The information management of enterprises is a long and bumpy truth, and of course there are also sparks of innovation here. Information construction varies with the environment and industry in which the enterprise is located. As a mature enterprise, under the current wave of big data, what constructive ideas do you have for the future informatization? Share the data management practice of a pharmaceutical group here.
The pharmaceutical group's main business includes pharmaceuticals, biological products, medical devices and pharmaceutical health products. Since the construction of informatization started 20 years ago, it has developed along the way. There are dozens of business systems of various sizes, but the core systems are nothing more than business and finance.
However, with the continuous expansion of the business scale, the business format of the company-scale business department has become more and more complex, and the business complexity has greatly increased. Enterprises have begun to pay more attention to the process, and the data of the process has gradually become a core part of the enterprise operation.
A large amount of data has been accumulated during the construction of the information system, which is a valuable resource for traditional pharmaceutical companies, because these data have accumulated business experience, industry data and industry standards. From the perspective of industry informatization, how to make good use of these data has become the key.
The use of data focuses on the process of analysis
In the past few years, the company adopted FanRuan's reporting system FineReport to display data around the company's various business levels. At the same time, some BI attempts have been made, such as products such as SAP. Whether it is reporting or BI, the core goal is to solve the problem of decision-making in enterprise operations. But after building it down, it doesn't seem to support the decision-making process very well when it comes to decision-making and reflection. The main reason is that more attention is paid to the digestion process of historical data. The use of these tools is only for the induction and reorganization of historical data, and for visual display, but this is not the end of data management. What is needed more is the analysis process of historical data. The first thing needed in such an analytical process is tools, and BI provides us with more methods in terms of data mining and forecasting.
Use the process of data analysis to optimize management decisions
The decision-making process is to provide scenarios for analysis. Decision-making can be divided into several levels: the highest level is strategic decision-making, then tactical decision-making, and operational decision-making. Their frequency and impact are different. The impact at the strategic level is very large, but only when the frequency is a 5-year, 10-year or larger cycle will we make a strategic change. Tactical decisions are second. Decision-making at the operational level is made along with the actual problems that may be implemented, encountered and faced in the process of operation.
The process of data analysis is actually a process of regenerating new information from historical data. This process hopes to serve our goals and use the process of analysis to optimize the process of management decision-making. In the actual process, we are actually more faced with daily business decisions. Such decisions are inseparable from reports and BI for visualization and let the operation department analyze.
Problems with existing data architectures
The following picture is the basic framework of information. From the framework, we can see that our core businesses are financial systems, business systems, OA systems, and warehousing and transportation systems. Through this core system, we will carry out a series of application construction.
Each section is an independent system, and we have been developing it in an application-targeted way. Although this development method solves many business operation problems, it also brings certain difficulties to data accumulation:
1. It is difficult to reuse data
Because the systems are independent of each other, the multiplexing of these data in each system becomes a difficulty.
2. The data semantics of each system are different
In addition, the meaning or name of the same data may be different in various systems, and its semantic definition is also different, which brings difficulties to the application of data.
3. Difficulty in identifying across departments, functions and organizations
The identification of data across departments, functions or at all levels of the organization is also difficult due to the above two issues.
How to Drive Data Efficiency
The following is an analysis chart of our data utilization degree when we pay attention to data, on the one hand, technological innovation, on the other hand, the impact of technological innovation on operational improvement. You can see that this is divided into 4 quadrants. First of all, in the first quadrant, we only accumulate data, and do not do any data processing. The second quadrant is for the data we have accumulated to help enterprises improve efficiency. The third quadrant is to go further, whether data can generate new strategies and opportunities for our business. The last is to combine benefits and opportunities.
After years of construction, the company has also done a lot of work in improving the efficiency and effectiveness of the enterprise. Mainly reflected in these aspects.
- Data integration: Based on the reporting system, we integrate the data of each system into the same data platform. Through this platform, we can display it for our business department or operation department.
- Build dashboards: Display key indicators and key performance through dashboards.
- Hierarchical report: establish a hierarchical authorization mechanism through the data platform.
- Electronic process: the process can be continuously tracked and optimized, and the system can provide optimization analysis.
通过这些数据积累，可以更多地利用报表去发现问题，发现问题后去纠正和优化，解决了很多不能量化和展现的问题，但是，部门间数据指标的标准还是未能统一。于是，我们又在从第二象限向第三象限的过渡做了一些尝试，在这个方面重新梳理了我们的业务模型。
数据决策如何应用于业务管理
医药商业作为供应链的中间环节，在发挥物流配送功能的同时，承担着资金周转的重要职能，因此对于医药企业利润最大化的关键因素是毛利水平的提升和费用成本结构的优化。公司运用全成本核算的方法，创新了CVP价值分析模型，精确测算客户、品种、供应商的净利润水平，并进行因素影响分析，通过挖掘利润增长点，提供营销决策参考。
那么这个对于我们整个医药运营来讲一个算输入一个算输出。那么围绕着输入输出我们开展了几个维度的分析，客户层面的和业态层面的，第二个是供应商层面的，第三个是品种层面，然后是我们业务人员层面。在这个模型中，有很多指标，很多关键项因素，我们要让大家知道每个指标之间的关系是什么，每一项指标的语意的定义是什么并且统一。
于是，我们首先建立了上下一致的对数据理解的过程，除此之外利用这样一个架构我们去完成几个场景的决策。
第一个就是我们业务结构的优化，通过平台上的数据去分析什么样的品种可做什么样的品种不可做，哪些品种带来的利润收益最大，哪些不挣钱。目的是指导大家做业务的结构调整。
第二个就是谈判就是贸易。我们要去引进一个新的品种，这个品种能为我们带来什么样的收益？我们通过数据平台的这些参数的关系，在每一次谈判之前由我们的财务部门做分析和策划。
第三个是经济化的预算。
第四个是对人员的考核，考核的指标来自之前提到的各个维度，比如说利润。
第五个是项目决策，每一次做项目投入，都通过数据平台来做支持。
未来展望
未来，我们希望优化原有单一形式的数据平台，更好的为我们的运营做决策分析。可以从外部市场抓取数据，和企业内部的数据相结合，并且我们做的这些数据可以开放给在我们供应链上的其他人使用。