This paper describes the goals and architecture of the Integrated Supply Chain Man-agement System (ISCM) being developed at the University of Toronto. ISCM provides an approach to the realtime performance of supply chain functions.
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The Integrated Supply Chain Management System December 7, 1993 1
The Integrated Supply Chain Management
System
Mark S. Fox, John F. Chionglo, Mihai Barbuceanu
Department of Industrial Engineering, University of Toronto
4 Taddle Creek Road, Toronto Ontario M5S 1A4
tel: 1-416-978-6823; fax: 1-416-978-3453; internet: msf@ie.utoronto.ca
Area:
Manufacturing and Production Systems
Abstract:
This paper describes the goals and architecture of the Integrated Supply Chain Man-
agement System (ISCM) being developed at the University of Toronto. ISCM provides an
approach to the realtime performance of supply chain functions.
1.0 Introduction
This paper describes the architecture of the Integrated Supply Chain Management System (ISCM)
under development in the Enterprise Integration Laboratory at the University of Toronto.
In response to competetive pressures, managers are focusing on the reengineering of operations.
Processes are being streamlined and automated, and work teams are reorganized and redeployed
for higher productivity. Together with these changes, companies are looking for ways to better
plan and control their operations. They are shifting away from a company with rigid and pre-
planned activities to one that is able to react quickly and appropriately to changes.
The supply chain is a set of activities which span enterprise functions from the ordering and
receipt of raw materials through the manufacturing of products through the distribution and deliv-
ery to the customer. In order to operate efficiently, these functions must operate in an integrated
manner. Providing rapid and quality responses to supply chain events requires the coordination of
multiple functions across the enterprise.
Supply chain management functions operate on three levels: strategic level, tactical level, and
operational level.
The Integrated Supply Chain Management System December 7, 1993 2
FIGURE 1. The Supply Chain Management Functions
Each level is distinguished by the period of time over which decisions are made, and the granular-
ity of decisions during that period. The strategic level addresses issues like: where to allocate pro-
duction, and what is the best sourcing strategy. The tactical level addresses issues like:
forecasting, scheduling, ordering of short lead time materials, and do we schedule overtime to
meet production requirements. The operations level addresses issues like: inventory deployment,
detailed scheduling, and what to do with an order when a machine breaks down.
Supply chain management also requires coordination with customers and suppliers. The dynamics
of the market make this difficult. Customers often make changes or cancel orders. Suppliers may
provide incorrect materials or deliver late. Systems that can quickly respond to market dynamics
while minimizing lead times and inventory are required.
Like the market, the production floor is also dynamic. Unplanned events occur and cause devia-
tions from scheduled activities. To acheive planned production, it is necessary for the production
control system to dynamically respond to these events in ways that optimizes production goals. In
some cases, events cause problems that are not "locally contained". The production control sys-
tem must coordinate its actions with higher-level functions such as planning, sales, and market-
ing.
In the remainder of this paper, we describe the architecture of the ISCM system, its agents and
their interactions.
2.0 System Design Issues
We view the supply chain as being managed by a set of intelligent (software) agents, each respon-
sible for one or more activities in the supply chain, and each interacting with other agents in the
planing and execution of their responsibilities. An agent is a software process that operates asyn-
chronously, communicating with other agents as needed.
The first issue we face is deciding how supply chain activities should be distributed across the
agents. Existing decompositions, as found in MRP systems, were limited by the sophistication, or
lack there of, of algorithms. This is exemplified by the distinction between MRP I and MRP II,
Orders
Monthly
Forecasts
Enterprise
Distribution
Planning
Weekly
Forecasts
Distribution
Requirements
Planning
Inventory
Deployment
Enterprise
Production
Planning
Master
Production
Scheduling
Process
Level
Scheduling
Enterprise
Material
Planning
Material
Requirements
Planning
Material
Releases
Strategic
Level
Tactical
Level
Operational
Level
Demand
Management Distribution Manufacturing Materials
The Integrated Supply Chain Management System December 7, 1993 3
which arises out of the move from infinite to finite Master Production Scheduling. We believe that
the successful planning and execution of supply chain activities relies upon more sophisticated
planning and scheduling algorithms than are available in current MRP systems. We view the plan-
ning/scheduling function as the "conductor" that "orchestrates" the behaviour of the other supply
chain agents. Consequently, the nature of the reasoning performed by other agents will change.
