecting productivity and thus energy use in different ways. While the implementation of a Lean technique often improves productivity, however, it does not guarantee a productivity improvement. Lean Techniques that may increase production include 5S, Visual Management, Standardized Work, Value Stream Mapping (VSM), Cellular flow, kanban, Poka Yoke,Point-of-Use (POU) systems and Kaizen events.Lean Techniques that directly target production increases include Quick Changeover and Total Productive Maintenance (TPM). Seryak et al.(2006) briefly described these Lean improvement types and their relationship to energy use. The Lean Manufacturing techniques listed above improve productivity in several ways,which may or may not have impacts on energy use. Additionally, Lean Manufacturingtechniques can also improve energy use in ways that have no relation to productivity. Improvement types that affect energy use include reduced changeover time, reduced downtime, reduced setup time, reduced cycle time, increased throughput, rework/scrap reduction, part travel reduction, space reduction and direct equipment efficiency improvement.Improvement types that do not typically affect energy use include inventory reduction. Basis for Both Algorithms Both of our new approaches begin with the same generating equation (Eq. 2):Energy Savings This general equation can be used with statistical regression models, which we will refer to as the “Energy Signature” method. The same equation is the basis for considering the energy use of the specific industrial equipment involved,which we will refer to as the “Energy Breakdown” method. Examples of both methods are presented below. Energy Breakdown Method The Energy Breakdown method involves calculating the energy savings for each piece of electricity-using equipment. The main steps used in the Energy Breakdown method are: 1. Develop an inventory of energy using equipment. 2. Determine how each piece of equipment uses energy. 3. Quantify existing energy use for each piece of equipment, based on pre-Lean event production. 4. Calculate baseline energy use for each piece of equipment, based on post-Lean event production and pre-Lean event processes. 5. Calculate post-event energy use for each piece of equipment, based on post-Lean event production and post-Lean event processes. 6. Compare post-event to baseline scenarios to calculate energy savings. The details of energy savings calculations using the energy breakdown method differ depending on which improvement type is in question. As such, like methods will be explored for the Energy Breakdown method. The relationship between equipment energy use and production differs based on the type of equipment. There are four main categories of equipment: Equipment with energy use independent of production (includes office equipment), Equipment with energy use dependent on production quantity, Equipment with energy use dependent on operating hours, Equipment with energy use dependent on both production quantity and operating hours. For example, an exhaust fan that operates 24 hours/day for a two-shift operation will use the same amount of energy no matter if production quantity or production hours increase. The exhaust fan is an example of equipment with energy use independent of production factors. Next, imagine dedicated production presses that ully shut off during idle cycle times. This equipment uses energy directly proportional to production quantity, regardless of the operating hours. However, lighting equipment for this same operation may be shut off on weekends, and is thus dependent on operating hours. Dedicated production presses that do not shut down, but instead idle, would have energy use dependent on both production