Attribute Data

CIOP is an end-to-end supply chain training and certification program designed and developed by supply chain practitioners and consultants. The syllabus was developed in consultation with the industry, which led to Inventory Management Body of Knowledge (IMBoK v3.0). Most of the class discussions you will hear cannot be found in any textbook rather these are all the original contribution from the developers of CIOP. For example, think about the types of inventories such as raw material, work-in-process and finished goods. Out of these three, WIP is the most difficult to sell in the market and that is one of the reasons, WIP should be controlled well. The company has a built-in capability and capacity to sell finished goods, which is their business. The second easy to sell are the raw materials as you know the suppliers, who may either buy-back or at least guide you. But in case of WIP, this is custom manufactured to suit your manufacturing processes and may not well fit any other organisation, hence you may have to sell it as a scrap.

In a traditional supply chain management approach, a company's data is usually classified into two different forms: qualitative data and quantitative data. Qualitative data refers to items of information that are directly related to the quality of a product or service-the level of quality of a factor, like a 

manufacturing process, for example. Quantitative data, on the other hand, refers to quantities (counts, units, etc.) that can be used to track inventory levels and overall performance of an organization. While both types of data are important, it is important to separate them so that an organization can properly assess its current or future performance and compare it with its historical performance.

A data type known as the "attributes" will help companies in determining how to categorize their varying types of product or service performance. These attributes can be complex, like production processes, or very simple, such as the number of store locations. There are many different attributes, which can be grouped under numerous headings, including: customer profiles, order cycles, or store locations. The attributes can be organized by a wide variety of criteria, and each head of the classification can contain many different subsets of factors.

As you can see, a data set can have many different aspects. It may be categorized as one of three types: continuous data, automated attribute data, or time-based data. Continuous attribute data is one in which a change occurs over a long period of time. An example is the change in a shipping company's inventory count from one day to the next day. Or, an example of this might be a new manufacturing process which changes the amount of materials used in producing each unit of a product over a period of time.

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