NISTIR 8108
 

Performance Challenges 

Identification Method for Smart 

Manufacturing Systems 

Kiwook Jung 

Katherine Morris 

Kevin W. Lyons

 Swee Leong

 Hyunbo Cho 

This publication is available free of charge from: 

http://dx.doi.org/10.6028/NIST.IR.8108 
 

 

NISTIR 8108 

Performance Challenges 

Identification Method for Smart 

Manufacturing Systems 

Kiwook Jung 

Katherine Morris 

Kevin W. Lyons 

Swee Leong 

Systems Integration Division 

Engineering Laboratory 

NIST 

Hyunbo Cho 

Department of Industrial and Management Engineering 

Pohang University of Science and Technology 

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.IR.8108

February 2016 

U.S. Department of Commerce 

Penny Pritzker, Secretary 

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
Abstract 

Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using 

detailed, precise, and appropriate data for intelligent decision-making. The intricacy of 

the relationship of strategic goals to operational performance across the many levels of a 

manufacturing system inhibits the realization of SMS. This paper proposes a method for 

identifying what aspects of a manufacturing system should be addressed to respond to 

changing strategic goals. The method uses standard modeling techniques in specifying a 

manufacturing system and the relationship between strategic goals and operational 

performance metrics. Two existing reference models related to manufacturing operations 

are represented formally and harmonized to support the proposed method. The method is 

illustrated for a single scenario using agility as a strategic goal. By replicating the 

proposed method for other strategic goals and with multiple scenarios, a comprehensive 

set of performance challenges can be identified. 

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Table of Contents 

1.	 Introduction.................................................................................................................. 1
 

2.	 Foundations.................................................................................................................. 3
 

Harmonization of SCOR and SIMA via ontology...................................................... 3
 

Representation of activities via IDEF0 ....................................................................... 6
 

3.	 SMS challenges identification method ........................................................................ 8
 

Scope determination.................................................................................................. 10
 

Current manufacturing system representation .......................................................... 12
 

Enhanced manufacturing system .............................................................................. 13
 

Planned manufacturing system representation.......................................................... 16
 

Gap analysis .............................................................................................................. 16
 

4.	 Discussion.................................................................................................................. 18
 

Reference models validity......................................................................................... 19
 

Method validity......................................................................................................... 20
 

5.	 Conclusion and future work....................................................................................... 20
 

6.	 Acknowledgement ..................................................................................................... 20
 

7.	 Disclaimer.................................................................................................................. 21
 

8.	 References.................................................................................................................. 21
 

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1. Introduction 

Smart Manufacturing Systems (SMS) are defined by the advent of new technologies that 

promote rapid and widespread information flow within the systems and surrounding its 

control [37, 43]. Along with these technologies, however, comes a greater need to be 

able to respond to information quickly [8] and effectively, thereby disrupting ongoing 

processes. SMS need to be agile to adapt to these challenges by using real-time data for 

intelligent decision-making, as well as predicting and preventing failures proactively [25, 

b]. To support this agility SMS need to meet rigorous performance requirements where 

performance measures accurately and effectively establish targets, assure conformance to 

these targets, and flag performance issues as evidenced by deviations from performance 

expectations [6]. By putting in place a continuous performance assurance process, 

companies can ensure products are manufactured through verifiable manufacturing 

processes. 

Both new and longstanding challenges at all levels of a manufacturing system inhibit the 

realization of SMS. The intricacy of describing these challenges stems from the grand 

complexity of manufacturing systems. This paper proposes a method for identifying 

challenges by focusing on a particular aspect of a manufacturing system. The proposed 

method integrates two existing models related to manufacturing operations: 

 The Supply Chain Operations Reference (SCOR) from the Supply Chain Council (SCC) 

[45], and 

 The manufacturing activity models from the Systems Integration for Manufacturing 

Applications (SIMA) Reference Architecture [4] 

The goal of the SCC is to identify and promote best practices in the management and 

operation of supply chain activities across many industries. The SCOR reference model 

provides a standard language for characterizing individual supply-chain activities. The 

SCOR model defines a system for organizing performance metrics and for associating 

those metrics with strategic goals and business processes. The SIMA Reference 

Architecture defines a set of activities describing the engineering and operational aspects 

of manufacturing a product from conception through production. For this research we 

have identified where the two models overlap when the business processes from SCOR 

directly correspond with the more technical, detailed and operational SIMA activities. 

Our intent in harmonizing these two models is to illustrate how performance metrics from 

the business-focused SCOR model can be identified in the operational activities of the 

SIMA model. We base this mapping on the use of formal representation methods for 

defining both models. The SIMA model uses a formal activity modeling technique 

known as IDEF0. To formalize the SCOR model which is presented in plain English, we 

use the Web Ontology Language [35]. For IDEF0 which is a formal diagramming 

technique, we develop an ontology that facilitates the mapping between the different 

viewpoints of the two models. 

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Figure 1 depicts how performance metrics are identified in the SCOR context. In this 

example, the agility goal is selected from the SCOR model. The agility goal is defined as 

the percentage of orders which are perfectly fulfilled when a disturbance is introduced 

into the manufacturing system. The disturbance in this case is a sudden increase in 

customer demand [34]. 

(a) Current manufacturing system (b) Planned manufacturing system 

Figure 1. Illustrative manufacturing system performance 

The agility goal is shown to be a function of time to recovery and residual performance. 

