Digital Twinning of Supply Chains: An Orchestration Platform for … … · This orchestration from an as-is to a to-be faces immense data challenges as one moves through several

January | 2019

Digital Twinning of Supply Chains:

An Orchestration Platform for Supply

Chain (Re) Design

THINK Executive White Paper Series

A Collaboration Between

Disclaimer, Limitation of Liability and Terms of Use

NUS and contributors own the information contained in this report, we are licensed by the contributors to reproduce the information

or we are authorised to reproduce it.

Please note that you are not authorised to distribute, copy, reproduce or display this report, any other pages within this report or

any section thereof, in any form or manner, for commercial gain or otherwise, and you may only use the information for your own

internal purposes. You are forbidden from collecting information from this report and incorporating it into your own database,

products or documents. If you undertake any of these prohibited activities we put you on notice that you are breaching our and our

licensors' intellectual property rights in the report and we reserve the right to take action against you to uphold our rights, which may

involve pursuing injunctive proceedings.

The information contained in this report has been compiled from sources believed to be reliable but no warranty, expressed or

implied, is given that the information is complete or accurate nor that it is fit for a particular purpose. All such warranties are expressly

disclaimed and excluded.

To the full extent permissible by law, NUS shall have no liability for any damage or loss (including, without limitation, financial loss,

loss of profits, loss of business or any indirect or consequential loss), however it arises, resulting from the use of or inability to use

this report or any material appearing on it or from any action or decision taken or not taken as a result of using the report or any

such material.

Presented at:

THINK EXECUTIVE SUMMIT:

JOURNEY TO DIGITALIZATION

DIGITAL TWINNING AND OPTIMIZING OF SUPPLY CHAINS

16 January 2019

Singapore

Digital Twinning of Supply Chains: An Orchestration Platform for Supply Chain (Re) Design

1

EXECUTIVE SUMMARY

This whitepaper marks the inauguration of the Advanced Executive Program in Supply Chain Innovation that

focuses on transformative ideation of supply chains and thence its operationalization of a selected

design. Digitalization, ecommerce and industry 4.0 have disrupted current practices calling for a revision of

managed events, processes, policies and outcomes that may have once served the business well but are now

being challenged at the fundamentals.

The complexity inherent in any supply chain makes it difficult to experiment with probable changes

piecemeal in a reductionist mode or even for that matter holistically ignoring the finer details – a balance is

required in orchestrating transformation. A granular approach, appropriate to the challenges posed in

migrating from an as-is to to-be model is proposed where the coarseness of data utilized or gathered is

commensurate with the problem statement under study.

This orchestration from an as-is to a to-be faces immense data challenges as one moves through several

transition phases, each perhaps requiring different modelling methods and progressively finer data tuning.

The physical supply chain is not easily tweaked. Authors have variously coined the term digital twinning

where the essential characteristics of the supply chain are captured in a digital model. However, such a digital

twin varies with modelling method, albeit visualization, to analytical to optimization or simulation. The twin

itself may vary in complexity and data requirements. It is our belief that we orchestrate to and fro with tools

best suited to the task but have a progressively finer matched data set that can be utilized across modelling

methods. This paper discusses this in some detail supported by examples from the many cases that the

Institute has undertaken.

Creating out-of-the-box ideas requires a sandbox for safe experimentation within the digital twins of

transformative ideas. The initial tools in the sandbox have been carefully picked and open to enhancements

as better castles need to be built with bridges across moats. The tools are organized to deliver interim

milestone results and data collection itself is progressive and matched to granularity required in the

respective digital twin.

We hope that you enjoy reading this whitepaper and that it provides some mapping in your digital journey

and that you will contribute to the ensuing discussion at the THINK Executive events.


2


3

TABLE OF CONTENTS

CHAPTER 1.

CHALLENGES IN THE DIGITAL SUPPLY CHAIN 4

CHAPTER 2.