With more sophisticated planning/scheduling algorithms, the overall quality of supply chain man-
agement will increase.
The second issue is the nature of interactions among agent? Given the dynamics of the supply
chain resulting from unplanned for (stochastic) events such as transportation problems, supply
problems, etc., what is the nature of the interactions among agents that will result in the reduction
of change-induced perturbations in a coordinated manner? If each agent has more than one way to
respond to respond to an event, how do they cooperate in creating a mutually acceptable solution?
In other words, how do agents influence or constrain each other's problem-solving behaviour?
The third issue is responsiveness. In a dynamic environment, the time available to respond may
vary based on the event. It is a requirement that an agent's algorithm be able to respond within the
time allotted. Algorithms that are able to generate a solutions no matter how much time is avail-
able are know as "anytime" algorithms. The quality of the solution of anytime algorithms is usu-
ally directly related to the time available.
The fourth issue is the availability of knowledge encapsulated within a module. In conventional
MRP systems, a module is designed to perform a specific task. The modules may contain certain
knowledge (used in the performance of each task) that could be used to answer related questions.
It is our goal to "open up" a module's knowledge so that it can be used to answer questions
beyond those originally intended.
In summary, the next generation supply chain management system will possess the following char-
acteristics:
Distributed:
The functions of supply chain management are divided among a set of separate,
asynchronous software agents.
Dynamic:
Each agent performs its functions asynchronously as required, as opposed to a batch or
periodic mode.
Intelligent:
Each agent is an "expert" in its function. Uses Artificial Intelligence and Operations
Research problem solving methods.
Integrated:
Each agent is aware of and can access the functional capabilities of other agents.
Responsive:
Each agent is able to ask for information and/or a decision from another agent - each
agent is both a client and a server.
Reactive:
Each agent is able to respond to events as they occur modifying is behaviour as
required, as opposed to responding in a pre-planned, rigid, batch approach.
The Integrated Supply Chain Management System December 7, 1993 4
Cooperative:
Each agent can cooperate with other agents in finding a solution to a problem - that
is, they do not act independently.
Interactive:
Each agent may work with people to solve a problem.
Anytime:
No matter how much time is available, an agent is able to respond to a request, but the
quality of the response is proportional to the time given to respond.
Complete:
The total functionality of the agents must span the range of functions required to man-
age the supply chain.
Reconfigurable:
The supply chain management system itself must be adaptable and must support
the "relevant subset" of software agents. For example, if the user only wants to schedule a plant,
he/she should not be required to use or have a logistics component.
General:
Each agent must be adaptable to as broad a set of domains as possible.
Adaptable:
Agents need to quickly adapt to the changing needs of the human organization. For
example, adding a resource or changing inventory policy should be quick and easy for the user to
do.
Backwards Compatible:
Agents need to have a seamless upgrade path so that the release of new
or changed features does not compromise existing integration or functionality.
3.0 Architectural Overview
The ISCM is composed of a set of cooperating agents, where each agent performs one or more sup-
ply chain management functions, and coodinates its decisions with other relevant agents. There are
two types of agents: functional agents and information agents. Functional agents plan and/or con-
trol activities in the supply chain. Information agents support other agents by providing informa-
tion and communication services.
The decomposition of supply chain functions and their allocation to agents represents one of the
first tasks in the project. The problem is that existing decompositions of functions, as found in MRP
systems today, arose out of organizational constraints, legacy systems and limitations on algo-
rithms. For example, the distinction between Master Production Scheduling and Detailed Sched-
uling is primarily due to algorithm limitations. The merging of these two functions and the
inclusion of some activities found in Inventory Management and Activity Planning is possible with
the availability of more sophisticated planning and scheduling algorithms. We are currently work-
ing on six functional agents: Logistics, Transportation Management, Order Acquisition, Resource
Management, Scheduling and Dispatching. They are described in more detail in the next section.