Agility enables the manufacturing system to shorten the time to recovery while also 

maintaining a high level of residual performance during the disturbance. Parts (a) and (b) 

in the figure illustrate a measurable improvement in agility between an existing system 

and a planned system. The challenge to improving agility is then reduced to challenges 

in improving these two performance metrics. While the goal of agility is not measured 

directly, performance metrics which are measurable are used to measure the capability of 

the manufacturing system to achieve the goal [45]. In this paper, we explain how this 

method can be consistently implemented for various goals and performance metrics using 

the formal representation methods for the two foundation models. 

The remainder of this paper is organized as follows. “Foundations” reviews the 

foundations to the proposed challenges identification method. We describe the challenges 

identification method in “SMS challenges identification method,” illustrate it with an 

example, and show how it can be used to identify challenges for performance assurance. 

“Discussion” provides discussion on the proposed method in the context of continuous 

improvement. Finally, we present our conclusion and discuss future work. 

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2. Foundations 

In this section, we review the use of the two formal representation methods used in the 

proposed challenges identification method: the Web Ontology Language (OWL) [35] and 

IDEF0 [19] models. 

Harmonization of SCOR and SIMA via ontology 

We develop an ontology to represent the SCOR model and the SIMA activity model. 

Originally, the SCOR model is presented in plain English, whereas the SIMA activity 

model is represented in IDEF0.OWL is a knowledge representation language for 

authoring ontologies. It is based on description logic which is a subset of first order logic. 

Gruber defines an ontology as the specification of conceptualization in formal description 

[15]. An ontology is a set of shared definitions of classes, properties and rules describing 

the way those classes and properties are employed. 

In this paper, we use the following notations for ontological constructs: classes, which 

represent the concepts being captured in Bold, the properties, which describe the 

concepts are in Italics with leading character in lowercase (groups); and individuals— 

instances of the concepts reflecting the real world example are in Italics with leading 

character in uppercase (Upside_Make_Flexibility). 

There are three main benefits of encoding the reference models in OWL: 1) structural 

support for harmonization of existing information 2) querying capability and 3) reasoning 

capability. Structural support for harmonization of existing information is not discussed 

in detail for this paper but the capability of the resulting ontology acquired from the 

harmonization is discussed in the context of building the classification for manufacturing 

operations from SCOR’s process model and SIMA’s activity model. Querying capability 

is illustrated in this paper in “Scope determination.” It is used to help scope the analysis. 

Lastly, reasoning capability is briefly highlighted below. 

The SCOR model is published as a nearly 1 000 page long document. The publisher, 

APICS (American Production and Inventory Control Society), recommends two days of 

intensive training to learn the structure, interpretation and use of SCOR framework 

elements. Representing this information using OWL provides improved accessibility to 

users, tools and knowledge engineers [3]. 

We use OWL to formally represent the major concepts and relationships described in 

SCOR. SCOR lends itself to representation in OWL in that it contains a rich network of 

hierarchical definitions which are interconnected with each other. Each of the abstract 

concepts is hierarchically decomposed in the SCOR model, and different elements across 

the decompositions are associated to each other. For example, SCOR contains a model of 

the business activities associated with all phases of satisfying a customer’s demand. The 

model consists of the four major components: performance, processes, practices and 

people. The performance component consists of performance attributes and performance 

metrics. A performance attribute is a grouping or categorization of performance metrics 

to express a strategic goal. Table 1 provides the complete list of performance attributes 

in SCOR. 

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Table 1. Performance attributes in SCOR [45] 

Performance 

Attribute 

Definition 

Reliability The ability to perform tasks as expected. Reliability focuses on the 

predictability of the outcome of a process. 

Responsiveness The speed at which tasks are performed. The speed at which a supply 

chain provides products to the customer. 

Agility The ability to respond to external influences, the ability to respond to 

marketplace changes to gain or maintain competitive advantage. 

Costs The cost of operating the supply chain processes. This includes labor 

costs, material costs, management and transportation costs. 

Asset Management 

Efficiency (Assets) 

The ability to efficiently utilize assets. Asset management strategies in a 

supply chain include inventory reduction and in-sourcing vs. 

outsourcing. 

Figure 2. High level view of the harmonization ontology 

These performance attributes are used to express the strategy for a manufacturing system. 

A Strategic goal (SG) is expressed by weighted Performance attributes (PA). 

This can be interpreted as a multiple criteria decision-making (MCDM) problem in itself 

[16, 23]. Most MCDM problems consider the use of criteria to assess effectiveness of a 

selection against a defined problem. Criteria can be used to structure complex problems 

well by considering the multiple criteria individually and simultaneously. 

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Figure 2 illustrates the high level view of the ontology we developed to harmonize the 

SCOR and the SIMA model. The SIMA activities are represented using the Activity class 

in the ontology. The ontological constructs enable the mapping between strategic 

objectives and operational activities. For the purpose of identifying challenges to SMS, 

only select components of the reference models depicted in Figure 2 are used in the 

examples. In addition, the illustrated example provided in this paper only considers one 

performance attribute--agility. 

Note that what is referred to as activities in SIMA are very similar to the processes in the 

SCOR model. Process and Activity are related using the aggregates-to/decomposes-to 

object property in Figure 2. In this paper, the details of the harmonization of the two 

models are not discussed but rather the method. In brief, all the activities in the SIMA 

activity model are encoded as individuals of Activity, which is a subclass of 

Manufacturing operation. An activity’s name is represented as an individual’s label. 

Parent and child activities are related using the object property aggregatesto/decomposes-to. 

