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Manufacturing and Production Control
identifying areas and processes where the technologies can be used
Manufacturing is rightly acclaimed as a significant contributor to the wealth creating base of any developed economy. However, it is clearly a very competitive set of industries and accordingly subject to varied and demanding agents of change.
Those countries and companies that have been responsive to change and the potential of emergent technologies have gained precedence over those who have not. Responding to change is not easy. Manufacturing and production control have been influenced by so many factors and developments over the years and still remain vulnerable to the vagaries of change and sustained competitive pressures.
Technology and techniques have, and continue to play, a significant role in the evolution of manufacturing and production control. Information technology (IT) in its broadest sense, has exercised substantial impact upon manufacturing and production control methods with computer-aided design, manufacture, management, planning and integration being formative elements in the evolutionary process.
Techniques such as materials requirements planning (MRP), manufacturing resources planning (MRP II), justin- time (JIT), modular manufacture and others have, and continue to exert their influence upon an industry geared to increasing efficiency, flexibility, quality and speed of response to both market and customer needs.
Within the current climate of change the basic imperatives remain. Companies still need to improve efficiency, quality and speed of response if they are to meet the continuing demand for lower prices on the goods that are produced.
Increasingly manufacturing and production control can be seen to be evolving within the wider context of supply chain management. The traditional bounds of manufacturing are being extended and re-engineered into other aspects of the supplier and out-bound logistics arenas.
A shift can be seen towards more outsourcing of manufacturing operations. This is particularly noticeable within the electronics manufacturing arena and modular manufacture. Such shifts are facilitated by developments in communications, software and collaboration.
The communications facilities support rapid and reliable information exchange between business partners. Software platforms provide operational support for disparate functions, while confident collaboration is required to allow the overall operations to function effectively.
Indeed the emphasis now being placed upon collaboration is manifest in yet another business acronym known as C-commerce – Collaborative Commerce.
With all these changes and the ongoing demands for lower costs and improved productivity, manufacturers are faced with the need to review and consider where improvements can be made and opportunities seized if they are to achieve competitive advantage and higher profit margins.
Despite the changes taking place at the very core of manufacturing and associated processes along the supply chain, there remains an intrinsic need for items to be physically handled and the data associated with them to be appropriately managed. Now, more than ever there is a need for effective and efficient item and item-data management.
The radical benefits that AIDC and other item-attendant technologies can provide are key to satisfying the requirements. It is also part of the confidence building partner involvement. Confidence in identifying items. Knowing where they are. Knowing how they need to be handled, rapidly and effectively without errors, without delays and without incurring unnecessary wastage of effort and resources.
With manufacturing processes as diverse as the imagination can stretch it is a somewhat daunting task to generalise on ways in which AIDC and other item-attendant technologies may be applied.
However, by directing attention to slow, error-prone paperwork support activities, a lot can be done to achieve significant improvements. Virtually any process involving the handling of items will require some information about the item to be generated and handled in some way. Back-office order handling through to main-stream manufacturing processes a great deal of paperwork, along with key-board data entry and other forms of manual processing. Each process or task of this kind provides an opportunity for gaining benefits from AIDC.
While isolated applications can provide significant benefits, it is important to consider that added-value process-linked applications supporting effective information flow within the manufacturing system as a whole, can offer even greater benefits.
To achieve such results it is useful to consider improvements in areas where AIDC has already been effectively applied:
Work in progress.
Raw materials and component-handling.
Automated processes – Item selection, machining and assembly.
Tool identification for automation and tool management.
Quality assurance and test.
In each case applications can be identified that are appropriate to wide ranging manufacturing and production disciplines, from food through to heavy industry.
Work in Progress
It is probably true to say that in the majority of manufacturing and production companies, tracking orders and work in progress is not as efficient and effective as it might be. Paperwork goes astray. It gets damaged or misplaced or confused when linking tasks in the management chain.
Where such processes also involve data gathering, written and/or manual entry into computer systems can cause delays and mistakes can arise contributing to diminishing performance. By using AIDC technologies dramatic improvements can be made even in small and medium sized enterprises (SMEs).
Documentation concerning orders, work tasks and work in progress can be coded, generally by means of linear bar codes, and linked as necessary to more durable data carriers accompanying items in the manufacturing or production process. By using suitable data capture terminals work in progress can be tracked and data captured automatically at appropriate points in the process.