The Integrated Supply Chain Management System December 7, 1993 5
FIGURE 2. The ISCM agents
The dynamics of the environment make cooperative behaviour an important factor in integrating
supply chain agents. In order to optimise supply chain decisions, an agent cannot make a locally
optimal decision, but must determine the affect its decisions will have on other agents, and choose
an alternative that optimises the entire supply chain
An agent is responsible for a set of functions or activities in the supply chain. Each agent stores
information and knowledge locally and it may access information and knowledge throughout the
network. We assume that the agents are in a heterogenous environment; hence, their interactions
are made through message-based transactions.
Supply chain agents exist within an Enterprise Information Architecture (EIA). The EIA provides
a distributed information environment where information may be stored anywhere in the network.
The EIA manages the consistency of information. Subsets of information may be designated has
being globally consistent and the EIA manages it. Other information, in which copies are stored
locally by agents, may develop inconsistencies and are of no concern to the EIA.
The EIA provides each agent with automated information acquisition and distribution. When an
agent requests information, the EIA will find it. When an agent creates information of interest to
others, the EIA will distribute it to those agents that wish to know. The EIA provides the "right
information in the right way" to the decision makers.
Order Acquisition
Scheduling
Resource
Management
Dispatching
Transportation
Management
Logistics
Information
Agent
Information
Agent
The Integrated Supply Chain Management System December 7, 1993 6
At the core of the EIA and the supply chain management system lies a generic reusable enterprise
model. In order to support the integration of supply chain agents, it is necessary that shareable
representation of knowledge be available that minimizes ambiguity and maximizes understanding
and precision in communication. The enterprise model will also support "deductive query pro-
cessing". Many of the terms in the generic model will be defined using Prolog axioms. These axi-
oms will automate the answering of a significant number of questions raised by the system's
users, thereby reducing software development costs.
4.0 Functional Agents
As said earlier, we believe that the successful planning and execution of supply chain activities
relies upon more sophisticated planning and scheduling algorithms than are available in current
MRP systems. We view the planning/scheduling function as the "conductor" that "orchestrates"
the behavior of the other supply chain agents. Consequently, the nature of the reasoning per-
formed by other agents will change. With more sophisticated planning/scheduling algorithms, the
overall quality of supply chain management will increase. The rest of this section describes
briefly each of the functional agents under development.
Order Acquisition agent
. This agent is responsible for acquiring orders from customers, negoti-
ating with customers about prices, due dates, etc., and handling customer requests for modifying
or canceling respective orders. This agent is one of the agents participating in negotiation that
may be necessary to successfully create supply chain plans. These will be exceptional situations
where other agents find an over-constrained situation requiring modification of constraints.
This agent captures the order information from directly from customers and communicates these
orders to the logistics agent. When a customer order is changed, it is communicated to the logis-
tics agent. When plans violate constraints imposed by the customer (such as due date violation),
the order acquisition agent participates in negotiating with the customer and the logistics agent for
a feasible plan.
Logistics agent
. This agent is responsible for coordinating multiple-plants, multiple-supplier, and
multiple-distribution center domain of the enterprise to achieve the best possible results in terms
of goals of the supply chain, which include ontime delivery, cost minimization, etc. It manages
the movement of products or materials across the supply chain from the supplier of raw materials
to the customer of finished goods.
The inputs to the logistics agent are customer orders, deviations in factory schedules which affects
customer orders, transportation plans and resource availabilities. The outputs of the agent are pro-
duction requirements for each factory, supplier, etc., and transportation requirements.
Transportation agent:
This agent is responsible for the assignment and scheduling of transpora-
tion resources in order to satisfy inter-plant movement requests specified by the Logistics Agent.
It will be able to consider a variety of transportation assets and transportation routes in the con-
struction of its schedules.
The Integrated Supply Chain Management System December 7, 1993 7
Scheduling agent
. This agent is responsible for scheduling and rescheduling activities in the fac-
tory, exploring hypothetical "what-if" scenarios for potential new orders, and generating sched-
ules that are sent to the dispatching agent for execution.
The inputs to the scheduling agent are production requests from the logistics agent, resource prob-
lems from the resource agent, and the deviations of the current schedule from the dispatching
agent. Its output is a detailed schedule.