Figure 2 also provides representation of how a Manufacturing operation is defined in 

the harmonization ontology. Inputs, controls, outputs and mechanisms of an activity in 

the SIMA model are classified into inputs of a manufacturing operation. Activity from 

the SIMA model and Process from the SCOR model are both subclass of 

Manufacturing operation and, therefore, inherit the same properties. Also, Activity and 

Processes are related using aggregates-to/decomposes-to, which is the same object 

property used for parent/child relationships in the SIMA activity model and for capturing 

the interrelations in the SCOR’s process hierarchy model. The ontology facilitates this 

semantic mapping and enables the proposed performance challenges identification 

method to focus on very specific activities. 

Table 2. An example of reasoning provided by the ontology 

Aim Infer that a performance metric that diagnoses other performance metrics is a 

leading performance metric 

Classes Performance metric, Leading metric 

Properties diagnoses (Domain: Performance metric, Range: Performance metric) 

Restriction On Leading metric: diagnoses min 1 

(a performance metric must be a Leading Metric if it is related with at least 1 

performance metric) 

Input An individual of Level-3 Metric (Current Purchase Order Cycle Times) is not 

diagnosed by any other Performance metric 

Output Current Purchase Order Cycle Times is classified as an individual of the class 

Leading Metric in addition to explicitly stated Level-3 Metric 

Finding the correct performance metrics has always been a difficult task. It is important 

to measure both the bottom-line results of manufacturing processes, as well as how well 

the manufacturing system will perform in future. For this reason, companies often use a 

combination of lagging and leading metrics of performance. A lagging metric, also 

known as a lagging indicator, measures a company’s performance in the form of past 

statistics. It’s the bottom-line numbers that are used to evaluate the overall performance. 

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The major drawback to only using lagging metrics is that they do not tell how the 

company will perform in the future. A leading metric, also known as a leading indicator, 

is focused on future performance. For simplicity, we qualify leading metrics as metrics 

that diagnose at least one other metric. A performance metric can be lagging and/or 

leading depending on the circumstances. Table 2 explains the rules embedded in the 

ontology to infer and classify performance metrics into lagging and/or leading. Figure 3 

shows the implementation in Protégé 4.3 [44]. The query is written based on the 

Manchester OWL syntax [54]. One can only execute a query on an ontology after a 

reasoner (a.k.a. classifiers) is selected. In the following example, we used the Pellet 

reasoner [42]. 

(a) Before reasoning (Leading_Metric) (b) After reasoning (Leading_Metric) 

Figure 3. The difference in classification of individuals’ membership before and after the 

inference 

Figure 3b shows that Leading_Metric has new individuals after reasoning. The aim 

described in Table 2 is fulfilled by reasoning. This type of inference is not limited to 

classifying leading and lagging metrics. For example, individuals of 

Performance_Metric can be classified into a new class called KPI (Key Performance 

Indicator), when we have a logical and agreed upon definition for the concept KPI and 

the ontology captures properties that distinguish KPIs from other performance metrics. 

These illustrated and potential classifications highlight the reasoning capability using 

OWL to represent the SCOR and the SIMA model for the purpose of finding the correct 

performance metrics. 

Representation of activities via IDEF0 

The IDEF0 definition of a function is ‘‘a set of activities that takes certain inputs and, by 

means of some mechanism, and subject to certain controls, transforms the inputs into 

outputs.” IDEF0 models consist of a hierarchy of interlinked activities in box diagrams 

with defined terms. Arrows attached to the boxes indicate the interfaces between 

activities. The interfaces can be one of four types: input, control, output or mechanism. 

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An IDEF0 model represents the entire system as a single activity at the highest level. This 

activity diagram is broken down into more detailed diagrams until the necessary detail is 

presented for the specified purpose. Figure 4 shows the basic IDEF0 representation with 

its primary interfaces. The decomposition of activities is represented using a numbering 

scheme. Numbers appear in the lower right corner of the box. Decomposed activities are 

always prefaced with the number of their parent activity. 

Figure 4. Basic IDEF0 representation of an activity and its related information overlaid 

on SIMA A0 

(a) Current activity from SIMA’s A413 (b) Planned activity 

A413 Create Production Orders 

Tooling designs Master Production Schedule Tooling/materials orders

Production Orders

Tooling list

Final Bill of Materials

Planning Policies

A413 Create Production 

Orders (modified)

Tooling designs

Bill of materials

CAD documents

Master Production Schedule Tooling/materials orders

Production Orders

Tooling list

Final Bill of Materials

Planning Policies

Web registry of suppliers

Figure 5. Activity of interest 

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Using SIMA to identify challenges, the IDEF0 function represents an activity of interest 

in the proposed challenges identification method. The activity of interest is subject to 

modifications to meet the strategic goal. Figure 5a depicts the activity A413 Create 

Production Orders, one of the lower level activities from the SIMA model. The arrows 

entering the activity from the left represent processing inputs to the activity, in this case 

the Master Production Schedule. The arrows coming in from above represent controls 

that guide the activity. For example, Planning Policies for a given organization will 

guide the creation of production orders. Arrows on the right are outputs from the 

activity, in this case tooling, material, and production orders. Finally, arrows coming in 

from the bottom represent mechanisms on the activity. 

Figure 6 depicts the next level of break down for this activity as is indicated by the 

numbers labeled on each box which all begin with A413. Figure 5b and 6 depict the 

modified version of the original A413 activity, and are discussed in depth in “Planned 

manufacturing system representation.” 