quantity and operating hours. Inventory Reduction and Space Reduction In some cases an inventory reduction could esult in a reduction in space use. Space use can also be reduced for other reasons, such as earranging equipment during a Cellular Flow project. Reducing space use can have energy savings, provided the lighting and air conditioning equipment in the eliminated space can be turned off or reduced. To calculate energy savings, lighting, air-conditioning and other equipment should be inventoried, with power equirements and existing runtimes detailed. For example, imagine a small warehouse lluminated by ten 400-W Metal Halide fixtures, drawing 460-Watts each that operates 20 hours per day, and is ventilated by two 5 HP fans that operate 24 hours per day. The first step for calculating energy savings would be to inventory equipment, as presented in Table 1. In this paper weve outlined the conceptual framework for claiming energy savings from productivity improvement projects. The relationship between production and manufacturing energy use has been reviewed on a plant-wide level and for specific manufacturing equipment. Weve suggested four categories of manufacturing equipment, based on their relationship between production and energy use. Common Lean Manufacturing improvements were discussed, along with their affect on energy use and the techniques used to achieve them. Finally, we described several existing energy efficiency programs which either promote productivity improvements or claim energy savings from productivity improvements. As state and utility energy efficiency programs expand and broaden, reducing manufacturing energy intensity remains a promising opportunity to achieving energy savings. Weve shown how encouraging productivity improvements are philosophically similar to existing “New Construction” or “Lost Opportunity” programs. The major obstacle to achieving savings through productivity improvement is a combination of market factors, which affect production levels, and non-Lean Energy use in the facility. As shown, without Lean Energy, manufacturing facilities will require the same amount of energy use as pre improvement. Coordinating Lean Manufacturing events with Lean Energy assessments could improve the persistence of savings from productivity improvements. 中小企业精益生产的实施路径探析对节能的需求源自我们实际生产与能量使用基准的差别。在基准情况下，产量增加是通过增加生产设备或延长工作时间来获得的。这种情况下，即使在基准情况下，由于设备的增多和工作时间的延长，能量消耗也将随之增长。或者在事后，我们通过精益生产方法，使我们在没有增加工作时间和增多设备的情况下，依然能够保证足够的产量。因此，在这种情况下，能量的使用和前者并没有什么不同，甚至比基准的能量使用更少。目前，通过提高生产率来节约能源并没有在能源效用项目和生产咨询方面得到广泛应用。事实上，鲜有企业出版关于从这些改进中来量化节能的刊物。本文简要回顾了现有的估算方法，便于能量使用量的计算。另外，我们也提出新的方法去估算每个实例中的能量使用情况。目前用于估算涉及节能的生产效率的方法由能源工业评估中心作出。该部门以及由其发起的26个工业评估中心坐落在贯穿整个国家的大学里。IACS提供能源、废物和生产力的评估成本和为中型企业提供生产设备。每个IAC必须从评估的报告中提供预期的能源、废物和生产力的节省情况。IACS可以从生产建议中提出有效的能源节约方案，这种方案是基于和以前的能源实施后的强度，再乘以实施后的生产速度。虽然这种方法已经详细地出版，我们现在只需一个简化的例子即可。例如，生产以单位需要10千瓦时，采纳精益生产方法后，变成了8千瓦时，并且年产量由1000上升至1200，从这个例子中看出了有效的节能方法：1200单位*（10-8）千瓦时/每单位=2400。戴盾大学的吉斯索克开发出一种类似的、却更详细的方法。而帕帕戴瑞特斯基斯等人只考虑了整体能源的使用，吉斯索克讨论了能源使用（能源强度）和净能源使用。吉斯索克也使用逆向回归模型，该模型给予真实的数据，以确定单位能源的产量、温度依赖性和单位能量的使用时间。吉斯索克也认识到节能产品需求的影响，不论产量增长与否，都显示出了对节能的不同程度的依赖：东北公用流程流程提高生产效率的公司和其赞助企业，康涅狄格店里与氯普照明以及马萨诸塞州西部电器公司。生产节能计划完全集中在提高生产率上达到节约能源。精益生产顾问合同，提高三至四天的惊异时间，作为惊异顾问并不一定具有节能专业知识。 1.全厂每年的用电量。2.用电量受影响的百分数：最初的许多事件不是针对整个制造过程，但或许只是针对一条生产线。因此，生产线的问题是确定全年总用电量的多少一个重要评估，通常是根据产值或销售。3.超前和事件后的生产量。除了上述三个输入，适用于每个站点的标准假设包括：1.10年的生活措施，这个值是有平均每人每年可节省的用电量的得出来的。2节余算法假定的受影响的用电量，5是指没有索赔节省设备。10的用电量是由于工作小时的依赖制造设备所导致。其余85是由于生产数量相关的制造设备所造成。.节余增量生产：该算法假定只对增量依赖生产设备的能源使用实现6的能源节约。现有的基准和事后的用电量是有每小时生产相关元件独立算出的。办公用电量的现有基准和事后的情况假设不变。假定每小时的用电量是在生产比例维持现有的基准。然而，在事后的情况下，假定每小时的用电量不变相当于现有的电量情况下。最终，按照生产用电量的增加与生产比例从现有的基线中算出。然而，用电量的计算是假设增加的产量是是用减少6的能源计算的，按照基准先推测更准确。计算节约生产力相关的能源的新途径精益制造技术和改进类型有一种技术定义为精益生产，精益生产制造项目科可能运用这项技