Where manual handling of work items is involved various techniques can be used for distinguishing stage and status within the process. This could include for example, machine-readable location codes and menus to allow rapid selection and entry of data into the management system.
This can be particularly appropriate for tasks involving checks to be made and reported, such as those to be found in quality control systems. By using AIDC data capture techniques the facility is also provided for identifying the manual operator involved in the tasks being performed.
The job can be efficiently linked to an individual and benefits gained through having information that can be used to better manage human resources and allocation of tasks. In its simplest form the sort of equipment required to support applications in which bar codes are used for identification purposes includes:
Printers to print bar code labels or documentation containing bar codes.
Hand-held or fixed position bar code readers with data collection facilities to read bar coded labels, or documentation at appropriate points in the manufacture or production process.
Interface facilities for linking bar code reader data collection facilities to the manufacturing information management system.
Support software to allow transfer and appropriate usage of the data collected.
The choice of bar code reader / data terminal, data transfer and associated interface will be largely determined by how quickly information is required, and how flexible the data gathering point needs to be. Hard-wired, optical or wireless data transfer options may be considered. The latter will provide more freedom of movement. Where speed of data entry is less critical, batch delivery of data may be considered using cradle-based up-load points.
In choosing the data terminal it does of course make sense to select a system based on common data carrier devices, such as bar codes, for each of the identification requirements involved (item, location and individual as required).
By exploiting the opportunity to identify the job, the stage location in the progress of the job, work item and operator involved in a manual work or operator assisted tasks the facility is provided for knowing the status for any work in progress.
Customers enquiring about progress can be rapidly and confidently informed. By automating or semi-automating the data collection tasks, work planning can be better achieved and controlled and productivity gains can be made.
Raw materials and component handling
Manufacturing and processing functions can generally be seen as operations requiring an appropriate balance between raw materials and/or components coming in and products going out. This is usually determined by customer, or consumer pull or estimates of demand. The procurement of these items is an essential and potentially critical part in the manufacturing or production process. Since the balance of input and output cannot be perfect some degree of storage or warehousing is invariably required. But this of course is a cost and pressures are invariably applied to reduce such costs.
More effective identification and inventory control through AIDC can in some cases dramatically help in this respect. It can also better support the management of items and preparations for entry into the manufacturing or production processes. Such preparations might include kitting for components or batching of materials
While networked communications is now playing a significant role in the ordering and acknowledgement of the procurement process, the physical movement and receiving of items can benefit from the use of AIDC to improve the acquisition processes.
Through customer-supplier collaboration, consignments of materials / components can be accompanied by machine-readable data carriers. These are often in the form of bar code labels, and are used to speedily identify and handle them as they arrive, with the information directed immediately to the information management system.
Some operations exploit bar code materials handling labels and a number of industries now have application standards specifying both human-readable content and the use of bar code symbols. Examples include the ANSI materials handling label, MH10.8 and the equivalent European Multi-industry Transport Label and the Serial Shipping Container label, incorporating the EAN.UCC Serial Shipping Container Code (SSCC-18).
General Motors specify a materials handling label, GM1724A, which is serving as a model for a Common Global Supply Chain Shipping Label template. It is being developed jointly by the Automotive Industry Action Group (AIAG), Odette (Europe) and JAMA/JAPIA (Japan) in which some of the linear bar code symbols appearing in earlier AIAG labels are replaced by multi-row bar code symbols.
Where shipment labels are being considered at the supplier end or outbound at the manufacturer end, it is important to establish if there are existing or developing industry-specific application standards that may have to be complied with to satisfy open system usage.
While shipping labels can be effectively applied for identifying consignments and content there is a need for further details to be obtained from forwarded information held in the information management system. Similar details can be transferred with the consignments or individual items using portable data files in the form of machine-readable twodimensional codes.
The multi-row bar code symbols now being considered for consignment labels are representative of such carriers. They are capable of carrying more data than linear bar codes – often in excess of 2,000 characters. A suitable reader can provide immediate information on what the consignment contains and deliver that information either in batch form or directly to an information management system via a wireless link.
Radio frequency identification (RFID) data carriers may also be used for consignment identification, particularly where returnable containers and pallets are involved. These can also provide added benefit in applications where read-write capability is required. This would particularly apply to returnable containers.