A4131 Retrieve capable suppliers

A4132

Predict purchasing cost

List of capable suppliers Bill of materials

CAD documents

Master Production Schedule

Web 

registry of 

suppliers

A4133 Simulate in-house 

manufacturing cost

A4134 Determine an 

optimal ratio

Purchasing alternatives Production 

alternatives

A4135 Plan orders

Optimal 

ratio

Planning policy

Production 

orders

Tooling/materi

als orders Tooling designs

Final Bill of Materials

Tooling list

Figure 6. Planned activity model decomposed 

3. SMS challenges identification method 

One of the drivers for smart manufacturing is the need to respond to changes in demand 

more quickly and efficiently [10, 52]. For example, we consider how a manufacturing 

operation might respond to an order that they are not able to fulfill in its entirety in-house 

in the time frame needed. In this scenario, we postulate that the manufacturer could fill 

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the order by outsourcing a portion of the production needs through the use of smart 

manufacturing technologies, which would enable them to identify suitable and capable 

partners. The understanding of how to implement such a scenario down to the operational 

level is one of the grand challenges in modeling of complex manufacturing systems [13] 

and is the objective of our challenge identification method. An order of scope reduction 

is needed for any requirements analysis to be meaningful and practical. Using the formal 

methods described we are able to precisely delineate scope. This helps to relate high-level 

strategic goals and requirements to low-level operational activities and provides the 

means to understand and represent interrelationships among the different elements of a 

manufacturing system. Further, the method supports effective communication across a 

manufacturing organization. 

Table 3. The proposed challenges identification method 

Task 1 

Determine scope 

Explanation Identifies manufacturing operations and 

performance metrics relevant to the scope 

of the challenges 

identification 

Input A strategic goal of a manufacturing system 

(Figure 7a) in query 

analysis Output A set of manufacturing operations and 

performance metrics relevant to the specified 

strategic goal (Figure 7a,b) 

Task 2 

Represent current 

Explanation Represent the identified manufacturing operation 

formally 

manufacturing 

system 

Input An identified manufacturing operation (Figure 

7b) 

Output A set of activities from the current manufacturing 

system (Figure 5a) 

Task 3 

Represent planned 

manufacturing 

Explanation Define the modifications to the current 

manufacturing system to improve the identified 

performance metrics 

system Input An identified activity and a set of performance 

metrics relevant to the specified strategic goal 

(Figure 5a) 

Output An improved activity from the planned 

manufacturing system (Figure 5b and 6) 

Task 4 

Gap analysis 

Explanation Compare the activity models of the current and 

the planned manufacturing system to highlight 

implementation barriers 

Input An activity from the current manufacturing 

system and the corresponding improved activity 

from the planned manufacturing system (Figure 

5b and 6) 

Output An analysis of implementation barriers for current 

manufacturing system (Table 8, 9) 

*Note that Activities are subset of Manufacturing Operations 

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Table 3 shows the proposed challenges identification method that integrates SCOR, SIMA 

Reference Architecture, and scenario-based validation. In Table 3, we provide references 

within parentheses to illustrated examples in this paper. 

To determine a scope of analysis, we use the SCOR mappings between performance 

goals and performance metrics of a manufacturing system. Further, SCOR links the 

performance metrics to business processes, which can be aligned to activities in a 

manufacturing system. These mappings determine the scope by identifying the relevant 

activities. The activities are drawn from the SIMA models, which we used to represent 

the current manufacturing system. We then create a planned manufacturing system 

activity model to identify modified capabilities. The planned activities reflect the 

enhanced capabilities envisioned for smart manufacturing and are then validated through 

a realistic scenario. Through a realistic scenario, a gap analysis between the activity 

model of the current and that of the planned system identifies challenges in the specific 

terms associated with the activity models. Table 3 summarizes these steps and they are 

illustrated below in the context of an example based on the Create Production Order 

activity. 

Scope determination 

This section highlights the query capability of the ontology as a key enabler to the 

proposed method. To evaluate performance with respect to SMS goals, we identify 

specific manufacturing operations that contribute to a goal and subsequently the activities 

which support those manufacturing operations. The SMS concept has several goals 

including agility, productivity, sustainability and others [17, 38]. In this paper, agility is 

selected to test the proposed challenges identification method. 

The result of the following series of queries and mappings defines the scope for our 

analysis. Figure 7a shows the results of querying the ontology to find leading metrics to 

the agility goal. Query #1 “What are the leading performance metrics to be monitored for 

agility?” is written in DL Query [54] as follows: Leading_Metric and (grouped-by value 

Agility). Performance metrics are organized hierarchically. One can drill down into lower 

levels of the hierarchy for one of the agility performance metrics, 

Upside_Make_Flexibility, to find the lower level metrics associated with the agility goal 

and to find processes associated with those metrics. Current_Make_Volume is one of the 

lower level metrics one can choose to investigate. If one chooses to investigate a 

performance metric at high level, the subsequent analysis and the identified challenges 

will likewise be at high level. Query #2 “What are the low-level manufacturing 

operations associated with agility goal?” is written in DL Query as follows: 

Manufacturing_operation and (contributes-to value Agility). The query results are 

partially shown in Table 4. Figure 7 shows the implementation of the queries. We 

identify generic processes that are important to agility: Engineer-to-Order, Make-toOrder and Make-to-Stock. These identified processes can be drilled down into the activity 

Create Production Orders. One of the explanations for this mapping is shown in Figure 

8. Create Production Orders is related to Engineer-to-Order with an object property 

aggregates-to (line 3). Engineer-to-Order is linked-to Upside_Make_Adapatability which 

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is grouped-by Agility (line 8, 9). A new property between Create Production Orders and 

Agility is inferred based on line 5, which chains several object properties into one object 

property. 

