Producing Value from Information and Market Data
The question isn't, "How does P&G sell soap?" but "How does P&G survive?" It must transform itself from a maker of mass-market goods into the world's largest boutique. After all, the consumer would obviously prefer not only the marketing message precisely tailored to him but the products as well. In this new market, there will be hundreds of versions of Tide or no Tide at all.
-- Michael Lewis
"The End of the Mass Market"
It is arguably the most important trend in manufacturing and customer service to happen in the last decade. There are many terms for it - mass customization, build-to-order, collaborative trading networks - but regardless of the name, the shift from one-size-fits-all to personalized production is having a seismic impact on manufacturing, distribution, and enterprise IT infrastructure. More than anything else, the ability to modify business strategy in the face of changing market conditions is decisive for long-term competitive advantage. And a crucial factor for rapidly optimizing business strategy rests on managing the information float, that is, the time between information discovery and information application.
Optimizing the information float has always been a critical factor in achieving enterprise efficiency. However, with the arrival of mass customization and collaborative supply chains, managing the information float is no longer under the control of an individual firm, but involves coordination by multiple parties. In that sense, a true collaborative supply chain involves more than just passing goods and product information sequentially from company to company. Collaborative trading networks are distinct in that the partners are working toward global optimization where shared market goals function alongside shared operational goals.
This has profound implications for how collaborative trading networks manage the information float. While "zero-latency" strategies have applied historically to internal enterprise information, collaborative supply chains are now required to integrate and map internal product information such as availability or inventory status with order and market information flowing up from the point-of-sale and external sources. Although a complex task, developing these information loops is vital to eliminate or lessen the incremental distortion that occurs when each participant responds with their own interpretation of supply and demand data. More importantly, the ability to integrate what occurs within a collaborative supply network with what is happening in the external market forms the basis for intelligent business decisions.
Consequently, the role of content is shifting like never before. Instead of solely being an input into the market research or production planning process, digital content has started to evolve from describing past events or reporting variance from a pre-determined plan toward becoming actionable, that is, actually making things happen in a supply chain - all in real-time.
While the content challenge for making internally generated data available and actionable is being addressed by a host of vendors and service providers, the issue of how to incorporate external data into a production process is only now being explored. This paper discusses the market environment around mass customization; provides an overview of the information float and actionable market data; and introduces Quantum Art as a provider of optimal information infrastructure solutions for incorporating external market data to achieve collaborative supply chain efficiency.
Implications of Mass Customization
A critical implication of mass customization, or customer-driven production, is that customer-touching business units - previously limited to sales, marketing, and customer service - have expanded to include engineering, manufacturing, and distribution as well. Simply put, the market leaders of the customer-driven economy must also be the leaders in customer-driven Supply Chain Management.
Consequently, enterprises are migrating from vertically-integrated supply chain structures to collaborative trading networks based upon the World Wide Web model of communication and interaction. The Web provides a ubiquitous piece of client software, the browser, as well as a universal infrastructure - thereby achieving flexibility, scalability, and affordability not possible under previous collaborative modes, such as telephone or Electronic Data Interchange (EDI). More importantly, the Web has created a self-reinforcing cycle of growth that makes it the benchmark for interoperability, scalability, and ubiquity.
But network connectivity is only part of the equation for collaborative supply chains. Collaborating firms also require visibility for mass customization to work. Visibility has special meaning in the context of mass customization. Rather than increasing production and inventory in advance of actual customer demand, businesses are looking to make their own supply chains and those of their customers and partners respond in real time to actual sales. Thus, visibility enables a business to analyze the interplay between interactions such as procuring materials from various suppliers, shifting production between installations or business partners, and moving goods to the final customer.
A Case Study
Real-world examples of this shift are compelling. In 1998, California-based Adaptec implemented Internet-based information exchange systems to communicate with its contract computer chip manufacturers in Asia. By collapsing the information float between when an order was received in the United States and when such information could be executed by a production system, Adaptec achieved a 33% reduction in order fulfillment cycle time and inventory savings of $10 million within six months.
Working with internal information such as data on supplier performance as well as inventory levels and capacity, enterprises are able to quickly gauge supply chain efficiency and thereby migrate to a build-to-order manufacturing mode. This means that customer orders finance the production process, instead of working capital, thereby freeing up capital that can be employed to research and develop new products and services. Moreover, the strategic value of such systems escalates when supply chain data is viewed in combination with other functional data in the enterprise such as marketing, finance, and human resources.
At the same time, while collaborative trading networks allow partners to align their operational goals, the need for market information external to the supply chain becomes critical. Just as the internal information float is collapsing due to the adoption of information technology, the external information float is also collapsing as customers possess nearly the same information processing capability as the manufacturers that serve them.