The scheduling agent assigns resources and start times to activitites that are feasible while at the
same time optimizing certain criteria such as minimizing WIP or tardiness. It can generate a
schedule from scratch or repair an existing schedule that has violated some constraints.
In anticipation of domain uncertainties such as machine breakdowns, material inavailability, etc.,
the agent may reduce the precision of a schedule by increasing the degrees of freedom in the
schedule for the dispatcher to work with. For example, it may "temporally pad" a schedule by
increasing an activity's duration, or "resource pad" an operation by either providing a choice of
more than one resource or increasing the capacity required so that more is available.
The scheduling agent also acts as a coordinator when infeasible situations arise. It has the capa-
bility to explore tradeoffs among the various constraints and goals that exist in the plant..
Resource agent
. The resource agent merges the functions of inventory management and purchas-
ing. It dynamically manages the availability of resources so that the schedule can be executed. It
estimates resource demand and determines resource order quantites. It is responsible for selecting
suppliers that minimizes costs and maximizes delivery. It generates EDI purchase requests and
monitors their fullfilment.
The inputs to the resource agent are the schedule from the scheduler, the availability or unavail-
ability of resources from suppliers, the arrival of resources from the factory floor, and the con-
sumption of resources from the dispatcher. The outputs of the resource agent include the arrival of
resources, the availability of resources, and the orders sent to suppliers.
The resource agent generates purchase orders and monitors the delivery of resources. When
resources do not arrive as exepcted, it assists the scheduler in exploring alternatives to the sched-
ule by generating alternative resource plans.
Dispatching agent
. This agent performs the order release and realtime floor control functions as
directed by the scheduling agent. It operates autonomously as long as the factory performs within
the constraints specified by the scheduling agent. When deviations from schedule occur, the dis-
patching agent communicates them to the scheduling agent for repair.
The inputs to the dispatching agent are the schedule from the scheduling agent, the status of the
factory floor, and the availability of resources. The outputs are the deviations from the current
schedule and the starting of activities.
Given degrees of freedom in the schedule, the dispatcher makes decisions as to what to do next. In
deciding what to do next, the dispatcher must balance the cost of performing the activities, the
The Integrated Supply Chain Management System December 7, 1993 8
amount of time in performing the activities, and the uncertainty of the factory floor. For example,
a) given that the scheduler specified a time interval for the start time of a task, the dispatcher has
the option of either starting the task as soon as possible (JIC) or starting the task as late as possible
(JIT), b) given that the scheduler did not specify a particular machine for performing the task, the
dispatcher may use the most "cost effective" machine (minimize costs) or use the "fastest"
machine (minimize processing time).
5.0 Enterprise Information Architecture
An Enterprise Information Architecture (EIA) provides communication and information services
supporting:
•
Persistent storage of information to be shared among the multiple functional agents in the cor-
porate network.
•
Deductive capabilities allowing new information to be inferred from existing information.
•
Automatic distribution of information to the agents that need it.
•
Automatic retrieval, processing and integration of information that is relevant to agents.
•
Checking and maintaining various forms of consistency of the information.
•
Performing information access control functions such as determining who is allowed to see
and change the available information.
The EIA is composed of both functional agents and information agents (IA). An IA services a
number of agents (functional and other IA-s) by providing them with a layer of shared informa-
tion storage and services for managing it. Agents periodically volunteer some of their information
to the IA (and keep it up to date) or just answer the queries sent to them by the IA. The IA uses its
own information together with the supplied information to determine which information needs of
other agents can be satisfied. It processes the information in order to determine the most relevant
content and the most appropriate form for the needs of these agents. In the process, it may
uncover various forms of inconsistency among the supplied information and take action to
remove them. IA-s will also communicate with each other in order to accomplish their functions.
IA-s are not meant to replace the direct communication channels established among agents during
their usual interactions. Rather, they support these interactions by providing shared access to
information and the basic information management services listed above. IA-s will be particularly
useful in cases when:
1. A consistent form of shared information needs to be maintained.
2. Information from many sources needs to be aggregated, perhaps in a continous fashion, to pro-
duce reports or answer queries,
3. Information has to be distributed among many agents.
The Integrated Supply Chain Management System December 7, 1993 9
4. Changes in the state of the modeled enterprise need to be propagated over the models and activ-
ities of various agents.