Table 4. DL query condition and query results 

Query Additional source volumes obtained in 30days, Customer return order 

result #1 cycle time reestablished and sustained in 30days, Upside Deliver Return 

Adaptability, Upside Source Flexibility, Downside Source Adaptability 

Upside Deliver Adaptability, Current Deliver Return volume, Percent of 

labor used in logistics not used in direct activity, Current Make Volume 

Supplier’s/Customer’s/Product’s Risk Rating, Upside Deliver Flexibility, 

Value at Risk Make, Upside Source Return Flexibility, Value at Risk Plan 

Current Purchase Order Cycle Times, Value at Risk Deliver, Demand 

sourcing supplier constraints, Upside Make Adaptability, Upside Source 

Return Adaptability, Downside Make Adaptability, Value at Risk Source, 

Upside at Risk Return, Additional Delivery Volume, Current source return 

volume, Percent of labor used in manufacturing not used in direct activity 

Query 

result #2 

SCOR Process: Receive Product, Mitigate Risk, Schedule Product 

Deliveries, Checkout, Route Shipments, Route Shipments, Process Inquiry 

and Quote, Stage Finished Product, Package, Authorize Defective Product 

Return, Receive product at Store, Receive Defective Product includes verify, 

Ship Product, Schedule Defective Product Shipment, Release Finished 

Product to Deliver, Identify Sources of Supply, Issue Sourced/In-Process 

Product, Enter Order commit Resources and Launch Program, Schedule 

Installation, Waste Disposal, Build Loads, Invoice, Request Defective 

Product Return Authorization, Verify Product, Receive and verify Product 

by Customer, Schedule MRO Return Receipt, Issue Material, Receive Excess 

product, Load Product and Generate Shipping Docs, Finalize Production 

Engineering, Schedule Excess Return Receipt, Receive MRO Product, 

Quantify Risks, Identify Risk Events, Return Defective Product, Deliver 

and/or Install, Pack Product, Transfer Excess Product, Stage Product, 

Transfer MRO Product, Transfer Defective Product, Authorize MRO 

Product Return, Receive Configure Enter and Validate Order, Generate 

Stocking Schedule, Evaluate Risks, Identify Defective Product Condition, 

Produce and Test, Pick Product, Obtain and Respond to RFP/RFQ, 

Negotiate and Receive Contract, Stock Shelf, Transfer Product, Authorize 

Supplier Payment, Disposition Defective Product, Schedule Production 

Activities, Establish Context, Fill Shopping Cart, Authorize Excess Product 

return, Receive Enter and Validate Order, Consolidate Orders, Select Final 

Supplier and Negotiate, Release Product to Deliver, Reserve Inventory and 

Determine Delivery Date, Select Carriers and Rate Shipments, Receive 

Product from Source or make, Load Vehicle and Generate Shipping 

Documents, Pick Product from backroom, Install Product 

SIMA Activity: Create Production Orders (illustrative) 

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(a) A DL query for retrieving leading metrics (b) A DL query for retrieving low-level 

manufacturing operation 

Figure 7. DL query and query results on Protégé 4.3 (illustrative) 

Figure 8. An inference explanation for the mapping between SIMA activity and SCOR 

process 

The identified operational activities are subject to redesigning for improvement. By 

redesigning the identified operational activities, the manufacturing system is assumed to 

be more capable of satisfying strategic objectives [9]. The redesign of the activities 

incorporates new and emerging capabilities that are the foundation of Smart 

Manufacturing. New capabilities from machine sensors to internet-enabled supply chains 

are emerging every day and can improve manufacturing operations. We provide a 

demonstration of this redesigning for improvement with the following example in the 

sections below-- “Current manufacturing system representation” and “Planned 

manufacturing system representation.” 

Current manufacturing system representation 

A manufacturing system is defined as the configuration and operation of its subelements 

such as machines, tools, material, people and information to produce a value-added 

physical, informational or service product [9]. The SIMA architecture represents the 

current manufacturing system. While this model does not represent any specific 

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manufacturing system, it is representative of the state of the practice. We use it as a 

baseline from which we can illustrate how new technologies will impact manufacturing 

practices. The new practices are described in the planned manufacturing system in the 

following section. As an example, Figure 5a shows the original Create Production Orders 

activity from the SIMA model and the planned activity model. Additional elements are 

highlighted in Figure 5b to show the difference. Table 5 defines four of the ICOMs from 

the figure that are discussed further in our example. 

Table 5. ICOM definitions for the current manufacturing system 

Element Definition Category 

Master 

Production 

Schedule 

A list of end products to be manufactured in each of the next N 

time periods. The list specifies product IDs, quantities, and due 

dates. 

Input 

Planning 

Policies 

The business rules by which the manufacturing organization does 

production planning including product prioritization, facility 

usage rules, make-to-inventory/make-to-order and selection of 

planning strategies. 

Control 

Tooling list The complete tooling list for some batch of the part in exploded 

form, including all tools, fixtures, sensors, gages, probes. The list 

identifies tool numbers, quantities, and sources. This list may 

include estimates for consumption of shop materials. 

