Enterprise Software Evolution

For several years now, a wide variety of enterprise-scope packaged applications – ranging in focus from enterprise resource planning (ERP) to manufacturing resource planning (MRP) to shop floor management – have been available for automating and managing business processes in corporations that are heavily involved in manufacturing and/or a supply chain. (The bottom layer in Figure 1 depicts these packaged applications.) In particular, the application modules devoted to manufacturing and supplier relations in ERP packages (primarily from Baan, Oracle Applications and SAP AG) have gained broad adoption in manufacturing industries. Also in this category, some corporations manage contacts with suppliers and distributors using packaged applications for customer relationship management (CRM), although these applications were not originally designed for this purpose.

In recent years, a category of software arose that focuses specifically on supply chain management (SCM). SCM applications (from vendors such as i2 and Manugistics) complement ERP and CRM by providing packaged transaction-based functionality for material management, demand planning and production planning services. (The middle layer of Figure 1 depicts this category of packaged applications.) Note that Internet trade exchanges (ITEs) such as Ariba and Commerce One purport to fulfill some of these functions; however, most ITEs are narrowly focused on procurement functions.


Figure 1: The Evolution of Enterprise Software

ERP and SCM applications have reached a maturity point where they are feature-rich, robust and well understood in terms of best practices. Even so, these are mostly devoid of analytic capabilities (with the exception of a few canned reports) that can give manufacturers and procurement specialists deep insight into manufacturing processes and supply chains.

Defining Supply Chain Intelligence

Packaged analytic applications for supply chain intelligence (SCI) are a new category of enterprise software that promises to provide insight into strategic issues for corporations in manufacturing industries. SCI applications enable strategic decision making by collecting detailed information from every stage of the product life cycle. (This new category is depicted in the top layer in Figure 1.) SCI provides a broad business intelligence layer that complements ERP and SCM.

SCI analytic applications operate on highly detailed information collected from the entire supply chain – from material procurement to the manufacturing floor through every stage of a product's life cycle and into the hands of the end consumer where warranty periods may apply. SCI takes a very broad view of the extended supply chain utilizing atomic-level data, which is requisite to understanding total cost and long-term quality ramifications.

For instance, the chain-wide view of SCI helps manufacturers relate design decisions (such as which components from which suppliers are selected) with failure rates in the field (which, in turn, generate costs due to warranty obligations). Understanding these long-term quality ramifications can help manufacturers reduce the inventory and cash reserves they must put aside for warranty fulfillment.

Furthermore, SCI can help explain production outages caused by quality or compatibility problems with specific materials from specific suppliers. These outages are well worth understanding, since increasing production yield even a small percentage through SCI can lead to millions of dollars in return, without investing in additional expensive manufacturing floor equipment in order to increase output.

In short, SCI provides a total understanding of quality, yield and costs that can lead to a rise in manufacturing performance.

SCI Complements Supply Chain Management

Packaged applications for SCM focus largely on managing the procurement and production links of the supply chain. SCI applications, however, provide a broad view of an entire supply chain to reveal a comprehensive product and component life cycle. This is just one of the many differences between SCM and SCI; Figure 2 summarizes several more. The lengthy list in Figure 2 proves that SCM and SCI are carefully differentiated.

Supply Chain Management Supply Chain Intelligence
Largely about managing the procurement and production links of the supply chain Provides a broad view of an entire supply chain to reveal full product and component life cycle
Transactional Analytic
Tactical decision making Strategic decision making
Helps reduce costs through improved operational efficiency Reveals opportunities for cost reduction, but also stimulates revenue growth
Usually just the SCM application's data (as a vertical stovepipe) Integrates suppplier, manufacturing and product data; possibly syndicated data, too (horizontal)
Records one state of data representing "now" Keeps a historic record
Assists in material and production planning What-if forecasting based on historic data
Quantifies cost of some materials Enables an understanding of total cost
Shows today's yield but cannot explain influences on it, thus provides no help for improvements Drills into yield figures to reveal what caused the performance level so it can be improved
Simple reporting Collaborative environment with personalizable monitoring of metrics

Figure 2: SCM Differentiated from SCI

The evolution of enterprise software illustrated in Figure 1 has been driven – at least in manufacturing industries – by real-world business issues involving the performance of the manufacturing floor or other links in the supply chain. Most manufacturers are under pressure to "move up to the next level" by increasing yield, reducing costs and improving time to resolution of problems on the shop floor. Manu-facturing performance often settles into a plateau after an increase. Manufacturers look for any leverage that can help them achieve critical velocity to raise performance above a plateau.