LEVERAGING DATA TO DRIVE SUPPLY CHAIN INNOVATION FOR SUPPLY CHAIN (RE) DESIGN 9

CHAPTER 3.

SUPPLY CHAIN ORCHESTRATION PLATFORM – CONCEPTUAL DESIGN

12

CHAPTER 4.

SUPPLY CHAIN ORCHESTRATION PLATFORM STEP-BY-STEP 18

CHAPTER 5.

KEY TAKEAWAYS AND FUTURE WORK 25


4

Chapter 1.

CHALLENGES IN THE DIGITAL SUPPLY CHAIN

In this technology dependent world, digitalization is disrupting the way businesses perform across all

industries. The mass adoption of digital technologies influences the operations of companies’ logistics and

supply chain management. Smart and interconnected technologies, such as the Global Positioning System

(GPS), Radio Frequency Identification (RFID), cloud computing, and sensor devices have changed how

businesses interact with their consumers. Customer-centric strategies, innovativeness, flexibility and

responsiveness with higher emphasis on fulfilling consumer expectations are the key drivers in this digital

era. On a personal note, consumers are moving from traditional catalogue sales, brick-and-mortar retailers

to online e-commerce, using their smartphones devices and cloud operation platforms at one’s convenience.

With that, more consumers are

embracing and accepting the digital

economy. Technological trends,

internet penetration (as illustrated in

Figure 1.1) and online channel

purchasing (as illustrated in Figure 1.2)

demands businesses to alter their

approach to consumer demands. On-

time, reliability, cost-effective

deliveries, speed and convenience are

now key factors that draw consumers

in perceiving equal importance to its

price and quality of the purchased

product. The role of logistics has to

match the growing volume of demand

for pick and pack services.

Traditional supply chains with linear and long chains (as illustrated in Figure 1.3) may not be sufficient in this

digital era. Businesses need to be proactive in recognizing the ever-changing trends of consumer demands

and likewise shift to a more connected supply network (as illustrated in Figure 1.4), via digitally

interconnected devices and complex webs to keep pace with digital transformations. Today’s digital supply

chain needs to have the capabilities for extensive information availability, superior collaboration and

communication across chains. A lack of visibility, sub-optimal performance, unresponsiveness to uncertainty

and poor flexibility are growth-impeding constraints.

KEY FACTORS FOR SUPPLY CHAIN AND LOGISTICS

EXCELLENCE IN DIGITAL SUPPLY CHAIN:

On-Time, Reliability, Cost-Effective deliveries, Faster and

Convenience Delivery Services for different level of customer’s

expectations.


5

Note: Size of bubble denotes 3Y-CAGR e-commerce growth1

while yellow circles denote market leaders and growth

markets.

Figure 1.1. Plot of number of non-internet users against internet penetration across country markets2

Figure 1.2. Online channel purchasing across global markets3

1 Euromonitor International. (2017). Retailing. Retrieved from http://www.euromonitor.com 2 The World Bank. (2017). Individuals using the Internet (% of population). Retrieved from https://data.worldbank.org/indicator/IT.NET.USER.ZS 3 Raw data is taken from Euromonitor (www.euromonitor.com). The data includes sales through websites from both pure players and store-based retailers, excludes C2C sales; sales of motor vehicles and parts; ticketing, travel and holiday packages; online gambling; click-and-collect where payment is made in store; quick delivery services of food, magazines, households goods.


6

Figure 1.3. Traditional supply chain

Figure 1.4. Digital supply chain

Figure 1.5 outlines the elements and expectations placed on its respective determinants involved in the

digital supply chain. These, coupled with the business problems of today's supply chain, gathered from

several industry players (as illustrated in Figure 1.6), if not handled properly will cause problems and issues

in the supply chains, ultimately leading to high operational costs, poor company margins, unacceptable

service levels, and low productivity.


