{"id":9622,"date":"2023-01-23T09:22:35","date_gmt":"2023-01-23T13:22:35","guid":{"rendered":"https:\/\/smartcorp.com\/forecasting\/how-much-time-should-it-take-to-compute-statistical-forecasts-copy\/"},"modified":"2023-01-23T10:26:14","modified_gmt":"2023-01-23T14:26:14","slug":"het-implementeren-van-software-voor-vraagplanning-en-voorraadoptimalisatie-met-de-juiste-gegevens","status":"publish","type":"post","link":"https:\/\/smartcorp.com\/nl\/guest\/implementing-demand-planning-and-inventory-optimization-software-with-the-right-data\/","title":{"rendered":"Software voor vraagplanning en voorraadoptimalisatie implementeren met de juiste gegevens"},"content":{"rendered":"

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Data verification and validation are essential to the success of the implementation of software that performs statistical analysis of data, like Smart IP&O.\u00a0 This article describes the issue and serves as a practical guide to doing the job right, especially for the user of the new application.<\/strong><\/p>\n

The less experience your organization has in validating historical transactions or item master attributes, the more likely it is there were problems or mistakes with data entry into the ERP that have so far gone unnoticed.\u00a0 The garbage in, garbage out rule means you need to prioritize this step of the software onboarding process or risk delay and possible failure to generate ROI.<\/p>\n

Ultimately the best person to confirm data in your ERP is entered correctly is the person who knows the business and can assert, for example, \u201cthis part doesn\u2019t belong to that product group.\u201d \u00a0That\u2019s usually the same person who will open and use Smart. Though a database administrator or IT support can also play a key role by being able to say, \u201cThis part was assigned to that product group last December by Jane Smith.\u201d Ensuring data is correct may not be a regular part of your day job but can be broken down into manageable small tasks that a good project manager will allocate the time and resources to complete.<\/p>\n

The demand planning software vendor receiving the data also has a role.\u00a0 They will confirm that the raw data was ingested without issue. The vendor can also identify abnormalities in the raw data files that point to the need for validation.\u00a0 But relying on the software vendor to reassure you the data looks fine is not enough.\u00a0 You don\u2019t want to discover, after go-live, that you can\u2019t trust the output because some of the data \u201cdoesn\u2019t make sense.\u201d<\/p>\n

Each step in the data flow needs verification and validation. \u00a0Verification means the data at one step is still the same after flowing to the next step.\u00a0 Validation means the data is correct and usable for analysis<\/strong>.\u00a0 <\/em><\/p>\n

The most common data flow looks like this:<\/p>\n

\"Implementing<\/a><\/p>\n

Less commonly, the first step between ERP master data and the interfacing files can sometimes be bypassed, where files are not used as an interface.\u00a0 Instead, an API built by IT or the inventory optimization software vendor is responsible for data to be written directly from the ERP to the mirrored database in the cloud.\u00a0 The vendor would work with IT to confirm the API is working as expected.\u00a0 But the first validation step, even in that case, can still be performed.\u00a0 After ingesting the data, the vendor can make the mirrored data available in files for the DBA\/IT verification and business validation.<\/p>\n

The confirmation that the mirrored data in the cloud completes the flow into the application is the responsibility of the vendor of software as a service.\u00a0 SaaS vendors continually test that the software works correctly between the front-end application their subscribers see and the back-end data in the cloud database. If the subscribers still think the data doesn\u2019t make sense in the application even after validating the interfacing files before going live, that is an issue to raise with the vendor\u2019s customer support.<\/p>\n

However the interfacing files are obtained, the largest part of verification and validation falls to the project manager and their team.\u00a0 They must resource a test of the interfacing files to confirm:<\/p>\n

    \n
  1. They match the data in the ERP. And that all and only the ERP data that was necessary to extract for use in the application was extracted.<\/li>\n
  2. Nothing \u201cjumps out\u201d to the business as incorrect for each of the types of information in the data<\/li>\n
  3. They are formatted as expected.<\/li>\n<\/ol>\n

     <\/p>\n

    DBA\/IT Verification Tasks<\/strong><\/p>\n

      \n
    1. Test the extract<\/u>:<\/li>\n<\/ol>\n

      IT\u2019s verification step can be done with various tools, comparing files, or importing files back to the database as temporary tables and joining them with the original data to confirm a match.\u00a0 IT can depend on a query to pull the requested data into a file but that file can fail to match. The existence of delimiters or line returns within the data values can cause a file to be different than its original database table.\u00a0 It is because the file relies heavily on delimiters and line returns to identify fields and records, while the table doesn\u2019t rely on those characters to define its structure.<\/p>\n

        \n
      1. No bad characters<\/u>:<\/li>\n<\/ol>\n

        Free form data entry fields in the ERP, such as product descriptions, can sometimes themselves contain line returns, tabs, commas, and\/or double quotes that can affect the structure of the output file.\u00a0 Line returns should not be allowed in values that will be extracted to a file.\u00a0 Characters equal to the delimiter should be stripped during extract or else a different delimiter used.<\/p>\n

        Tip<\/em>: if commas are the file delimiter, numbers greater than 999 can\u2019t be extracted with a comma. Use \u201c1000\u201d rather than \u201c1,000\u201d.<\/p>\n

          \n
        1. Confirm the filters:<\/u><\/li>\n<\/ol>\n

          The other way that query extracts can return unexpected results is if conditions on the query are entered incorrectly.\u00a0 The simplest way to avoid mistaken \u201cwhere clauses\u201d is to not use them.\u00a0 Extract all data and allow the vendor to filter out some records according to rules supplied by the business.\u00a0 If this will produce extract files so large that too much computing time is spent on the data exchange, the DBA\/IT team should meet with the business to confirm exactly what filters on the data can be applied to avoid exchanging records that are meaningless to the application.<\/p>\n

          Tip<\/em>: Bear in mind that Active\/Inactive or item lifecycle information should not be used to filter out records.\u00a0 This information should be sent to the application so it knows when an item becomes inactive.<\/p>\n

            \n
          1. Be consistent<\/u>:<\/li>\n<\/ol>\n

            The extract process must produce files of consistent format every time it is executed.\u00a0 File names, field names, and position, delimiter, and Excel sheet name if Excel is used, numeric formats and date formats, and the use of quotes around values should never differ from one execution of the extract one day to the next. A hands-off report or stored procedure should be prepared and used for every execution of the extract.<\/p>\n

             <\/p>\n

            Business Validation Background<\/strong><\/p>\n

            Below is a break down each of validation step into considerations, specifically in the case where the vendor has provided a template format for the interfacing files where each type of information is provided in its own file.\u00a0 Files sent from your ERP to Smart are formatted for easy export from the ERP.\u00a0 That sort of format makes the comparison back to the ERP a relatively simple job for IT, but it can be harder for the business to interpret.\u00a0 Best practice is to manipulate the ERP data, either by using pivot tables or similar in a spreadsheet.\u00a0 IT may assist by providing re-formatted data files for review by the business.<\/p>\n

            To delve into the interfacing files, you\u2019ll need to understand them.\u00a0 The vendor will supply a precise template, but generally interfacing files consist into three types: catalog data, item attributes, and transactional data.<\/p>\n