Control 

Final Bill of 

Materials 

The complete Bill of Materials (BOM) for the part/product in 

exploded form, with quantities of all materials needed for some 

batch size of the Part. This may include any special materials 

which will be consumed in the process of making the part batch, 

such as fasteners, spacers, adhesives; alternatively those may be 

considered “shop materials” and included in the tooling list. 

Control 

Enhanced manufacturing system 

To illustrate our approach consider the following scenario for a company that 

manufactures gears. The company receives a customer order change request for one of 

their specialized gears. The required delivery date for this order is reduced by two weeks 

from the original production schedule. The gears are produced by specialized processes 

of either powder metal extrusion or hot isostatic pressing (HIP) method. HIP is similar to 

the process used to produce powder metallurgy steels. Heat treating of gears is also a 

required process step. The manufacturing system is constrained by the capacity of the 

specialized processes and the heat-treating machine to satisfy this rush order request. 

With the current system, the company would risk losing the order because they would not 

be able to produce the product in the required time. In the envisioned system, however, 

the company would look for partners to help where their own capacity is limited. A webbased registry of suppliers is used to quickly find capable partners in this new 

environment [2]. The digital representation of precise engineering and manufacturing 

information is used to specify production requirements for new partners [31, 37]. 

These proposed enhancements to the system may very well make the company more 

competitive, but before attempting to introduce these changes the company must fully 

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understand the implications. The method that we propose allows a company to 

understand how the business processes will be impacted and what performance metrics 

will be needed for that assessment, as well as what new information flows will be needed. 

In terms of information flows there are several notable changes in the current system. 

For the planned system to identify capable suppliers a Request for Proposal (RFP) 

package is prepared and sent to a web-based supplier registry for quote. This package 

contains all the required product and process information necessary to respond to the 

RFP. Information includes, but is not limited to, CAD documents, bill of materials, 

quantity, due dates, product specifications, process technical data characteristics, and 

other information necessary to produce the part, assembly, or product. Other suppliers 

prerequisites’ to qualify to quote are supplier competency in the specialized processes, 

powder metal extrusion or hot isostatic pressing process, past quality performance 

history, capacity and sound financial standing. Qualified suppliers will be evaluated 

based on supply flexibility in make, delivery, delivery return, source, source return, and 

other qualifications. A web-based supplier registry contains a supplier-capability 

database. 

Upon receipt of the RFP at the supplier registry, the performance metrics for measuring 

supply flexibility in make, delivery, delivery return, source, and source return are 

retrieved. Other secondary performance metrics can be used as required. This includes 

mapping the supplier capabilities with the performance metrics, matching supplier 

capability with RFP’s evaluation criteria, and retrieving a list of capable suppliers that 

meet the performance evaluation criteria. Each supplier provides a price quotation to 

deliver the BOM’s order quantity at the requested due date. The remaining activities are 

simulate and predict the in-house manufacturing cost for the quantity specified in the 

MPS (Master Production Schedule), determine an optimal ratio between supplier’s 

purchasing and in-house production cost for each BOM, and finally plan and execute 

production orders. 

We have defined formal representations of performance metrics and performance goals 

for agility, their relationships and properties. The performance metrics are supply 

flexibility in make, delivery, delivery return, source, and source return. Based on the 

harmonization ontology concepts, definitions, relationships and properties, we 

implemented the mapping between performance goals and performance metrics. 

For each supplier, a predictive model of the planned system provides a purchasing cost 

for all variations in the ratio of in-house production to outsourced from one to the 

quantity specified in the MPS. The in-house manufacturing cost for the quantity 

specified in the MPS can be simulated using a cost table. For all pairs of outsourcing and 

in-house production costs, the minimum cost can be found. By exploding the BOM, 

individual items and consequent tooling and materials orders are identified. Then, the 

optimal ratio between in-house and outsourcing is determined. 

14
 
 

 

 


 

Table 6. Select elements in identified activity for a planned manufacturing system 

Element Current definition Planned definition 

Bill of The complete Bill of Materials for The BOM is used as an input to 

materials the part/product in exploded form, discover suppliers. The part 

(BOM) with quantities of all materials 

needed for some batch size of the 

Part. This may include any special 

materials which will be consumed 

in the process of making the part 

batch, such as fasteners, spacers, 

adhesives; alternatively those may 

be considered “shop materials” and 

included in the tooling list. 

number in the BOM is attached to 

supporting Computer-Aided Design 

(CAD) documents. 

CAD Not used in this activity STEP (Standard for the Exchange 

documents of Product model data) is used to 

express 3D objects for CAD and 

product manufacturing information 

[21]. This exchange technology 

enables the discovery of suppliers 

that can manufacture such parts. 

Alternatively, Web Computer 

Supported Cooperative Work 

(CSCW) to translate CAD and FEA 

(Finite Elements Analysis) data into 

VRML (Virtual Reality Markup 

Language) can provide an easy-toaccess to mechanical-design-andanalysis in a collaborative 

environment [11, 48, 55]. 

Web registry Does not exist This registry of suppliers stores 

of suppliers supplier’s information using MSC 

(manufacturing service capability) 

model. The MSC model enables 

semantically precise representation 

of information regarding production 

capabilities [11, 28, 29, 30, 49, 50] 

Planning The business rules by which the The planning policy in the planned 

policy manufacturing organization does 

production planning. This includes 

product prioritization, facility usage 

rules, make-to-inventory/make-toorder and selection of planning 

strategies, 

e.g., Just-In-Time, Critical 

Inventory Reserve. 

system may include a decisionmaking mechanism that determines 

an optimal ratio between purchasing 

and in-house production quantity. 

