Supply Chain Intelligence – Moving from Decision Support to Decision Expert System

Supply Chain Intelligence – Moving from Decision Support to Decision Expert System

Ever increasing market potential and continuously evolving models based on the concept of “Design anywhere, Build anywhere and Service anywhere” has led to a situation where supply chain or more appropriately, the supply network has become complex and difficult to manage. All major players are looking for ways to manage the supply network through
innovative ideas, tools, and techniques. Lean enablement, supply chain intel l igence (SCI ) , collaboration with business partners, and outsourcing non-core processes are of priority. In this paper, we propose what is going to be the most effective Decision Expert System in the SCI space for years to come.

This paper compares the proposed decision expert system (DES) with the typical decision support system (DSS) that is already available in the market, and explains how DES will address some of the gaps in the DSS model. This concept will in future become a framework for companies that plan to adopt and deploy a solution around SCI. This system will not only provide reports, dashboards, and analysis but also bring a human touch. This is done by providing step-by-step assistance towards arriving at a decision by using preconfigured guided analysis provided on demand by supply chain experts.

This paper provides examples that illustrate how companies can become more competitive by applying business intelligence and analytics at various levels of supply chain management. The paper also describes preferred solutions, with enabling options, that can be handy for companies that want to evaluate products based on certain criteria.

Why do we need “Decision Expert System”?

Be it the top floor or shop floor, information is required at various levels in various functions to make decisions. Management executives are finding it extremely difficult to accurately arrive at the actual manufacturing cost of their products. Production managers are struggling to have a better visibility of constraints on a real-time basis to make shop floor decisions and deliver products on time. Quality managers also feel that more than 95% of their actions are corrective in nature; they are looking for a solution that enables them to take preventive measures.

A decision support system (DSS) can provide answers to these questions up to an extent. However, the definition and spectrum of a DSS has been changing over decades with increasing complexity of the supply chain network. Evolution of data warehouse and online analytical processing has changed the way DSS was looked at and used three decades back. In today’s environment, various enterprise systems and point applications are generating huge amount of transaction data in a typical manufacturing organization. Data and information available is increasing day by day. These challenges make information extraction slower and inflexible from an already reactive DSS.

There are many challenges for organizations that have implemented decision support systems. Decision makers in these organizations are forced to evaluate models, which offer proactive and pre-guided analysis.

A DSS typically extracts relevant data from various sources, manipulates it through a data model and publishes predefined set of reports and dashboards so that users can use them for decision making. In this way, there are chances that users overlook potential analyses which can be done by using the same set of data.

This paper discusses methods to enhance the traditionally used system with additional functionalities and features so that it can provide all the answers that are expected from an expert. Simulations for some of the following ideas are also discussed.

• “What-if analysis”
• Proactive alerts based on performance and trends
• Predefined hierarchy of correlated metrics
• Decision tree on common supply chain issues
• Industry standard based metrics definitions
• Mechanism to identify repeat issues
• Best industry practices

With the help of these features, users have options to conduct a guided analysis, which in turn will result in faster and accurate decision making. This defines a decision expert system (DES) in a broader perspective. In summary a DES with its added functionality provides a platform for expert advice to business owners in taking intelligent decision (refer to the following exhibit).

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