5. Inconsistencies arising during agent interaction need to be quickly detected and resolved.
6.0 Agent Interaction
Given the dynamics of the supply chain resulting from unplanned for (stochastic) events such as
transportation problems, supply problems, etc., what is the nature of the interactions among
agents that will result in the reduction of change-induced perturbations in a coordinated manner?
If each agent has more than one way to respond to respond to an event, how do they cooperate in
creating a mutually acceptable solution? In other words, how do agents influence or constrain
each other's problem-solving behavior?
In order for two or more agents to cooperative, there must exist a "cultural assumption". The
existence of a cultural assumption implies what an agent can expect in terms of another agent's
behavior in a problem solving situation. A possible cultural assumption is that agents are "con-
straint-based problem solvers." That is, given a set of goals and constraints, they search for a solu-
tion that optimizes the goals and satisfies the constraints. Another cultural assumption could be
that agents have the ability to generate more than one solution. Thereby the enabling the consider-
ation of alternatives and trade-offs by a set of cooperating agents. A third cultural assumption is
that agents have the ability and authority to subpotencies local goals and possibly relax a sub set
of constraints if the global solution is further optimized.
Our approach to coordination is to view agent problem-solving as a constraint satisfaction/optimi-
zation process. An agent solves a problem by first understanding what constraints and goals exist,
then intelligently searching for a solution that satisfies the constraints and optimize the goals as
best as it can. When an agent's problem-solving relies upon or affects the problem-solving of
another agent, it must interact with it. We believe that an agent can modify another's problem-
solving behavior through the communication of constraints. Research has demonstrated the power
of this approach [Fox 83] [Fox 86] ] [Fox & Sycara 90] [Sycara et al. 92]. Coordination occurs
when agents develop plans that satisfy their own internal constraints but also the constraints of
other agents. Negotiation occurs when constraints, that cannot be satisfied, are modified by the
subset of agents directly concerned.
7.0 Enterprise Model
At the core of the EIA and the supply chain management system lies a generic reusable enterprise
model. In order to support the integration of supply chain agents, it is necessary that shareable
representation of knowledge be available that minimizes ambiguity and maximizes understanding
and precision in communication. The goal of the TOVE Enterprise Modelling project is to create
a data model that has the following characteristics: 1) provides a shared terminology for the enter-
prise that each agent can jointly understand and use, 2) defines the meaning of each term (aka
semantics) in a precise and as unambiguous manner as possible, 3) implements the semantics in a
set of axioms that will enable TOVE to automatically deduce the answer to many "common
The Integrated Supply Chain Management System December 7, 1993 10
sense" questions about the enterprise, and 4) defines a symbology for depicting a term or the con-
cept constructed thereof in a graphical context.
We approach the first goal by defining a generic level representation which the application repre-
sentations are defined in terms of. Generic concepts include representations of Time [Allen 84] ,
Causality [Rieger 77] [Bobrow 85] , Activity [Sathi 85] , and Constraints [Fox 83] [Davis
87] . The generic level is, in turn, defined in terms of a conceptual level based on the 'terminolog-
ical logic' of KLONE [Brachman 85] .
We approach the second and third goals by defining a set of axioms (aka rules) that define com-
mon-sense meanings for the terminology. By common sense, we mean that the more obvious def-
initions/deductions about the entities and attributes in our ontology. (We view definitions as being
mostly circuitous, as opposed to be reducible to a small set of grounded terms.) What is an obvi-
ous deduction should be determined by a subset of questions used to determine the competence of
a representation. Since there does not exist a standard for determining the competence of a model,
we will define, in english, a set of questions and the axioms used to answer them.
To date we have developed ontologies for activity, state, time, causality, resources, constraints,
quality, cost and organization structure.
TOVE is not only a research project but a testbed. TOVE has been used to implement a
virtual
company
whose purpose is to provide a testbed for research into enterprise integration. TOVE is
implemented in C++ using the ROCK@+[TM] knowledge representation tool from Carnegie
Group. TOVE operates "virtually" by means of knowledge-based simulation [Fox 89] .