This extension allows the enterprise 

to not only meet the customer 

demands with flexible capacity but 

also in the most economical way. 

15
 

 

Planned manufacturing system representation 

The SIMA model describes manufacturing activities at a level of detail that does not 

prescribe how to achieve the activities. Thus, in our method the activities are further 

decomposed into specific tasks. This conceptual design through further decomposition is 

crucial to defining new creative manufacturing systems [32]. Figure 6 is a decomposition 

of the planned activity in Figure 5b with modifications that reflect how the activities are 

made more robust by the envisioned enhancements. The particular modification reflects 

the sourcing of capable suppliers more intelligently using the web-based registry as 

described above. To meet increased demand, production capacity is rapidly increased by 

identifying capable suppliers that meet the production requirements. A sample of 

enhanced capabilities is given in Table 6. Note that these enhanced capabilities are only 

for demonstration purposes and does not imply that these are the best for the purpose. 

Other of alternatives such as simulation-based integrated production planning approach 

[26] and SOA-based configurable production planning approach [27] are possible. 

In short, the enhanced capabilities of the planned manufacturing system can be 

summarized as follows. First, using product and process data, the system discovers and 

retrieves a list of candidate suppliers who can manufacture the required product. Second, 

the system is able to predict both the purchasing and in-house production cost given the 

MPS. Based on the predicted costs, an optimal ratio of in-house production versus 

purchasing is determined. Finally, using the optimal ratio between in-house and 

purchasing, the system generates production, tooling, and materials’ orders. Note that the 

activity A4131 Retrieve capable suppliers would be further decomposed to describe those 

details. 

Gap analysis 

Challenges to assuring the performance of an enhanced system fall into two categories: 

technology and performance measures. Once an enhanced system is planned, suitable 

technology can be sought to satisfy the new system. Table 7 illustrates some of the 

technology challenges for our example. 

Table 7. Identified technology challenges 

Activity Challenges Reference 

Retrieve capable suppliers Supplier capabilities are marked up using 

semantic manufacturing service model. 

Queries are generated automatically from 

product and process data 

[7, 24, 28, 

46] 

Predict purchasing cost 

Predict in-house 

manufacturing cost 

Part cost are predicted for new parts that 

have never been produced before 

[12, 14, 

33, 47, 

51] 

16
 

 

To ensure that the new system will actually improve performance, performance measures 

need to be identified. The application of performance assurance principles through-out 

all phases and levels of manufacturing help ensure that the manufacturing processes meet 

their intended functional requirements while providing necessary feedback for continuous 

improvement. Performance data must support the objectives of the manufacturer, from 

the highest organizational level cascading downward to the lowest appropriate levels. It is 

critical that these lower level measurements reflect the assigned work at their own level 

while contributing toward overall operational performance measurements for the 

enterprise. 

For example, two key measures of performance, manageable quantities and production 

cost (defined in detail in the SIMA documentation), are significantly impacted in the 

planned system and more data is needed to calculate these in the new system. In the 

enhanced system, the capacity that determines the manageable quantities becomes 

flexible by identifying capable suppliers via the web. Once the production orders become 

a combination of in-house and purchasing, a decision needs to be made on which orders 

will be sent out to bid. Secondly, determining the production cost is not a simple addition 

of costs between in-house and purchased parts. For example, quality may not be 

consistent with purchased parts. From the total cost point of view, this may result in more 

cost than expected due to inspection and customer claims. Thus, the concept of a cost is 

much more complex in the planned system. It is a comprehensive metric that is closely 

integrated with a predictive model to estimate the cost incurred in later stages of 

production and usage. The comparison of the activities relevant to the above ideas is 

summarized in Table 8 and potential enablers for the enhanced capabilities of the planned 

manufacturing system are listed in Table 9. 

Table 8. Manufacturing system design comparison 

Current activity design Limitation Planned activity design 

Create production orders 

for manageable quantities 

with specific due dates 

Production orders may not 

be able to produce 

quantities with specific due 

dates given the capacity of 

resources 

Rapidly identify capable 

suppliers on web who are 

capable of producing 

required products 

Determine which orders 

will be produced in-house 

(and in what facilities) and 

which will be sent out to 

bid. 

The determination of the 

ratio between in-house and 

outsourcing does not 

account for total cost of 

production including 

quality and inspection 

Determine an optimal ratio 

of which orders will be 

produced in-house and 

which will be sent out to 

bid based on the total cost 

of production 

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Table 9. Mapping between enhanced capabilities and potential enablers 

Enhanced capabilities of 

the planned 

manufacturing system 

Potential enablers Relevant current 

manufacturing system 

elements 

Semantically rich 

production and process 

information can help to 

dynamically discover 

capable suppliers using the 

product information of the 

required production 

MIL-STD [31] 

ISO 10303 [21, 40] 

STEP-NC [22] 

MTConnect [36] 

Tooling list (Control) 

Final Bill of Materials 

(Control) 

Manufacturing cost for the 

new parts that have never 

been produced before are 

initially unknown but need 

to be approximated 

Predictive analysis models 

[41] 

Not used in this activity 

4. Discussion 

This section discusses the proposed method in the context of larger practice, the 

continuous improvement process. We acknowledge that the proposed method has 

limitations. Then, we lay out the plans for improving the proposed method. 

The proposed method is based on an ontology that explicitly represents the relationship 

between high-level strategic goals and requirements to low-level operational activities. 