Once the consignment data is read or entered into the management system the opportunities are presented to achieve a range of item support activities. The individual items may be separately identified, sorted into batches and batches identified, by use of appropriate data carriers.
Where items and batches are not accompanied with data carriers at source, these may be applied to items when unpacked and populated with data from the information management system. Where, for example bar codes are appropriate, the labels can be printed using information derived from the consignment list, possibly by means of hand-held printers. Components, or materials so identified, may then be transferred to store or directly to a production line. Inventory management can effectively become a real-time or near real-time function.
The facility can also provide for more effective KANBAN / MRP systems. This would be via AIDC supported inventory management, electronic communications and other system data carriers applied to containers and locations.
Within a flow-based, repetitive manufacturing system AIDC can assist the KANBAN linking between production stages. Here the customer is pulling stock from the warehouse using KANBAN while advisory schedules indicate the likely requirements in terms of materials and capacity. These are in turn pulled down by the KANBAN links within the supplier and manufacturer stages of the process.
With synchronization being a key requirement for effectively operating such systems it is important to apply appropriate technology to satisfy that requirement. AIDC can assist in this way.
While KANBAN is effectively confined to repetitive and balanced mixed model manufacturing systems, AIDC for item and container identification can be used to facilitate improvements in mass, batch and ‘jobbing shop’ production. Small and medium sized enterprises (SMEs) have benefited from such technology, using linear bar codes, for example, to help manage processes, and identify and track jobs through the manufacturing process.
Where data carriers are being considered for use in manufacturing processes the data carrying requirements invariably determine the type of data carrier required. However, read-write requirements, durability and longevity also have a bearing upon the choice of data carrier required.
Automated processes – item selection, machining and assembly
In addition to supporting manufacturing and production processes through item, location and operative identification AIDC may also be effectively applied in more direct support of automated processes. Data carrier technologies can be particularly useful in supporting processes in which there is a need for:
Item sortation – data carriers being used to identify or carry item specific information that can be captured and compared in order to achieve selection based upon a requirement or set requirements. Examples include selection and matching of toleranced machined parts, different sized garments, or different weights of process ingredients.
Item-specific process or machining operations – in which machine settings, machining or process requirements are held in a suitable data carrier attached to or accompanying the item concerned. It can be read when required and used to initiate the necessary item-specific operation. Examples include drilling, milling and other machining operations, coating and spray-paint functions and forming operations.
Assembly operations – requiring automated identification of assembly items, including orientation and other assembly support data.
Within these operations the item-specific operating or setup instructions may be derived from a centralized management system or from the item-attendant data carrier. Various data carriers can be considered for automated process support, typically in one of the following forms:
‘Licence-plate’ carriers for applications requiring rapid identification of items. Any further itemspecific information is derived from a host information management system in response to the ‘licence-plate’ data. Such carriers may also be used for carrying control data which, when read, can be used for directly activating a control mechanism, such as a tip tray or line selection in a multi-line process. Low cost bar code, contact memory and RFID data carriers are typical choices for such purposes The actual choice is dependent upon application specific needs and conditions under which they are expected to be used.
Portable data files, which carry item-specific data or instructions for flexible handling of assembly operations. These would include matching of components, selection of components and processspecific control instructions. Using read/write data carriers the opportunity is there for controlling assembly and recording of assembly line data for use in subsequent processes, assembly, test and quality control. Two-dimensional code (read-only), contact memory and RFID data carriers may be considered for such uses. The choice is again depending upon application-specific needs.
Portable data files, with processing capability (smart tags), to handle data transfer functions. On-tag processing could avoid excessive communication exchange between tag and host and accommodate faster conveyor speeds and efficient process functionality.
Generally speaking, the data carriers are used to engineer more efficient processes through appropriate partitioning of data and the benefits of rapid, accurate capture of data as needed within a process transaction or control function.
A further and significant role that AIDC data carriers can provide is in the realization of traceability systems. With many products being produced that are safety, business or process critical the need is often recognized for traceability. This may be traceability of raw materials or components.
Food traceability is a case in point where the origins of food products need to be identifiable for customer confidence and food safety support purposes. Traceability can also be recognized for warranty and analytical purposes concerning a diverse range of products. Pumps for the oil industry require component castings and parts to be traceable, often with details on the component formation processes. Similarly, traceability needs may be recognized for medical and aero-space products. Such systems may be achieved by cascading identification data into progressive item-attendant data carriers. These would include code keys to traceability information stored elsewhere, possibly accessed via the Internet.
