制造专业人员不断努力改善其组织的财务业绩，但通常要获得与运营资源和流程有因果关系的财务信息。对于每天或每周做出常规改进的许多较小和每周做出的较小和渐进的决定，这场斗争是最激烈和令人困惑的。
The primary problem is that financial data normally is far from being as granular and timely as operational data. The future is arriving, however. If manufacturing professionals demand, the future could include financial data that is responsive to and supportive of operational decisions in real time.
Picture this scenario: An Internet of Things (IoT) sensor picks up an equipment performance anomaly. The anomaly is transmitted to the manufacturing operations management (MOM) system, other sensors are instantly queried, and the anomaly is evaluated as having a significant—but not imminent—probability of failure if maintenance is not performed.
There are several options to choose from: Stop the line now for maintenance, wait a few hours to see if a line stoppage occurs for other reasons, wait until the end of the shift to fix the issue, or wait for failure. Which course of action is most cost-effective? Each scenario involves a different set of costs for lost production, extent of damage, repair parts, amount of downtime, product quality impacts, late deliveries, etc. What is needed is a financial system that can rapidly provide the costs of each scenario, use the MOM calculated or judgmental probability of failure for each scenario, and provide the risk assessment with cost impacts to support management’s decision-making.
妈妈的功能系统和物联网传感器are typically available within the manufacturing system. The linkage to such granular financial data and the integration between financial and operations systems is much more novel. Even more unique is financial data that comes from a system or data source agreed upon by both finance and manufacturing. How can this type of coordination and financial data be made a reality?
First, finance needs to recognize that “one version of the financial truth” is not a reality. Truth for external financial reporting is found in regulatory accounting standards. Truth for internal decision support must be based on causality, the cause-and-effect relationships between resources, processes, and the intermediate and final outputs. This is known as a managerial costing model.
其次,必须建立成本模型,反映了the operational model—without the distortions caused by external financial reporting standards. This means cost data is collected that reflects the use of resources. The nature of the consumption (and cost) relationships, normally fixed or proportional, must also be clearly reflected. This will allow marginal and incremental costs to be rapidly calculated. The term “reflect” is used as an analogy to the clarity of a mirror.
第三，当需要做出决定时，必须可用管理成本数据，并且必须在整个组织中得到信任。没有时间进行特别研究或分析；而且，决策者必须确信对此费用数据做出的决定不会导致以后受到质疑。
The cost modeling to create this type of model is not part of many finance and accounting departments’ portfolio of skills and abilities. Manufacturing professionals will need to push for this type of information. However, advanced costing methodologies such as resource consumption accounting (GPK) and quantity-based, pull-oriented activity-based costing have been around for decades.
注意：本专栏是由Siemens的MOM软件和Mindsphere平台和Alta通过Consulting的Proeo Managerial Cofting软件的启发，作为西门子MOM专业知识中心（MEAC）倡议的一部分。查看上面的图形和一个presentation from Alta Via and Siemens。
>> Larry White，CMA，CFM，CPA，CGFM，lwhite@rcainstitute.org, is executive director of theResource Consumption Accounting Institute，培训和倡导者改善了将运营连接到业务绩效的成本信息。