8.0 Conclusions
The goals of the Integrated Supply Chain Management Project are:
•
Identifying an appropriate decomposition of supply chain functions and encapsulate into
agents.
•
Developing protocols and strategies for the communication of information, coordination of
decisions, and management of change.
•
Develop/use state-of-the-art algorithms for agent decsion-making.
•
Developing an incremental, "anytime" model of problem solving for each functional agent so
that it can provide rapid responses to unplanned for events.
•
Extending each function oriented agent so that it is able to answer more questions within its
functional domain.
The Integrated Supply Chain Management System December 7, 1993 11
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[Bobrow 85] Bobrow, D.G.
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[Bobrow 77] Bobrow, D., and Winograd, T. KRL: Knowledge Representation Language.
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[Brachman 77] Brachman, R.J.
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[Brachman 79] Brachman, R.J. On the Epistemological Status of Semantic Networks.
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[Esprit 90] ESPRIT -AMICE. CIM-OSA - A Vendor Independent CIM Architecture.
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[Martin 83] Martin, C., and Smith, S.
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... Supply chains have become of great strategic importance in today's corporate context as effective supply chain management leads to high performing supply chains [2]. Supply chain able to improve inventory management [3] and proposed a form to take supply chain inventory problems and opportunities [4] that describe the objectives and architectures of Integrated Supply Chain Management System (ISCM). Supply chain can be arrange by a set of responsibility of agent intelligent for planning and implementation. ...
... Inter-organizational systems aim to support SCM which older ERP systems lacked the capacity to realize. Scholars see this high level of integration as complex and have tried to propose models to address the challenge (Fox et al, 1993;Hazel, 2000;Wolfert et al, 2010). These authors, however, have failed to identify the fact that while developed countries are trying to take a step ahead, developing countries are yet to be abreast with the traditional ERP systems back office functionalities due to bottlenecks that emanate from lack of process technology employee (Carutasu, 2006). ...
... Ahora bien, se puede volver un sistema integrado de gestión inmanejable cuando los procesos que involucran el desarrollo de las actividades se vuelven complicados (Fox, Chionglo, y Barbuceanu, 2013). Claro ejemplo es el caso de estudio, al ser un Laboratorio que pertenece al Sector Público, éste no posee independencia para poder llevar a cabo todo lo relacionado con la implementación de un SIG, ya que depende de otras áreas como son: Contabilidad, Facturación, Talento Humano, Seguridad y Salud Ocupacional, las cuales no necesariamente se manejan con un Sistema Integrado de Gestión propio. ...
El Laboratorio de Análisis Instrumental ubicado en el 5to piso del Edificio No. 17 de la Escuela Politécnica Nacional brinda servicios de análisis de gases de pozo, biogas, biocidas, solventes, vidrios laminados y templados, entre otros; no obstante, no cuenta con un sistema de ISO 9001:2015 ni ISO 14001:2015, los cuales deberían ser una base fundamental a fin de permitir una dirección basa en la optimización de procesos con una disminución de los impactos ambientales generados por la empresa. Con estos antecedentes, el presente trabajo tiene por objetivo desarrollar un mecanismo para implementación de un SIG y que vaya acorde a la normativa ISO 17025:2005 como parte de la acreditación del mismo. Los resultados obtenidos muestran que se utilizará la técnica de integración total, propuesta por Block y Marash (2000) y la técnica de alineamiento propuesta de Ferguson et al. DOI: https://doi.org/10.33333/rp.vol42n2.959
Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since early 2000; industrial uptake of them has been lagging. The reasons quoted include the immaturity of the technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.
