In the contemporary digital economy, intangible assets, of which information is a critical component, fuel a dominant share of growth and prosperity. This is in contrast to the value added by physical assets in the erstwhile traditional business model [3]. Information has typically been analyzed as a product, with the focus primarily derived from a snapshot view taken at a particular time. However, emphasis on the product view falls short of a precise measurement due to the nonquantifiable nature of the characteristics (such as relevance and reliability) of information. A systematic and conscious effort to influence and control the flow of information will lead to efficiencies in organizational processes. Therefore, it is imperative to manage information flow (and not just information) to improve business process efficiencies, especially in organizational environments.
Numerous studies done in the business process redesign realm have articulated the need for rationalizing organizational processes [4]. If process efficiencies are to be realized, it is critical to take another look at the infrastructure based on the parameters affecting the flow of information. Few studies, however, have specifically addressed how flow irregularities can affect the process. The framework proposed here adopts a process view of information, which requires an understanding of information characteristics during its flow through communication channels, and its processing by organizational agents. Understanding the process view should help managers in measuring the impact of flow parameter variations on information quality.
Companies, however, are often poorly organized and underprepared to manage such complex information flows [1]. The existing state of underpreparedness may partly be attributed to a lack of understanding of the dynamics of such flows. We propose a parameter-based guiding framework of information flow to manage organizational processes. It establishes a foundation to assist organizations in measuring and reporting information by better managing their flow.
Information Flow Dynamics
In order to comprehend the process view, we draw from an analogy of information flow with fluid flow. During its flow, a fluid is known to change its properties (such as velocity and viscosity) with respect to space and time [10]. Fine-tuning its measurable dimensions can meaningfully alter the nature of fluid flow. Knowledge of the relationship between properties of fluid and its flow is used in engineering to design efficient fluid conduits (such as pipes) and altering fluid flow mechanisms (such as dams).
Could there be a conceptual equivalent of information flow that parallels fluid flow dynamics? For example, the speed with which information flows in an organizational process depends on the number of intermediaries that belong to that process. Furthermore, subtle changes can result from flow irregularities due to localized delays and biases. In the current business environment, an understanding of flow parameters is essential for enhancing the value of complex business processes and designing systems that can more effectively manage these flows. Alteration in the values of any one or combination of these parameters should help achieve a desirable influence on the usability of information.
System design provides the necessary infrastructure for information processing functions that influence the quality of the resulting output. The architecture of the design should, therefore, incorporate the flow parameters and their changes in real time.
Node Density
A node is used to describe an entity or a group of entities capable of altering the properties of information flow. The node density is then defined by the number of intermediate nodes in the information processing channel. The complexity of information flow is directly related to node density. Studies have suggested the importance of managing coordination gaps that arise due to the lack of useful information or the presence of incorrect or unusable information [9]. Specifically, the strategic role of IT design in reducing coordination gaps in the form of time, space, and information distances between nodes in information flow has been emphasized.
The number of intermediate nodes appears to be an important factor for two reasons. First, if decision making at each node depends on information from other nodes, then the presence of a large number of nodes along the processing channel should result in an increase in uncertainty. Second, a large number of nodes may impede the speed of information flow. If the extreme case of manual processing (human node) is assumed, then an increase in the number of intermediate nodes would also negatively affect the processing efficiency of the entire system. Organizations can manage internal and external flows by altering the number of intermediate nodes. Two broad strategies for managing such information flows include supply chain integration and efficient procurement processes.
Virtual integration across supply chains. Supply chain integration generates efficient information flows for participating entities by focusing on value-added components. For example, Dell Computer Corp. adopted an aggressive strategy of revamping its supply chain by pruning the non-value-added nodes and information flows (see Figure 1). By sharing information about its demand forecasts across the supply chain, Dell does not need to carry inventory until it is needed during production. Also, suppliers maintain convenient shipping points to satisfy demand on a real-time basis. Information sharing and supplier accessibility help Dell manage to carry inventory on a just-in-time basis. Dell has been successful because it could effectively eliminate the distribution nodes from its supply chain resulting in leaner order-to-delivery times [7].
However, replication of the Dell model requires a careful accommodation of situation-specific variables. For example, in cases where product development requires several components and complex configurations, there is more material flow and consequently more information flow. Implementing the Dell model can be difficult for some companies (such as Ford) because their supply chains are inherently more complex with many layers (tier-1, tier-2, tier-3…) and intermediary companies. While tier-1 suppliers may have a well-developed IT infrastructure, suppliers toward the end of the chain have neither the technological sophistication nor a justifiable business case to assist in the sharing of information.
Procurement process efficiencies. Reducing the number of nodes can also simplify internal workflow processes (such as procurement) and consequently decrease the typically high cost associated with purchases of MRO (maintenance, repair, or operating) or indirect supplies (see Figure 2). In such situations, all employees within the purchasing organization have access to a proprietary master catalog (created from various supplier catalogs). In order to control access, the system contains procurement rules that enforce purchasing privileges. When an employee selects a product, the purchase request is routed to the supervisor (or other intermediaries) for further approval. This order is eventually sent to an exchange (hosted by either the purchasing organization or by the procurement software vendor). The order is decomposed into suborders and then routed to individual suppliers. The immediate value proposition of procurement process efficiencies for most suppliers is that being connected to an exchange reduces their costs of finding potential buyers. Additional value may depend on the complexity of items being purchased. Most MRO purchases do not present a problem because they are standardized functions. Companies are, therefore, able to streamline their procurement activities.
