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Information Systems in Supply Chain Management S.T.Sriskanda MSc.IS (SLIIT), PGD-IS (SLIIT), FISMM (SL), FIPMA (UK), MCSSL (SL), MAMM (Australia), MILog (UK), PhD (Scholar) st Supply Chain Management (SCM) has become the 21 century global operations strategy for achieving organizational competitiveness. Companies are trying to find ways to boost their flexibility and responsiveness and in turn competitiveness by alternating their operations strategy, methods and technologies that include the implementation of SCM paradigm and Information Technology (IT). However, it is impossible to fulfill an effective Supply Chain without IT. Since suppliers are located all over the world, it is essential to integrate the activities both inside and outside of an organization. This requires an integrated Information System (IS) for sharing information on various value-adding activities along the supply chain. Nowadays, the market is electronically connected and dynamic in nature. Therefore, companies are trying to improve their agility level with the objective of being flexible and responsive to meet the changing market requirements. In an effort to achieve this, many companies have decentralized their value-adding activities by outsourcing and developing Virtual Enterprise (VE). Recently the concepts of supply chain design and management have become a popular operations paradigm. This has intensified with the development of information and communication technologies (ICT) that include Electronic Data Interchange (EDI), the Internet and World Wide Web (WWW) to overcome the ever-increasing complexity of the systems driving buyer–supplier relationships. The complexity of SCM has also forced companies to go for online communication systems. For example, the Internet increases the richness of communications through greater interactivity between the firm and the customer (Watson et al., 1998). Graham and Hardaker (2000) highlight the role of the Internet in building commercially viable supply chains in order to meet the challenges of virtual enterprises. Philip and Pedersen (1997) attempt to study the ways in which the business community harnesses EDI with the help of a literature survey based on the application. Armstrong and Hagel (1996) argue that there is beginning of an evolution in supply chain towards online business communities. Supply Chain Management (SCM) is an approach that has evolved out of the integration of these considerations. SCM is defined as the integration of key business processes from end user through original suppliers that provides products, services, and information hence add value for customers and other stakeholders (Lambert et al., 1998). According to Simchi-Levi et al. (2000), SCM is a set of approaches utilized to effectively integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize system wide cost while satisfying service level requirements. Companies need to invest large amount of money for redesigning internal organizational and technical processes, changing traditional and fundamental product distribution channels and customer service procedure and training staff to achieve IT-enabled supply chain (Motwani et al., 2000). 1
The developments in IT have resulted many possible alternative solutions for managing the supply chain effectively by using Relational Database Management Systems (RDMS). A DMBS schedules concurrent access to the data in such a manner that users can think of the data as being accessed by only one user at a time. Further, DBMS protects users from the effects of system failures. There are three broad types of possible interrelationships among business units: tangible interrelationships, intangible interrelationships, and competitor interrelationships. All three types can have important, but different, impacts on competitive advantage and are not mutually exclusive. One of the major developments in IT which are transforming the supply chains today is Data Mining. Data Mining is the process of exploration and analysis by automatic or semi- automatic means of large quantities of data in order to discover meaningful patterns, sales & customer support. What is a Data Mining Environment? The data mining environment is the part (or parts) of an organization whose core competency is data mining. It includes several pieces: o A recognized group that develops data mining skills. o Communication pathways into one or more business units, so the work is focused on business needs. o A set of tools, hardware, and software, to affect data mining. o Access to data throughout the organization and the ability to publish results so they can be acted upon. Styles of Data Mining There are two styles of data mining. Directed data mining is a top-down approach, used when we know what we are looking for. This often takes the form of predictive modeling, where we know exactly what we want to predict. Undirected data mining is a bottom-up approach that lets the data speak for itself. Undirected data mining finds patterns in the data and leaves it up to the user to determine whether or not these patterns are important. o Directed Data Mining This is the approach used when we know what we are looking for, when can direct the data mining effort toward a particular goal. It means we do not care what the model is doing; we just want the most accurate result possible. o Undirected Data Mining Sometimes though, predictive accuracy is not the only or even the primary goal. Undirected data mining is about discovering new patterns inside the data. These patterns provide insight, and this insight might even prove very informative. We represent this form of data mining with semitransparent boxes. Unlike directed data mining, we want to know what is going on, we want to know how the model is coming up with an answer. 2
