Labels

Label-specific processes

The label segment poses specific workflow and data requirements, very different from other industry segments, driven off the tooling (die) parameters, which controls the length and width, the gaps, the margins, and the way the product will be delivered to a customer. Overprint MIS is intuitively developed around the label industry, featuring label-specific parameterization.

Considering different production methods that may be used, mainly ranging between traditional/analog and digital, Overprint provides production proposals based on cost and time parameters. This is enabled through the Cost Estimation tool, which will calculate in all detail the different costs in varying quantities, and through the Machine Learning engine, which is using actual historical data to calculate and forecast the optimum production proposal.

A Detailed Cost Analysis is provided for multitudes of quantities, to cater for different cost estimations thus different quotes with varying quantities. The cost analysis is presented in a comparison table depicting a detailed split of costs for each label product to be quoted and a Production Proposal for the technology yielding the most cost-effective production.

Efficiency in Production Planning

Important functionalities for the label industry are embedded in the Orders sub-module, such as the calculation of the optimum amount of multiplicity of the job, depending on the job’s run length, the number of labels per roll, the roll diameter, the repeat/cut-off length, and the quantity of remaining stock, which in some cases is an important decision-making factor. This will provide an optimum ganging layout, which is a valuable feature for different product versions and repeatable jobs.

Further detailed views can be retrieved within Planning. In one main view, production preparation tasks will display the calculated estimate of the time needed to complete each individual job that is described in the job ticket. In the case of labels separate forecasting times will be shown for preparing plates, printing, hot-foil stamping, varnishing, die-cutting, rewinding, packaging, etc. The indicated times for each job are calculated based on parameterization, machine learning, and even manual data entry. This forecast is based on actual historical production data and no matter how complex it provides the most accurate time estimations.

Machine Learning technology is used to prioritize jobs based on timing calculations, featuring an engine that is constantly improving job prioritization and gain on preparation times, regardless of last-minute changes in the product’s technical specification or production-related issues.

Stock Management, Tracking & traceability

Monitoring can easily track setup times, production times, material consumption, and waste – all important aspects when it comes to label production. Data collection can be done using manual input, even more efficiently it can be automated using PLCs, Sensors, RFID scanning, which are quite efficient in tracking material consumption and waste.

Shop floor terminals are used in conjunction with PDAs to collect data, with the aid of LOT numbers, barcodes, and/or RFID tags, syncing label production for full backward product traceability. Identifying each raw material that comprises a product is important for food and pharma labels. In addition, quality control of materials is possible and birth certificates can be issued.

Warehouse procedures, like receiving, putting away, picking, and packing procedures, as well as inventory and warehousing, are all simplified within the Warehouse module. Starting from the purchase order to packing for shipping to the customer, the system will easily correlate all information and perform all relevant transactions. This is achieved with a variety of user-friendly mobile apps for use in warehouse facilities, for issuing goods receipts, packing lists, delivery notes.

To maintain effective stock management, you need material forecasting that uses Machine Learning to forecast stock procurement. This can be optimized to minimize stock in the warehouse, in the meantime to prevent out-of-stock situations. Stock procurement is considering the average consumption over selected periods, any materials reserved from production, and quantities that are on order to be produced. Detailed views with several filtering criteria, allow for the effective stock management of raw materials and semi-finished products.