As production moves from a batch to a real-time mode, a collaborative chain's ability to gauge the likely profitability of an order becomes far more important than when it was common to produce large production lots that sat in inventory until they were sold. In that sense, a key profitability driver and a major differentiating factor for future competition will be the ability of a collaborative supply chain to take real-time action in response to changing market conditions.
Collapse of the Information Float
External events are influencing the cost base and value proposition of supply chains like never before. A recent Benchmarking Partners study indicated that over half of a supply chain's variable costs arise from external factors and decisions. Consequently, enterprises and their partners are quickly discovering that ubiquitous information processing (courtesy of Moore's Law), combined with inexpensive information transport via the Web, collapses the most important information float ofall - the time between when a product or service is introduced and feedback returns from the market.
Thus, the most important information float to manage for a collaborative trading network involves demand information extracted as close as possible to the final point of consumption. When this happens, organizations can re-orient their focus of supply chain efficiency, evolving it from being just the flow of materials from source to user toward being able to respond rapidly to the value proposition behind a given market signal (such as a bid-by on an auction or a competitive mark-down of a similar good).
In that sense, it is erroneous to assume that collapsing the information float is always synonymous with running "lean" operations. Instead, efficient collaborative supply chains are able to adjust output to match market demand and can switch rapidly from one product set to another. They are migrating from being forecast-driven to being demand-driven.
Functionally, these supply chains use information technology to enhance time spent doing something that creates a benefit for which the customer is willing to pay, for example, by prioritizing and reconfiguring a production schedule based upon whether a user requested overnight delivery or 3-5 working days. Other evidence of mass and time compression involves postponement strategies where a manufacturer delays customization of the product until the very last moment. Hewlett Packard is notable in its ability to ship near generic peripherals such as printers to distribution centers where they are localized upon receipt of a customer order.
Such supply chain agility will take on greater prominence as customized pricing grows. It is a contradiction to maintain uniform pricing with customized production. Given the fact that competition based upon information asymmetry is less feasible in a Web-centric environment, supply chains will constantly be analyzing not only the internal dynamics of producing and distributing goods based upon customer specifications, but also whether it makes business sense based upon a customer's willingness-to-pay. In that sense, as supply chains become more collaborative in order to become more internally agile, their need to understand the real-time market (in all its dimensions)of a given good or service and incorporate it into manufacturing and distribution decisions becomes that much more important.
Actionable Market Data
The objective of making data actionable is to push as many operational and tactical decisions into software as possible for efficient operations and to free up human beings for strategic decision-making. As a result, whether or not a given data set can be called "actionable" depends as much upon the business process it supports as the nature of the data itself.
That said, external data becomes actionable based on its syntactic and semantic characteristics. From a syntax point of view, data must be machine-readable in order for enterprise software to accept it from the outside, store it in a usable format, and employ it for analytic purposes. External data that must be captured by a separate system and/or input manually by a human being into a primary production system should not be considered actionable.
Semantic aspects that make data actionable are of more interest. The primary semantic aspect involves outside data that causes an operational business process to execute. Usually, this is based upon the degree that business processes are formalized and defined.
From an internal point of view, supply chain visibility systems such as i2's Global Logistics Manager (GLM) allow firms to track and trace the flow of their goods through a supply chain by, for example, communicating alert messages when pre-defined tolerances (perhaps a shipment is more than eight hours late) have been violated. Such messages are aggregated and analyzed via data storage and analysis tools to help firms identify bottlenecks, benchmark responsibilities between carriers and suppliers, and better understand actual distribution costs.
External data can be used in a similar manner in order to make it actionable. It might be the case where business analysts define the acceptable price band for a bundle of raw materials that feed a production process. From that point, a content provider regularly furnishes price information on that commodity bundle to the production system. The production or decision support system uses a decision table on how to proceed so long as the price for the given commodity bundles stays within the pre-defined band. Depending on the price volatility of the input, the system is refreshed on a regular basis with the latest market prices. Should the floor or ceiling of that price band be crossed, a workflow to human management is activated. At the same time, business analysts are able to take the external data feed and import it into analytic systems that allow them to continually refine their benchmarks.
Granted the importance of both internal and external data management for collaborative operations, it boggles the mind that in a supposed "Information Age," leveraging digital content to achieve market goals is often one of the worst executed business processes. The content challenge spans the length and breadth of internal and external data that comprise the "Information Supply Chain" that underpins collaborative activities. One reason for the content problem is the sheer variety of information required to execute a complex production process. More often than not, a supply chain must capture, analyze, communicate, and execute its business processes based on a small set of structured and larger set of semi-structured or unstructured data. Price lists and inventory figures first spring to mind when considering structured data but a host of other necessary information exists in a semi-structured format such as technical drawings, Engineering Change Orders (ECOs), product specifications, customer profiles, sales spreadsheets, etc.