Agent-based systems have the capability to fuse information from many distributed sources and create better plans faster. This feature makes agent-based systems naturally suitable to address the challenges in Supply Chain Management (SCM). Although agent-based supply chains systems have been proposed since the early 2000s; industrial uptake of them has been lagging. The reasons quoted include the immaturity of technology, a lack of interoperability with supply chain information systems, and a lack of trust in Artificial Intelligence (AI). In this paper, we revisit the agent-based supply chain and review the state of the art. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains; are filling in gaps, leaving the concept applicable to a wider range of functions. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation. Digital ledgers help securely transfer data between third parties, making agent-based information sharing possible, without the need to integrate Enterprise Resource Planning (ERP) systems. Learning functionality in agents enables agents to move beyond automation and towards autonomy. We note this convergence effect through conceptualising an agent-based supply chain framework, reviewing its components, and highlighting research challenges that need to be addressed in moving forward.
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![Michael Amukanga]()
Supply Chain Management (SCM) is a very important function in any company and it should aim at cost cutting and customer satisfaction. It gives the firm a competitive edge in the industry and this brings about an increased interest both from researchers and practitioners. SCM performance is influenced by a hybrid of factors. The main purpose of the study was to assess whether top management support has influence on SCM performance among sugar companies in Kakamega County, a case of Mumias Sugar Company (MSC). The study used descriptive research design where 72 questionnaires and 23 interviews schedule were used to collect data from the staff and top managers. Purposive sampling and simple random sample was used to identify the respondents for the study. The data collected was analyzed using statistical package for social science version 22. The descriptive analysis shows that 56 (88%) of the respondents agreed that Supply chain management function is recognized and supported by top management within the company while 57 (89%) of the respondents agreed that Supply chain management is recognized in the company structure and given the right team of staff by the top management. The regression analysis was performed at 5% level of significance where the null hypothesis that there is no influence of top management support on SCM performance and was rejected and the study concluded that top management support (t = 3.319, p > 0.002) influenced SCM performance.
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![Michael Amukanga]()
- Willis Otuya
Supply Chain Management (SCM) is a very important function in any company and it should aim at cutting or reduction cost and customer satisfaction which translates to performance. This management function within an organization plays a key role in the performance of any company and no single firm can run away from this. Organizations have realized that competitive edge within and without the industry are achieved through performing supply chains. In Kenya, over 70% of public sector organizations have reduced costs and meet customers' expectations through a vibrant Information Communication Technology in SCM. E-procurement reduces paper work and increase productivity especially when firms adopt Information Communication Technology (ICT) in the physical flow of material products. SCM performance may be influenced by a hybrid of factors however Information Communication Technology is playing a key role in its performance and competitiveness. We therefore carry out document review to gain in-depth analysis and understanding on how information communication technology impacts supply chain management performance.
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![Hassan Lashkari]()
- Hiva Selki
- Fatemeh Tahaee
The city of Saqez has a special climate due to its location in a particular geographical location, topographical conditions and atmospheric systems affecting the region. As severe colds cause many problems for the residents of the city, therefore, the study of climatic conditions related to the design of the building is an attempt to alleviate the problems associated with the use of synoptic meteorological data of the city's climate. Turpentine was investigated and the following results were obtained: According to the building thermal requirement index, 43.7% of the time of year we completely need mechanical heating and only 20.8% of the year, it is possible to use sunlight. About 10.4% of the time we have comfort in indoors and only 11.1% of the time of year is in complete comfort. In order to optimize the climate in the building using the formula of the cosine law of total sunshine during the hot and cold seasons of the year, it was found that in Saqqez, the optimal orientation of the building to the southeast with elongation in the east-west direction was calculated. Be it. To this end, the question arises: Is the construction of the city of Saqqez in line with the climatic conditions of the region? )
Supply chain systems are network systems with several subchains each of which consists of facilities and distribution entities (suppliers, manufacturers, distributors, and retailers), and thus such systems can be viewed as multiagent systems. Consensus or coordination of multiple subchains is crucial for a stable market supply, especially in the case of some subchains suffering from production interruption, or losing connection with others, which become isolated subchains in some time period due to irresistible reasons. Once some unspecified isolated subchains appear, the topology structure of the system will be changed, and thus the multiagent-based supply chain system can be modeled as a switched system. This paper aims to investigate the problem of ${H_\infty }$ consensus for multiagent-based supply chain systems under switching topology and uncertain demands. To achieve the consensus of the system, switching controller is designed in which both production rate and distributed consensus protocol are considered. Sufficient conditions are given such that the whole system reaches consensus and desirable attenuation of bullwhip effect with an average dwell time approach, which allows some subchains to be isolated in some time periods. Finally, a simulation example is presented to illustrate the effectiveness of the proposed method.
