The construction of enterprise informatization is very complicated, and the selection and evaluation methods corresponding to different industries and business departments are different. If there is such a set of selection standards, it is likely to be mixed and unreliable.
At present, there are many and fairly common standards on the market, all of which are judged from the perspective of the industry. Let me explain to you how to choose an information system at different stages of the manufacturing industry.
Let’s take a look at the “Data Construction Maturity Assessment Model”. The complete white paper on manufacturing digitalization construction is here:
When selecting a model, the IT department should list the transformation goals, current resources, and pain points, and then use the list of functions of various platforms and tools on the market to correspond to them one by one. When a more suitable platform and tool is found, then research the corporate background of the product, such as the founding team, financing rounds, etc. This kind of pyramid selection method makes each step of the action actually disassemble the goal of the previous step, and every time the next step is executed, the achievement of the goal of the previous step must be checked to ensure that the selection does not go wrong.
(The picture comes from the iResearch report)
The first step is to figure out the stage of enterprise informatization construction and establish the goal of informatization transformation.
Still taking the manufacturing industry as an example, the informatization construction of the manufacturing industry can be roughly divided into five stages, and each stage has obvious characteristics.
1. Traditional stage: information fragmentation
The obvious features of this stage are: a people-centered extensive management model and an operating mechanism based on paper forms.
The former means that there is no standardized data management process, and the latter means that the information base is weak. If your company still relies on manpower to organize data and use paper forms to collect and manage work, it basically belongs to the traditional stage.
2. Initial stage: business digitization
The obvious features of this stage: information system-led and the purpose of accelerating business online flow.
The data culture began to sprout, led by the IT department, with the cooperation of the core business department, the company began to invest heavily in data construction from 0-1. At this stage, the company's investment in data talents and digital construction is huge.
3. Acceleration stage: data value
The obvious features of this stage are: optimizing IT construction and emphasizing data support for decision-making.
The modern management model of "relying on the power of data and making scientific and rational decisions" has become the mainstream. Through the flexible call, orderly integration, multi-dimensional analysis, scenario-based application, and visual presentation of the data generated by the production and operation activities of the enterprise, the value of the data has been greatly released, and the data construction and business management of the enterprise have been more closely integrated. into the acceleration phase.
4. Mature stage: digital platformization
The obvious features of this stage: overall digital orientation and innovative applications promote the upgrading of the digital industry.
With the goal of eliminating internal data barriers and maximizing production and operation efficiency, manufacturing companies use digital platforms to manage and plan all data assets in a unified manner, reducing redundant deployment of data construction. Data realizes the barrier-free flow across departments, businesses, and systems, and data effectiveness runs through the entire process of daily operations, management decisions, and strategy formulation.
V. Wisdom Stage: Intelligent Ecology
The obvious features of this stage are: the establishment of an internal intelligent management model and an external sustainable cooperation ecosystem.
Manufacturing enterprises have integrated advanced digital technologies such as artificial intelligence with business development at a deeper level, and built a comprehensive intelligent management model covering intelligent production, intelligent marketing, intelligent operation and maintenance, and intelligent forecasting within the enterprise; A data resource system of intelligent sharing, efficient circulation, and sustainable utilization has been established, thereby creating a smart industrial ecology of mutual trust, mutual benefit, value co-creation, and harmonious symbiosis.
The second step is to take stock and understand how much money the company is willing to invest, whether the manpower is sufficient, and the company's recognition of informatization construction.
There are too many tools on the market. If there is no accurate money budget, manpower budget, and determination to what extent, then talking about tool selection is empty talk.
ps: Don't be greedy for cheap, don't be greedy for cheap, don't be greedy for cheap! Cheap is not good, and it is IT that suffers in the end. . .
The third step is to find out the stuck points of tools and enterprise needs on the market, and then examine the enterprise background.
Dealing with many manufacturing companies, as far as Chinese companies are concerned, most of them are actually in the second, third and fourth stages.
In the first stage, it can be considered to conduct preliminary business system construction according to the situation of the business department, and select systems such as ERP, PDM, MES, SRM, and CRM.
In the second stage, the enterprise has already launched its business system and has some data. The next thing to do is to "digitize the business" and "break the data island". Here, a data decision-making platform can be built for basic data construction, including breaking Data islands, improve the efficiency of making fixed reports and build a data foundation. This big data decision-making analysis platform construction plan is relatively comprehensive. Those who are at this stage can take a closer look:
In the third and fourth stages, enterprises are no longer satisfied with simply "looking" at the data, but hope to use the accumulated business data to "collision" to create new sparks, to feed back and optimize the business, and to find out more from the data. Optimal way of operating. This stage includes building a data warehouse, launching BI, etc. For specific tool selection, still follow the above selection diagram.
The last thing I want to say is that digital transformation is basically "crossing the river by feeling the stones", and there is no experience for reference at all. Therefore, when facing pressure from the external environment, don't go for everything, and don't go for nothing. How much money do you have? how many things to do.