Managing this internal information flow is difficult enough within a vertically-integrated firm. Taken to a collaborative environment, the challenge is compounded by the need to incorporate external market information so that the collaborating network may adopt some of the market-facing practices mentioned previously. Organizations that rely on human intervention to manage such information streams (or else lack truly integrated information systems) will face scalability and competitive issues as the business world migrates deeper into a Web-centric mode of production and distribution.
And while it is true that many software vendors and service providers have rallied to tackle the internal information challenge, this is only a partial solution. Without effectively incorporating external market data directly into the production process, collaborative supply networks must fall back on a forecast-driven planning model; thereby negating many of the possible efficiencies that enhanced information sharing might offer.
Current methods of receiving and processing external information to drive operational decisions are time-consuming and usually ineffective. What typically happens is that after establishing a commercial relationship with a content provider and engaging in lengthy price and information use negotiations, setting up payment terms and methods, and configuring an internal network to accept the content feed, the external information must often be reformatted and packaged for enterprise software handling, uploaded to analytic, decision support, ERP, data warehousing, or other supply chain applications, whereupon human managers wait for results, and take appropriate actions once results are returned.
And this is within a single enterprise!
Another downside of the current approach is the frequent incompleteness of data that is being used by the enterprise or its employees. It is often the case that information is purchased by different departments within a corporation and resides in autonomous systems or on user desktops, which renders it inactionable for enterprise purposes.
To counteract these constraints and address the fact that content is taking an active role in production operations, enterprise customers are demanding content that is both customized to the specifics of a firm or its supply chain and distributed in machine-readable format to be received, analyzed, and efficiently distributed across the enterprise.
But while customization and machine-readable delivery allows content providers to add "stickiness" to their offerings, that desire collides with the reality that custom development, distribution, billing, and collection for tailored data products incurs significant overhead cost, especially if such content needs to be shared among collaborating enterprise partners. At the same time, collaborative supply chains will require just those capabilities to make external market data actionable and take strategic steps to address changing market conditions.
Conclusion
Previously, the primary asset base of a supply chain consisted of physical assets such as inventory, loading docks, transportation facilities, rights-of-way etc, that combined to push product A through to customer B. Mass production and mass markets made such vertical strategy both workable and profitable. The main value driver for information technology in this context was its ability to replicate existing tangible business processes in the intangible form of electronic data.
Now, large organizations are attempting to transform themselves into providers of customized goods and services. It is likely they will view Supply Chain Management as the coordination of customer demand that pulls product through its transformation from primary materials to intermediate inputs to finished goods that eventually make their way to an end user. The main IT challenge here is to take information needed to manage demand (i.e., specification, price, availability, ordering status, installation, financing, upgrades, technical support and repair, etc.) and map it across shared IT and physical assets of partners. Only then can organizations have their information and data "produce" corporate value directly in the market, as if they were physical assets.
And the critical factor to produce such corporate value lies in how demand-led supply chains manage the intertwined information floats between internal supply chain dynamics and the external market. Viewing one in isolation to the other is a recipe for fair-to-middling return on investment. Instead, collaborative supply chains will have to integrate these data streams into both business processes and enterprise software in order to make their information assets directly actionable, and thereby valuable.
This is not to say that integrating external market data feeds into supply chain operations will be neat, parsimonious, logical, or elegant. Expect a significant amount of tinkering in order for external market feeds into real-time Supply Chain Management applications to become routine. However, such tinkering will move rapidly toward order - toward business models and technical architectures that handle complexity more effective than their predecessors. And as its value is tested and shared among partners, it is likely that future supply chain leaders will find it hard to conceive of any other way of doing business.
A Note on Quantum Art
Quantum Art Quantum Art is a leading Internet software and services company, providing a line of fully modular e-business applications for facilitating commerce, data exchange, and dynamic content delivery across a single enterprise or a collaborative supply chain. Quantum Art has designed a unique approach to application development and created a flexible architecture for rapid solution deployment and integration. The modular architecture of Quantum Arts core platform is ideally suited for rapid delivery of customized applications and seamless integration with any third-party solution or online service, including the integration of external market data into collaborative supply chains. One of the key benefits of Quantum Arts approach is the ability of application users to modify the feature set of an application without compromising already existing functionality. In addition to lowering the cost of application maintenance, this approach also offers a platform for modular integration, enhancing enterprise ability to instantly collaborate with its customers and suppliers, subscribe to online services, and modify application workflows in light of changing business strategy or market conditions. Based on open standards for increased interoperability, Quantum Arts infrastructure includes an integrated service platform for streaming machine-readable data originating from content providers, developing customized data products, and administering related data sales and procurement processes.
