You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review On Evaluation And Benefits Of Decision Support Systems 
Chapter Title:  Knowledge-driven DSS 
1. Abstract of Cited Paper (BIB001):  The problem of bidding on a customized product in a manufacturing environment is very complex. Such a bidding requires determination of labor, material, production process requirements, outsourcing strategies, if necessary, and the recognition of profit margins. The complexity may arise from deciding production process requirements, which may involve hundreds of steps, selection of appropriate raw materials, and computation of profit margins in the presence of various estimates. This process is expert intensive and time consuming. The purpose of this paper is to develop a knowledge based decision support system to deal with bidding problem for a forging and steel company. The decision support system captures engineers’ expertise, uses group technology principles for product classification, and uses the business domain specific knowledge. It is implemented in Microsoft Access, a relational database system. The objectives of this decision support system were to cost majority of the jobs efficiently and effectively, standardize the bidding process, aid the salespersons, and provide means to facilitate learning from past successful and unsuccessful bids. This decision support system with few modifications can be used in a variety of manufacturing and service job order environments. 
2. Abstract of Cited Paper (BIB002):  This section addresses the automated and legally compliant determination of key contract parameters for commercially and internationally provided electronic services in the Internet. It designs and implements a decision support system that provides recommendations on suited contract parameters at the time of contract formation. Recommendations base on automated reasoning taking into consideration the relevant set of service- and contract-specific facts. The range of facts to be considered is imposed by the laws of these states that have sufficient connection with the international service contract to be concluded. Accordingly, this section develops and documents the solid basis for the decision support system, consisting of an information model that integrates perspectives of service and contract management as well as in a detailed method to formally model the legal basis for machine execution.

Literature Review:
Knowledge-driven DSS use a knowledge base extracted from the tacit knowledge of an expert. An early example of a knowledge-driven DSS is in BIB001 . Such a system makes a knowledge base available for interaction with a decision-maker. It allows heuristics embedded in the knowledge base to be combined with algorithmic knowledge found in models and data found in a database to be combined with the decision-maker's insights. The difference between this type of system and a knowledge management system is that the knowledge in a knowledge-driven DSS is structured, often into a rule-base or some other heuristic mechanism. Sklar et al (1990) describe a system that uses experts' knowledge coded in an expert system to build linear programming models. The resulting system is able to automate the model construction process, but it is up to the decision-maker to describe the problem to the system and then to execute the model and interpret the results. The benefit of the system is to incorporate experts' judgments into the process of constructing mathematical models and to speed up the model construction process. BIB002 use domain-specific knowledge to estimate costs for customized products. The benefit is in a more efficient bidding process. Using the system, engineers are able to estimate costs in 35 minutes for a bid that had required eight hours prior to implementation of the system. The system operates in a development environment, enabling engineers to manipulate requirements, and in a consulting environment, allowing sales personnel to prepare bids for jobs. The bidding process is carried out by the inference engine, consisting of a feature agent, steel cost agent, manufacturing cost agent and profit agent. Sales personnel work with the consulting environment to facilitate the bidding process. Feedback from both engineers and sales personnel following the initial testing phase is that is the system is useful, indicating it is both user-friendly and easy to update. Some problems are encountered thereafter, however, due to programmer turnover and lack of documentation. Additionally, the authors determine that explanation facilities would make the system more acceptable to users. BIB003 discusses DeRISC (Dispute rEsolution Recommender for International Service Contracts), a knowledge-driven DSS, providing support for international service contracts between service providers and their customers. The system provides Private International Law (PIL)-compliant recommendations for contract parties concerning jurisdiction and applicable law. The purpose is to provide improved risk assessment for both contract parties throughout the service life cycle. The DSS implementation provides the first completely machineexecutable implementation of Brussels I regulation. Wright et al (2009) discuss decision support capabilities included in commercially-available electronic medical record and computerized provider order entry systems. Generally, the DSS capability is a trigger that warns a provider when he/she is about to make a potentially dangerous order. These situations would include orders for drugs that a patient is allergic to or orders for drugs that have a harmful interaction with an already-prescribed drug. The decision support capability is advisory, and the provider has the ability to ignore the warning message. The realworld implications of these systems include reductions in the rate of adverse drug events.