The objective of supply chain management is to achieve the highest practicable and sustainable level of service to the end-user and to each of the key participants in chain or network at an acceptably low-cost and at acceptable risk levels. Product and information flow together, and therefore good data management is essential.
Total or whole-life cost includes all the resources deployed to provide that product or service, including prices paid, research and development, operational costs, training, support, maintenance and enhancement, inventory, waste, capital employed and capacity utilisation, disposal etc.
The service level is measured not only by, for example, the percentage of orders or requirements met in full on time, but also by the associated degree of risk involved, i.e. the level of safety and security (e.g. patient safety, or security of supply of parts and fuel for a machine, or ammunition and food for a soldier), and legal liability should something go wrong. Service to the end-user (e.g. business customer, client, consumer, operator, citizen) is composed of the level of performance and timeliness, of speed and certainty at an acceptable total cost and acceptable levels of risk.
Operating a value chain cost-effectively demands [s2If current_user_can(access_s2member_level1)]clarity about decision-making and also about the supporting data - who is to take which decision on the basis of what information; what data are to be shared, with what timeliness and accuracy; what messages need to be communicated; what factors in the value chain need to individually identified.
Costs increase with uncertainty and this arises both from the natural dynamics of markets, consumers, customers, suppliers and technologies and also from the failure of organisations to collaborate well.
Components of supply chain data
1. Governing Principles
Before looking at the components of supply chain data in detail, it is worth familiarising yourself with overall supply chain governing principles.
A system needs to be in place to identify
(a) the value chain Participants and their Locations
(b) the Items (products and services in their various forms)
(c) the Processes (rules, treatments, recipes, etc.)
(d) the Assets
This should be achieved via the smallest practical number of globally accepted systems of numbering, preferably employing unique and non-meaningful identities. This means that the coding system should not have inbuilt meaning since meaningful numbers/codes imply human recognition rather than computerised data, require extra digits and hence introduce a potential lack of accuracy and higher costs of operation. Meaning should be derived from associated master data files.
The Identity Numbers discussed in the Identities section above should be expressible in a form which can be automatically captured, wherever cost-effective. This is called Auto-Identification or Auto-ID and includes:
(a) laser scanning of a printed symbol (bar code);
(b) radio frequency identification of a tag (RFID); or by
(c) reading a smart card with a Personal Identification Number (PIN).
4. Master Databases
The descriptions and key characteristics, including classifications of the identities (participants, locations, items, processes and assets) should be held in structured master data files that are accesses via ID numbers or auto IDs (e.g. as in electronic point-of-sale scanning in a supermarket via GS1 numbers) which are linked to product and price master data files. The master data files are brought together under a Master Database.
Other related forms of master data are technical specifications/design, product life cycle data and financial data (prices/costs). Master data are semi-static, i.e. they have a time structure which defines when they are applicable; now, past and future.
5. Dynamic Databases
Dynamic Databases contain data about Events relating to Identities (transactions, use, movements, treatments, etc.) which occur across the value chain. An Event changes an existing State into a new State or Outcome via an Activity or Transaction.
Dynamic Data should be stored in a structured (e.g. relational) database alongside expected or planned activity so that performance can be measured, volumes of activity can be tracked, exceptions monitored and actions initiated.
Too often Master Data and Dynamic Data are confused with each other.
Messages contain the combination of data elements (such as quantities, names, dates, etc.) which enable individual and joint management of the value chain by the participants e.g. Transactions such as order to produce, move, deliver or pay; invoices; statements; plans; etc.