Information Flow
There are forward information flows to relay data from one partner to another, enabling supply chain activities to unfold; in the above diagram, this would include projections of demand as well as actual orders placed. These are accompanied by backward flows to communicate important supply chain information to other stakeholders. Using the above diagram as an example, backward flows would be a communication of the transportation timeline back to whomever placed the order; or information on stock levels to the producer.
A key information mechanism in many supply chains is the collection and reporting of data on Key Performance Indicators
-or KPIs. KPIs can help an organization determine how well it is meeting certain goals. The results of these metrics should provide actionable data to the organization on where to make improvements in the supply chain.
Strategic Issues in Information Flow
First, it is important to understand the relationship between need and demand. Need assesses the theoretical market for a product during a period of time, while demand looks at the true number of people requesting a product when that time arrives.
As an example: in a particular country, it is estimated that 100 children will need RUTF during the month of January. This is the need for RUTF--it is likely calculated off past statistics of how much RUTF was used in that country in January. When January actually arrives, however, the demand for RUTF might be higher than the earlier projected need (for example, if there is a drought that has reduced crop yield) or it may be lower. For each supply chain, it is important to understand discrepancies between need and demand, and why these gaps exist.
Manufacturers rely on forecasts of need to order supplies and plan production; logistics firms use this information to plan transportation schedules; and outlets use it to plan their actual program delivery activities.
Additionally, high-quality information is important for a successful and efficient supply chain. This may include consistency of forecasting methods (both the data collection and analysis methods themselves, as well as whether reporting forms and protocols are standardized), as well as interoperability of information systems and whether data input is automated or is done manually.
Trust in the supply chain can impact data communication. If partners lack faith that the supply chain will operate efficiently and effectively to bring them the product that they requested, they may try to compensate for these shortcomings by gaming the ordering system. This may include artificially inflating orders, or requesting unrealistic production or transportation timelines. This can also take the form of the bullwhip effect, whereby each partner in the supply chain interprets uncertainty and variability and changes their ordering behaviors accordingly and stakeholders early in the process are forced to accept wide variance in the information available to them.
Data transparency is important for consistent and open communication of information. It empowers stakeholders to change their behaviors to improve supply chain efficiency. Concerns about data transparency might focus on who collects data and how they are shared.