In many cases, enterprise software can be the leverage point, as illustrated in Figure 3. For many manufacturers, the software automation of ERP packages helped raise performance. After ERP-induced increases settled into a plateau, many manufacturers turned to SCM applications to lift them to another performance increase. Those same manufacturers are now entering a plateau from which they will need to escape. In these cases, SCI analytic applications can provide the leverage point for the next increase in performance.


Figure 3: Gains and Plateaus of Manufacturing Performance over Time

Software Requirements for Supply Chain Intelligence

Software that supports SCI has several base requirements:

Packaged Analytic Application. Ideally, SCI is enabled by a packaged analytic application with focused functionality for manufacturing specialists. This way, the prepackaged application delivers high value out of the box without need for undue amounts of development, as would be the case with a general analytic platform. Furthermore, a packaged analytic application for SCI encapsulates best practices for manufacturers and others involved in supply chain issues, whereas generic analytic platforms cannot. An analytic application for SCI should include functions for analyzing historic supply chain and manufacturing performance, as well as what-if analysis that helps to forecast material and production line needs.

Professional Services. A packaged analytic application (for any purpose, not just SCI) can never be as firmly packaged as packaged applications for ERP, CRM or SCM. This is because source data required for analysis varies greatly from one corporation to another, meaning that the data integration process is different for each deployment of a given analytic application. Furthermore, each corporation has a unique collection of business entities, so the data model that represents these in the analytic application must be customized for each deployment.

Hence, a software vendor providing an analytic application for SCI must also provide a healthy mix of professional services to aid with customizing the application for each customer, as well as providing training, support and project management. The consultants should have experience and training in manufacturing and supply chain domains, as well as technical areas such as data warehousing and analytic application development.

Domain Expertise. The primary value proposition of any analytic application for SCI is the domain expertise it encapsulates. Therefore, anyone evaluating an SCI application should look for proof of domain expertise in the product as well as in the vendor. For instance, the software vendor should have executives, developers and consultants who have demonstrable experience and successes in manufacturing (both process and discrete) and other supply chain-intense industries. The application should have functions designed specifically for SCI, such as data models and analytic algorithms for product life cycle information, shop floor data and parametric test equipment data (whether from the factory or the field).

Build versus Buy. For the classic build-versus-buy decision that many evaluators face a packaged application for SCI has the reduced time and cost benefits of "buy" combined with the customization-to- requirements benefits of "build". In other words, a packaged analytic application for SCI is faster to install and customize (months instead of years) than built-from-scratch supply chain decision support solutions. Furthermore, many organizations decide to build their own applications because they cannot find commercially available ones that satisfy their requirements; however, the customization process of an SCI application ensures that most of the customer's unique requirements are met.

Time and cost benefits aside, buying an analytic application for SCI delivers tremendous domain expertise and best practices that few in-house warehousing projects can match. The SCI vendor assists companies in deciding which analytic questions to ask, as well as providing the enabling technology to answer those questions. The reality is that most IT and software engineering organizations are highly effective with software exclusive issues, but simply lack the specific domain expertise required for building a truly comprehensive SCI application.

The time is ripe for manufacturers – especially those with ERP and SCM packaged applications in place – to "move up to the next level" by increasing yields, lowering supply costs and ensuring product quality with analytic applications for SCI. Evaluators should look for analytic applications that provide manufacturing-specific domain expertise, a comprehensive view of the entire supply chain and total product life cycle coverage.

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