In some cases data may be assembled into a final product using a suitable data carrier and maintained as an escort memory. These data carriers may be used for the management of item maintenance and possibly product disposal or re-cycling.
By suitably structuring the data encoded in the escort memory the carrier may be used as an effective maintenance support tool. This would carry data keys to allow access to maintenance manuals and product-specific information (including maintenance histories), through communications networks or computer-based storage media. Read-write or write-once-read-many (WORM) capability does, of course, allows life-cycle updating of item-attendant information.
Specific applications would need to define the requirements to be satisfied in terms of speed, complexity of function, data and data transfer requirements, range and environmental issues concerning the application of data carrier technology. These requirements would form the basis for selecting the appropriate technology and the systems solution.
Tool identification for automation and tool management
With continued emphasis being placed upon automated processes, computer controlled machining opportunities have and continue to be seen for applying data carrier technology to enhance them. Where components of an automation system need to be identified for purposes of selection and maintenance, the prospect is presented for applying AIDC technology in realizing a use-service cyclic support process.
Characteristically AIDC has been applied in this way to the management of computer numerical control (CNC) machine tools. By using RFID data carriers with read-write capabilities embedded in the stock of a machine tool, the specific tool can be automatically identified and carry data relevant to its use and maintenance.
Once a tool has been used it is automatically returned to its tool store cradle. The tool is then selected, examined and measurements taken to determine its condition and requirements for sharpening. The tool is then sharpened checked by measurements to ensure it is fit for purpose and returned to tool store cradle ready for use.
When required for use the tool changer can automatically identify the correct tool to use from those available on the cradle, irrespective of where in the cradle the tool is situated. Having identified the tool by means of the tag identifier the tool changer can also derive from the tag other data, such as minimum and maximum rotation speeds, dimensional features and correction figures. This information can then be fed into the controller to allow appropriate and effective use of the tool. By identifying and using data in this way the machining processes can be better managed and machining wastage reduced or eliminated, as part of a statistical process control facility. Moreover, care of the tools in this way can assist in avoiding tool breakages and longer effective use of the tools. Other manufacturing assets may be similarly better managed by means of machine-readable identification and asset-specific data, utilized within use-service cycles.
Quality Assurance and Test
Just as AIDC can assist in improving paper-based work-inprogress activities AIDC may also be applied effectively to support quality assurance and test activities involving the collection of data. Quality assurance procedures involving forms and other documentation requiring checks to be made, to assure conformance, can readily be re-engineered to allow automatic capture of data, possibly based upon bar-coded ‘menus’.
Such developments can also allow the quality assurance operatives to be identified through appropriate individual identifiers, together with locations and time-stamps as required.
The support may be extended to more detailed data acquisition and verification procedures, including those for providing statistical process control (SPC) – linked with automated sensory inputs for SPC data gathering.
Defect reporting and control of rework is a further area of quality assurance in which AIDC supported processes can improve management performance.
Where defect codes can be assigned to particular manufacturing defects or mistakes, such as damage, wrong component, or faulty mechanism, providing the code listing is not too long they can often be accommodated in bar-code ‘menus’. As a defect is identified an item code can be captured from the defective item or its physical carrier and entered into the information management system together with a selected defect code.
The data entry can be time-stamped and, as required, the operator may also be identified. The defective item can be diverted for rework and with the information already in the system the rework requirements can be appropriately balanced and managed. Such systems can often be achieved with a modest amount of hardware. There is of course the need for appropriate software, but as with any of these applications advice and support can usually be gained from knowledgeable suppliers and systems integrators.
Set-up procedures and other process support functions tied to quality assurance can also be achieved through the use of appropriate AIDC systems, such as two-dimensional portable data files. These can be used to accompany items requiring specific set-up instructions for particular measurement or calibration devices.
As manufacturing evolves . . .
With perhaps more emphasis upon out-sourcing, further opportunities will arise for using AIDC as integral part of the systems. The availability of other item-attendant technologies, such as sensory, location and local communication technologies will provide the foundations for new and innovative process developments. Integration and intelligent processing will further expand this potential. A look at the case studies will provide insight in what can be achieved now, with the prospects of radical improvement.