The value of a node depends on the extent of reduction in information content or decision-making quality if that node is removed. Neither of these consequences occurred in Dell’s situation by the removal of the distributor node. While fewer nodes may result in a smoother transfer of information, it is important to realize that the quality of information at each node affects the efficacy of decision making at subsequent nodes.
Velocity
Velocity refers to the speed of incoming information at a node. In recent times, such terms as flow and velocity are used more extensively to indicate the speed of change in the economy. Bill Gates has argued that the primary driver of organizational change will be the flow of information. The Federal Reserve Board routinely tracks the velocity of money to guide its fiscal and monetary policy. Michael Dell uses the phrase “inventory velocity” to refer to rapid inventory flow in business transactions [5].
Velocity’s effect was particularly evident during holiday seasons when several e-commerce retailers were unable to handle the deluge of seasonal orders. Therefore, systems that handle millions of e-commerce transactions (such as Web servers, database servers, and payment servers) require a design that is robust enough to sustain wide variations in the velocity of information flow without an adverse effect on their performance. Further, the existing business infrastructure (such as warehouses and delivery trucks) supporting order fulfillment processes should also be sufficiently robust to accommodate different speeds.
Typically, inventory and fulfillment systems cannot manage high velocity better in a situation where the subsystems are partially automated and poorly integrated. The CIO of a major tier-one supplier to the three largest automotive manufacturers commented, “It takes two or more weeks for information from the automaker regarding the increase in the sales of a specific type of model, that translates into materials requirements for our company, to get to us. This leaves us with about a week to manage our supply chain, leaving our inventory management ad hoc at best.” Covisint (www.covisint.com), an exchange system based on standards agreed upon by the three major automakers, will facilitate exchange of the type of information that companies need. This exchange could lead to an increase in the velocity of information flow.
Clearly, systems designed to facilitate the automation of information exchange help to streamline the organizational processes. However, it is not always true that automated processes are less prone to influence velocity. Some processes could potentially suffer from automation when information flows too quickly. This might occur in ERP environments where users are unaware of the consequences of their actions. For example, in the pre-ERP days, if a sales clerk entered an incorrect order (wrong specification, price, or shipping address), there usually was time to correct the error. In an ERP environment, sales order information is directly routed to the manufacturing module where it is scheduled into production eventually waiting to be shipped. Since the sales order module is also integrated with the accounting module, it is likely an undelivered product will result in unpaid invoices because of which the customer’s credit status could possibly be downgraded. It is also possible to imagine the consequences of manufacturing a product without the correct specifications. In the past, when most processes were manual and paper-based, businesses managed to cope with these problems because there was more time available to them to react and correct some of the inaccuracies.
Viscosity
Viscosity reflects the degree of conflict at the node. The conflict arises due to the presence of contradictory information components known as information particles—the smallest component of information that can exist independently and still retain the characteristics of information. In such cases, viscosity appears in the form of multiple values of information (multiple information flows feed similar information content to a node) that must be resolved before the node can begin processing. If there is lesser conflict between the multiple values, then a quicker resolution can occur—a situation characterized by low viscosity. However, a high degree of conflict will likely delay the resolution time—a situation characterized by high viscosity.
Consider the following example of Toys-R-Us, which illustrates the effect of viscosity on business processes. During a past holiday season, Toys-R-Us was one of the poorer performers in order fulfillment. The company handled both offline and online sales orders during that period. The number of online orders outweighed many times the available processing capacity and the inventory the company had in its warehouses. The company’s less than satisfactory performance stemmed from the perplexing nature of inventory management. Prudent management practice dictates maintaining inventory at lower levels to avoid storage-related costs. Contrarily, there are longer-term costs associated with stock-outs that include lost sales, impaired goodwill, poor customer resource management, damage-control expenditures, and changes in customer loyalty. These alternatives represent information particles of inventory cost management.
Inventory management presents an interesting administrative dilemma: maintaining excess inventory versus stock-out possibilities. Planning decisions in such cases involve seeking an optimal inventory level—a tradeoff between demand projections by the marketing department, and inventory cost control by the production department. The constraints imposed by the two opposing elements render the decision making relatively inflexible. An understanding of the interaction and effect of such viscous information flows would have helped the company better manage the costly and lasting effect Toys-R-Us had to cope with.
The Toys-R-Us experience demonstrates the potential for adverse consequences when organizations are unable to manage viscosity. The cause of such consequences is usually a lack of accurate and streamlined information across the supply chain. Viscosity-related uncertainty eventually results in what is known as a bullwhip effect [6]. In a bullwhip effect, entities along the supply chain resort to stockpiling (for just-in-case scenarios), thereby eventually leading to excess inventories.