What Can Data Mining Do? Data Mining can be defined as set of activities, all of which involve extracting meaningful new information from the data. The six activities are: o Classification o Estimation o Prediction o Affinity grouping or association rules o Clustering o Description and Visualization The first three tasks - classification, estimation and prediction are all examples of directed data mining. In directed data mining, the goal is to build a model that describes one particular variable of interest in terms of the rest of the available data. The next three tasks - affinity grouping or association rules, clustering, description and visualization are examples of undirected data mining. In undirected data mining, no variable is singled out as the target; the goal is to establish some relationship among all the variables. Data Mining Techniques o Automatic cluster detection o Decision trees o Neural networks When to use Automatic cluster detection? Use cluster detection when you suspect that there are natural groupings that may represent groups of customers or products that have a lot in common with each other. These may turn out to be naturally occurring customer segments for which customized marketing approaches are justified. More generally, clustering is often useful when there are many competing patterns in the data making it hard to spot any single pattern. Creating clusters of similar records reduces the complexity within cluster so that other data mining techniques are more likely to succeed. When to use Decision trees? Decision tree methods are good choice when the data mining task is classification of records or prediction of outcomes. Use decision trees when your goal is to assign each record to one of a few board categories. Decision trees are also a natural choice when your goal is to generate rules that can be easily understood, explained and translated into SQL or a natural language. When to use Neural networks? Neural networks are a good choice for most classification and prediction tasks when the results of the model are more important than understanding how the model works. Neural networks actually represent complex mathematical equations, with lots of summations, exponential functions, and many parameters. Those equations describe the neural network, but are quite opaque to human eyes. The equation is the rule of the network, and it is useless for our understanding. 3
Neural networks do not work well when there are many hundreds of thousands of input features. Large numbers of features make it more difficult for the network to find patterns and can result in long training phases that never converge to a good solution. Here, neural networks can work well with decision-tree methods. Decision trees are good at choosing the most important variables and these can be used for training a network. Approaches to Data Mining There are essential four ways bring data mining expertise to bear on a company's business problems and opportunities. o By purchasing scores from outside vendors that are related to your business problem; analogous to using an automatic Polaroid camera. o By purchasing software that embodies data mining expertise directed towards a particular application such as credit approval, fraud detection or churn prevention; analogous to purchasing a fully automated camera. o By hiring outside experts to perform predictive modeling for you for special projects; analogous to hiring a wedding photographer o By developing In-House expertise within your organization; analogues to building your own darkroom and becoming a skilled photographer with your skills. The Virtuous Cycle of Data Mining The virtuous cycle of data mining, highlights the fact data mining does not exist in a vacuum. It is a high-level process, consisting of four major business processes; - Identifying the business problem - Transforming data into actionable resulting - Acting on the results - Measuring the results There are no shortcuts success in data mining requires all four processes. Results have to be communicated and, over time, we hope that expertise in data mining will grow. Expertise grow as organizations focus on the right business problems, lean about data and modeling techniques, and improve data mining processes based on results of previous efforts. However, IT is like a nerve system for SCM. There are many articles on IT in supply chain. A comprehensive survey of IT in SCM will be useful to identify the critical success factors of IT for an integrated supply chain. Unfortunately, design and implementation of IT system for an effective SCM have not received adequate attention from both researchers and practitioners, in particular, business to business (B2B) e-commerce (EC) and SCM. 4
The following are some of the problems often cited in the literature both by the researchers and practitioners when developing an IT-integrated SCM: o Lack of integration between IT and business mode o Lack of proper strategic planning o Poor IT infrastructure o Insufficient application of IT in Virtual Enterprise o Inadequate implementation knowledge of IT in SCM Still there are lots of debates around the applications of IT in SCM concerning business to business e-commerce model, matching to business model, etc. References: R. RAMAKRISHNAN and J. GEHRKE. (2003). Database Management Systems M. E. PORTER (1998). Competitive Advantage M. J. A. BERRY and G. S. LINOFF. (2000). Mastering data mining the art and science of customer relationship management. GUNASEKARAN, A. and NGAI, E.W.T. 2004, Information systems in supply chain integration and management, https://www.umassd.edu/media/umassdartmouth/businessinnovationresearchcenter/publi cations/ism_scm.pdf. 5