Agent-Based Modeling of Third-Party Logistics Performance Indicators. Elfriede Krauth. Hans Moonen. Viara Popova. Martijn Schut. Rotterdam School of ...
Agent-Based Modeling of Third-Party Logistics Performance Indicators Elfriede Krauth
Hans Moonen
Viara Popova
Martijn Schut
Rotterdam School of Management Department of Management of Technology and Innovation RSM Erasmus University, Rotterdam (NL)
Artificial Intelligence Section Department of Computer Science Faculty of Sciences Vrije Universiteit Amsterdam (NL)
{ekrauth,hmoonen}@rsm.nl
{vn.popova,mc.schut}@few.vu.nl
Author names ranked in alphabetical order
Abstract Monitoring of relevant performance indicators can greatly contribute to the success of a logistics service provider. In practice, however, this is often done in an adhoc manner, leaving huge potential for improvement through adequate IT support. This paper proposes an approach to build an agent-based information system with emphasis on the interaction between the operational and strategic goals of the company. We present an example of the applied approach for the context of schedule generation and selection.
1. Introduction The design and use of any (agent-based) information system asks for a careful consideration of both the operational activities of a company at the work-floor as well as the pursued strategic goals. However, this requires an explicit exploration of knowledge, which is often only present in an implicit manner. For example, many small and medium sized companies do not explicify knowledge about the tangled web of performance indicators: are we doing well, and why? In this paper, we propose an organizational model that marries operational and strategic goals. We also include a step-by-step methodology that guides one through the computational specification design of the organizational model. The greater goal of our research is to contribute to building an information system that will support operations of logistics service providers (LSP) by providing a link between daily operations and strategic goals [4]. The focus on core competencies opened up many business opportunities for logistics service providers [1]. By nature, a logistics service provider is a highly complex, distributed system, spanning several organizational boundaries. Actors may operate in a partially independent way. Current systems are often
limited by their high complexity and low levels of flexibility. A more suitable approach is to represent the involved parties as agents operating in a multi-agent system [8]. Such a system can be adaptive by reacting to the changes in the environment in a distributed way without necessarily affecting the whole system. The complexity on the level of a single agent is limited, yet the whole system can generate solutions to highly complex tasks. The proposed organizational model consists of different planes for company divisions, performance indicators, roles and agents. The research objective of this work is to establish a synergy between operational and strategic activities of an organization by means of an agent-based approach. The remainder of the paper is structured as follows: Section 2 presents the organizational model and explains how to construct such a model. Section 3 discusses the performance indicators in the model; Section 4 presents the agents in the model. Section 5 discusses a case study. And finally, section 6 concludes and presents future work.
2. Organizational model We introduce an organizational model that consists of four planes (see Figure 1): 1) Company – description of the company structure, organigram, etc.; 2) Roles – represent responsible parties for different processes and activities in the company (persons, software, machines, etc.); 3) Indicators – contains the performance indicators, the relationships between them and requirements over them; 4) Agents – contains negotiating agents that exchange operational (roles) and strategic (indicators) information. The company plane represents the organization of the company in departments, sections, power relations, etc. without explicitly referring to the involved activities, and dynamics. The roles plane includes all roles involved in the company. A company employee may take up several
roles. Roles may also be automated, for example, route planning might be carried out by a computer program.
indicators
company
agents
roles
Figure 1. The four planes of the organizational model For the indicators plane, we utilize previous research [5] that focused on the relationships between performance indicators and on the requirements to be satisfied. Based on this information, analysis can be performed on detecting conflicts between the requirements. In the agents plane, we integrate the strategic priorities of the company (represented by the performance indicators in the corresponding plane) with the operational activities of the company (represented through roles in the corresponding plane). Therefore we distinguish between 1) strategic agents, which are responsible for performance indicators, and 2) operational agents, which represent different roles of people or automated processes in the company. Through a coordination mechanism, the agents interact and thereby support the modeled organization. Setting up the organization model that was presented above consists of a three step process. First, using domain experts, the performance indicators and their relationships are specified in the language from [5]. The second step is to select the key performance indicators and transform these into company specific requirements. Third, the full set of indicators is assigned to the strategic agents. The requirements are translated into agents’ goals, the relationships to agents’ communication channels. Also specific knowledge about the nature of the relationship is provided. The operational agents support the roles in the company. They distill performance information from data coming from the roles and existing applications.
3. Specifying indicators At the performance indicators’ plane there are several types of knowledge that need to be collected and coded. First, the list of performance indicators has to be identified. These indicators will often be connected by different types of relationships through which the changes in one indicator will influence changes in others. The second step is to identify these relationships. The third step is to incorporate the company’s preferences in its performance, i.e. which indicators are considered to be most important (key performance indicators), which of
their values should be considered successful performance and where should the company strive for improvement. Identifying these elements is not trivial and requires input from company’s management and domain experts. A comprehensive language will be beneficial in the discussions and in coding the knowledge into the software system. We use the language developed in [5] which is based on order-sorted predicate logic and inspired by requirements engineering. It is identified by a set of sorts, predicates and functions. We describe here only a subset of predicates informally. The relationships we consider are: correlated – correlation between indicators; causing – causality relation between indicators; aggregation-of – an indicator is a different aggregation level of another (per day/month/year, per employee/unit/company, etc.); included-in – an indicator is included in the definition of another. Each relationship has a sign: positive (maximizing one indicator will also maximize the other) or negative (maximizing one, will minimize the other). The sign of IS-AGGREGATION-OF is always positive. Examples CORRELATED(NC, NO, pos), CAUSING(MP, AP, neg), IS-INCLUDED-IN(NNC,NC,pos), IS-AGGREGATION-OF(NT,TT) where the names stand for: NC – number of customers, NO – number of orders, MP – personnel motivation, AP – personnel attrition, NNC – number of new customers, NT – number of trips, TT – trips per truck.
The language further allows defining of requirements regarding the indicators. Qualified expressions define constraints over the value of an indicator of the type: minimize(v), approximate(v,bestKD), satisfy(v