This provides the means to understand the interrelationship among the elements of a 

manufacturing system at multiple levels. The method also provides a potential means to 

communicate across a manufacturing organization. More importantly, it clearly 

distinguishes between what (goals) and how (manufacturing system design). This 

powerful capability, however, has innate limitations in the design. In addition, there are 

areas in the proposed method that require further validation to assure performance of a 

manufacturing system. 

Figure 9 shows the identification of performance challenges in the context of a 

continuous improvement process [5]. The proposed method helps to specify what needs 

to be considered to meet a strategic goal. Performance metrics associated with a strategic 

goal and respective manufacturing operations at all levels of a manufacturing system are 

retrieved. A planned system is a configuration of a manufacturing system known and 

available. Users’ expertise sets the boundary for available configurations. The resulting 

configuration is expected to meet the strategic goal. Therefore, planned manufacturing 

systems are subject to expertise of the users, which is unaccounted for in the scope of the 

method. Figure 9 also has the shading that the proposed method addresses. 

18
 
 

 


 

Specify what needs to be considered to meet the strategic goal Users’ expertise (known/available configurations of manufacturing system) A user needs to improve manufacturing system to meet a strategic goal

Is it the ideal configuration? Yes No Is there a configuration that meets the goal? (pre) Yes

Did it meet the strategic goal? (post) Identify implementation barriers Manufacturing system is improved and meets the strategic goal Yes No Create a 

configuration No

Figure 9. Performance challenges identification in the context of continuous 

improvement process 

Reference models validity 

SCOR and SIMA may not capture all possible strategic goals and manufacturing 

operations required for the performance challenges identification. In other words, agility 

in the SCOR model cannot be representative of all agility concepts used in practice. 

Table 10 shows similar but not identical definitions for agility from various sources. 

Table 10. Agility definitions 

Sources Definition 

Dictionary Definition for agile 

Marketed by ready ability to move with quick easy grace 

Having a quick resourceful and adaptable character [1]. 

SCOR 

model 

The ability to respond to external influences, the ability to respond to 

marketplace changes to gain or maintain competitive advantage [45]. 

IEC 62264­

1 

Agility in manufacturing is the ability to thrive in a manufacturing 

environment of continuous and often unanticipated change and to be fast 

to market with customized products. Agile manufacturing uses concepts 

geared toward making everything reconfigurable [20]. 

Wiendahl 

model 

Agility means the strategic ability of an entire company to open up new 

markets, to develop the required products and services, and to build up 

necessary manufacturing capacity [53]. 

Likewise, the manufacturing operations defined in the ontology do not account for all the 

manufacturing operations. The ontological structure provides means to harmonize 

reference models to better characterize such concepts with more detail. 

We chose the SIMA model to represent manufacturing operations but actual systems will 

vary. We also need to better understand the relationship among the low-level activities 

19
 
 

 

 


 

and performance metrics as well. They not only have impact on the high-level strategic 

goals, but they also interrelate with each other. For example, increasing the batch size 

influences the average work-in-progress; changing the supplier portfolio affects the 

quality of the product. Ultimately, designing a manufacturing system should account for 

the interrelationships between low-level activities and performance metrics as well as 

their relation to high-level strategic goals. 

Method validity 

The proposed method does not assure that the planned system actually meets the 

specified strategic goal. Or, the planned system may not be the ideal configuration for the 

given strategic goal. This validation and evaluation of a planned system corresponds to 

“Is it the ideal configuration?” “Identify implementation barriers,” “Did it meet the 

specified strategic goal?” in the Figure 9. Thus, it is logical to provide a means to further 

validate and evaluate the planned system. Various technologies can be used in this regard 

including physical testbed construction, simulation, mathematical formulation of the 

planned system and others. Physical testbeds enable validation of the planned system by 

collecting data from a shop floor for analytical use. This proposed method is only a 

starting point for system enhancement. 

5. Conclusion and future work 

Smart Manufacturing Systems (SMS) are characterized by their capability to make 

performance-driven decisions based on appropriate data; however, this capability requires 

a thorough understanding of particular requirements associated with performance across 

all levels of a manufacturing system. The proposed method uses standard techniques in 

representing operational activities and their relationship with strategic goals. This paper 

proposed a method to systematically identify operational activities given a strategic goal. 

It is an integrated approach that uses multiple reference models and formal 

representations to identify challenges for enhancing existing systems to take into account 

new technologies. A scenario that illustrates how a manufacturing operation might 

respond to an order that it is not able to fulfill in-house in its entirety in the time frame 

needed was presented. We demonstrated the proposed method with that scenario. By 

replicating the proposed method for other performance goals and with other scenarios, a 

more comprehensive set of challenges to SMS can be identified. 

Future work will 1) replicate the proposed method for other performance goals 2) 

validate the proposed method discussed in “Discussion” and 3) explore ways in which the 

identified challenges can be systematically addressed, thereby reducing the risk for a 

manufacturer to introduce new technologies. We plan to expand on the ontology as more 

examples are developed. The ontology will serve a fundamental role in managing the 

system complexity as more SMS technologies are introduced and will be described 

further in future work. 

6. Acknowledgement 

The authors are indebted to Dr. Moneer Helu for feedback, which helped to improve the 

paper. 

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7.	 Disclaimer 

Certain commercial products in this paper were used only for demonstration purposes. 

This use does not imply approval or endorsement by NIST, nor does it imply that these 

products are necessarily the best for the purpose. 

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