Volatility
Information volatility denotes the associated uncertainty in its content, format, and/or timing. The degree of volatility may depend on the impact of external forces based on either industrywide or economywide factors. Thus, changes in economic policies or interest rate by the Federal Reserve Board (perturbation) are likely to affect the operating performance of an organization. Depending on the effect such changes have on the organization, they would generate either laminar or turbulent information flows.
For example, an average daily volume of a few thousand transactions over a month with a variance of more than 5,000 or 6,000 could be characterized as a turbulent flow (high volatility), whereas an average daily volume of a thousand transactions over a month with a variance of 500 or 600 could be considered as a laminar flow (low volatility). More specifically, when the distribution of transactions is comprised of several peaks, there is a higher likelihood that the flow is turbulent. Similarly, spikes in transaction volume for an online trading system when there are widespread sell-offs are representative of a turbulent information flow.
It is difficult for an organization to control the timing, content and, extent of turbulence. However, knowledge of relationships between external forces and internal processes can help manage the effect on the system. Consider the example of online retailers who frequently face the problem of preparing for the surge in demand during the holiday season. Preparation could entail making necessary investments in both the technical infrastructure (such as increasing the number of servers) as well as the business infrastructure (such as more efficient arrangements with delivery companies). Planning for such capacity alternatives requires an assessment of anticipated demand and subsequent translation of demand into resource requirements. Thus, an online toy retailer could project an increase in page views (resource requirements) based on fourth-quarter estimates and historical data on the average number of page views required per order.
Organizational Implications
Organizations invest in e-business drivers to improve operational and financial performance [2]. Examples of such drivers include system integration, internal orientation of information technology, and customer/supplier-related processes. For successful implementation of these drivers, careful attention should be given to the parameters influencing the flow. Figure 3 outlines the role of flow parameters in influencing the nature of interaction between an organization and its various stakeholders. For example, a customer order triggers various interdependent business processes and the associated information flows. Performing a credit and inventory check through related subsystems will validate the order. A satisfactory evaluation should initiate information flows related to the generation of production schedules, contacts with suppliers, arrangements with logistics providers, and realization of cash from customers. Hence, the relationship between the organization and the external stakeholders (such as customers, suppliers, and service providers) can be affected by variations in flow parameters. Some illustrative implications of flow parameter variations in the context of Figure 3 are described in more detail here.
Customer relationships. The number of orders per unit time would constitute the velocity of flow and may be affected by the number of nodes through which the order reaches the implementation stage at the back end of data processing operations. Furthermore, variations in the number of orders processed per day can increase the volatility of incoming traffic at electronic trading sites. Hence, back-end applications must actively control the influence of velocity and volatility of the incoming traffic.
Supplier relationships. As a general rule, the further a decision point is along the value chain, the higher the likelihood it will be affected by node density. Nevertheless, the nature of controls and interactions may cause node density to become critical at any decision point. For example, controls for the purchase function in Figure 3 occur at a relatively early stage in the value chain, and may affect the node density. Node density can be a factor along two dimensions: internal and external. Internal sources stem from the organization of the purchase department. A large purchase department with a centralized authority structure has the potential to congest the decision-making nodes. External source is a function of suppliers’ population in this example: selection amongst a large number of suppliers injects increased node density (and externally induced volatility) into the system every time a purchasing decision is involved. Further, the ability to respond to fluctuations is limited when information systems of the company and its suppliers are not effectively integrated.
Outsourcer relationships. If credit assessment is outsourced, it might affect the nature of information flow in the value chain. The level of integration of the outsourcer’s information system with the company’s system will determine the velocity of information. If the credit-check function is built into the company’s system, then the degree of integration within the ERP system would influence the velocity of flow. In specific instances, the velocity will also be affected by whether required information to perform the credit check is available internally or help is needed from a credit bureau system. Also, when the organization has some information for performing credit-check activities internally but requests assistance from credit bureau systems for additional corroborating information it could end up receiving contradictory information leading to higher viscosity.
Environmental factors. Besides intrabusiness transactions, the nature of flows between corporate intranets and external entities would also be moderated by various environmental factors. For example, a 500-point drop in the Dow Jones Index can increase the velocity and volatility of incoming traffic at electronic trading sites. The uncertainty in the oil market or a global financial crisis can result in high viscous flow of information between business entities. Additionally, legal factors sometimes force an increase in the number of nodes along the information flow—for instance, certain states do not allow cardirect.com to sell cars directly to consumers over the Internet; such restrictions would introduce more intermediary nodes before the final decision maker processes the information.
Finally, the overall utility of the information flow parameters has specific significance for enhancing system design and improving management control. System design provides the necessary infrastructure for information processing functions that influence the quality of the resulting output. The architecture of the design should, therefore, incorporate the flow parameters and their changes in real time. Knowledge of factors affecting process efficiencies via flow parameters assumes significance in providing effective management. In the future, organizations will evolve into intricate networks of dynamic relationships with external entities. The complexity of the resulting processes can best be managed by analyzing the parameters of information flow.
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