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![Mark Stephen Fox]()
This thesis investigates the problem of constraint-directed reasoning in the job-shop scheduling domain. The job-shop scheduling problem is defined as: selecting a sequence of operations whose execution results in the completion of an order, and assigning times (i.e., start and end times) and resources to each operation. The number of possible schedules grows exponentially with the number of orders, alternative production plans, substitutable resources, and possible times to assign resources and perform operations. The acceptability of a particular schedule depends not only on the availability of alternatives, but on other knowledge such as organizational goals, physical limitations of resources, causal restrictions amongst resources and operations, availability of resources, and preferences amongst alternatives. By viewing the scheduling problem from a constraint-directed search perspective, much of this knowledge can be viewed as constraints on the schedule generation and selection process. In this thesis, we present a system called ISIS. ISIS uses a constraint-directed search paradigm to solve the scheduling problem. ISIS provides: a knowledge representation language (SRL) for modeling organizations and their constraints; hierarchical, constraint-directed scheduling of orders, which includes: constraint-directed bounding of the solution space; context-sensitive selection of constraints, and weighted interpretation of constraints; analytic and generative constraint relaxation; and techniques for the diagnosis of poor schedules.
Representation of activity knowledge is important to any application which must reason about activities such as new product management, factory scheduling, robot control, vehicle control, software engineering, and air traffic control. This paper provides an integration of the underlying theories needed for modeling activities. Using the domain of large computer design projects as an example, the semantics of activity modeling is described. While the past research in knowledge representation has discovered most of the underlying concepts, our attempt is toward their integration. This includes the epistemological concepts for erecting the required knowledge structure; the concepts of activity, state, goal, and manifestation for the adequate description of the plan and the progress; and the concepts of time and causality to infer the progression among the activities. We also address the issues which arise due to the integration of aggregation, time, and causality among activities and states.
- Richard Panse
CIM-OSA is a strategic architecture supporting all phases of a CIM system life cycle from requirements definition, through design specification, implementation description and execution of the daily enterprise operation. Standardised Modelling constructs enable generation of particular enterprise models for analysis, improvement and simulation of the daily enterprise operation. Standardised Information Technology services and protocols within the CIM system environment enable execution of the daily enterprise operations under control of the above derived models. A brief discussion of the necessary international standards activities provides an outlook of the future work.
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![Daniel G. Bobrow]()
- Gary G. Hendrix
- William A. Martin
- N. S. Sridharan
This paper is a condensed version of the author's thesis [Bolles 1976], which investigates a subclass of visual information processing referred to as verification vision (abbreviated VV). VV uses a model of a scene to locate objects of interest in a ...
- Ronald J. Brachman
This report presents on associative network formalism for representing conceptual knowledge. While many similar formalisms have been developed since the introduction of the semantic network in 1966, they have often suffered from inconsistent interpretation of their links, lack of appropriate structure in their nodes, and general expressive inadequacy. In this paper, we take a detailed look at the history of these semantic nets and begin to understand their inadequacies by examining closely what their representational pieces have been intended to model. Based on this analysis, a new type of network is presented - the Structured Inheritance Network (SI-NET) - designed to circumvent common expressive shortcomings.
- Chuck Rieger
- Milt Grinberg
This paper describes a theoretical framework and a LISP implementation for describing and simulating the cause-effect behavior of mechanisms. For the purposes of this research, a mechanism is defined to be any physical device, complex or simple, which exhibits cause and effect relationships useful to humans. Ordinarily, this will mean any purposively constructed object, such as a vacuum cleaner, a pencil, a button, a lightbulb or a computer. However, the authors also include in the definition any naturally-occurring physical devices and principles whose cause and effect relationships are of use to humans. Also considered are information-manipulating 'mechanisms' such as computer programs, as though they were physical in nature.
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Source: https://www.researchgate.net/publication/239033756_The_Integrated_Supply_Chain_Management_Project
