Whether your company is a start up or well established, accurate inventory control is a key issue. And, an integral part of an inventory control system is barcodes. The concept of using barcodes is familiar in our daily lives. However, without a good understanding of what a barcode is and how it works, its application in an inventory environment may be daunting.
A barcode in its simplest form is just another type of language. Most common barcode labels consist of the actual barcode (scanner readable) and words or numbers (human readable). A barcode does not intrinsically hold any additional information. However, the barcode plays a key function in inventory control because it allows a scanner to read the item number or SKU (Stock Keeping Unit) associated with a piece of inventory.
Regarding inventory control, it is common for a business to have what appears on the surface to be one main stumbling block. For example, your business seems to be accurate in recording the inventory received, but has trouble shipping the correct quantity or item to your customer. This is when the concept of data collection (spreadsheet) vs. data validation (database) comes into focus.
If we look at the example above from a data collection perspective, only the picking and shipping process needs to be corrected. We will assume, for this example, that the inventory we are receiving contains an existing manufacturer's barcode label. A person picking an order and collecting data with a barcode scanner will have the ability to record things such as the item that was picked, item quantity, a date and time, etc. This will allow someone at a later time to review the information in a spreadsheet and possibly pinpoint why errors occur during picking. Note that this method does not correct any behavior during the picking process nor does it take into account the total inventory process.
We will now look at the same example from a data validation perspective. For this process, we need to address the total inventory and initial set up, and not just the picking process. A relational database would be created to use the manufacturer's item numbers. Through the use of a database, you can store item information like minimum/maximum/reorder quantities and whether lot numbers or serial numbers are required; additionally, you are able to track vendor information, purchase orders, and sales orders and store them against the item number. This process would require receiving the inventory to a location in a quantity with a predefined inbound order. This normally correlates to a Purchase Order.
With data validation the person receiving the inventory can be prompted if the wrong item or quantity is received against an order and it can be addressed immediately instead of at a later date. Now that inventory has been received and put away we can pick in the same manner. A predefined picking order will direct the user to the proper location for the correct item in the correct quantity. This usually relates to a sales order or work order. Again, the relational database allows for immediate correction during the picking process.
Article Source: https://ezinearticles.com/?Data-Collection-Vs-Data-Validation&id=6215578
A barcode in its simplest form is just another type of language. Most common barcode labels consist of the actual barcode (scanner readable) and words or numbers (human readable). A barcode does not intrinsically hold any additional information. However, the barcode plays a key function in inventory control because it allows a scanner to read the item number or SKU (Stock Keeping Unit) associated with a piece of inventory.
Regarding inventory control, it is common for a business to have what appears on the surface to be one main stumbling block. For example, your business seems to be accurate in recording the inventory received, but has trouble shipping the correct quantity or item to your customer. This is when the concept of data collection (spreadsheet) vs. data validation (database) comes into focus.
If we look at the example above from a data collection perspective, only the picking and shipping process needs to be corrected. We will assume, for this example, that the inventory we are receiving contains an existing manufacturer's barcode label. A person picking an order and collecting data with a barcode scanner will have the ability to record things such as the item that was picked, item quantity, a date and time, etc. This will allow someone at a later time to review the information in a spreadsheet and possibly pinpoint why errors occur during picking. Note that this method does not correct any behavior during the picking process nor does it take into account the total inventory process.
We will now look at the same example from a data validation perspective. For this process, we need to address the total inventory and initial set up, and not just the picking process. A relational database would be created to use the manufacturer's item numbers. Through the use of a database, you can store item information like minimum/maximum/reorder quantities and whether lot numbers or serial numbers are required; additionally, you are able to track vendor information, purchase orders, and sales orders and store them against the item number. This process would require receiving the inventory to a location in a quantity with a predefined inbound order. This normally correlates to a Purchase Order.
With data validation the person receiving the inventory can be prompted if the wrong item or quantity is received against an order and it can be addressed immediately instead of at a later date. Now that inventory has been received and put away we can pick in the same manner. A predefined picking order will direct the user to the proper location for the correct item in the correct quantity. This usually relates to a sales order or work order. Again, the relational database allows for immediate correction during the picking process.
Article Source: https://ezinearticles.com/?Data-Collection-Vs-Data-Validation&id=6215578