7

Figure 1.5. Digital Disruption Forming the Digital Supply Chain

Figure 1.6. Pain points of Industry Players

To transfer their traditional supply chain to digital supply chain, businesses conforming to such practices need

to address questions as follows:

“How to (re)design the supply chain network to be more robust and reliable for handling high

delivery demand?”

“What tool should be used to (re)design the supply chain network for digital transformation?”

“What needs to be revamped to mitigate supply chain network risks and losses in the face of

stochasticity?”

“Can high level responsiveness be maintained with minimum investment and resources deployed in

the supply chain?”

“Can a more adaptive tool be developed to minimize the gap of actual demand and planned

supply?”

Customer Centric

•Shorter delivery lead-time

•Narrower and more specific delivery time-windows

•On-demand logistics (a variety of small quantities)

•Highly fragmented dynamic demand

•Real-time tracking

•Delivery customization

Connected-Network and Visibility

•Information sharing

•Visibility to important information for all stakeholders

•The unified views of important information

•Stakeholder collaboration

Responsiveness

•Fast response to changing demand

•Flexibility to change supply and demand across the supply chain

•Dynamic planning, routing, scheduling and pricing

Automation

•Auto-capturing data

•Real-time information update

•Autonomous decision making

Predictive Deliveries

•Adjusting shipment to avoid delivery delay

•Predicting service disruption across the supply chain

•Detecting what customers want most


8

Although research has mentioned that digital supply chain transformation is very important for businesses4,

many companies still struggle to make progress with a view to digital supply chain transformation. One of

the main reasons for this is that the current available supply chain and logistics tools/platforms are not able

to efficiently address and handle digital supply chain complexities. Therefore, creating a more adaptive and

orchestrated platform for assets, business processes, and complex operations has become the imperative.

The following chapters discuss the orchestration platform and how Big Data and Machine Learning

technologies are used to develop simulation models as “digital” twins of the physical (real) supply chain

networks. These simulation models can be used to (re) design the ideal supply chain network through

identifying and evaluating transformative strategies to the supply chain networks.

4 For example: GT Nexus. 2016. The Current and Future State of Digital Supply Chain Transformation. http://mktforms.gtnexus.com/rs/979-MCL-531/images/GTNexus-Digital-Transformation-Report-US-FINAL.pdf, accessed 24.08.2018

http://mktforms.gtnexus.com/rs/979-MCL-531/images/GTNexus-Digital-Transformation-Report-US-FINAL.pdf


9

Chapter 2.

LEVERAGING DATA TO DRIVE SUPPLY CHAIN INNOVATION

FOR SUPPLY CHAIN (RE) DESIGN

IOT and Big Data are at the heart of digital transformation. It produces enormous data and information that

can be in form of structured data such as delivery transactions and warehouse operational data, or

unstructured data from external resources and social media such as delivery feedbacks. If handled and

managed properly, this data can help generate smarter supply chain and logistics solutions and improve

decision making processes. Hence, many companies are rapidly evolving and investing large amounts of

funding and resources in trying to collect and transform data into competitive advantage. However, only

collecting (raw data) would not turn the data into business insights. Data processing and analytics, with

Artificial Intelligence (AI) and Data Mining (DM) technologies, are crucial.

As described in the previous chapter, one of the main problem faced by businesses today is “how to

(re)design the supply chain network to be more robust and reliable for handling high delivery demand”. Data

analytics and mining can be used to tackle this problem to generate transformative strategies to (re) design

the supply chain network. The (raw) data needs to be processed into the following steps as illustrated in

Figure 2.1.

Figure 2.1. Data Processes


10

Supply Chain Understanding and Requirement

This step involves understanding what supply chain aspects are to be improved or identification of the supply

chain problems to be addressed before re-designing the supply chain network. Bottlenecks and important

problems need to be clearly identified. To do that, relevant data such as the current supply chain network,

supply and demand flow, measurement and performance indicators, are needed. Benchmarking from other

supply chain networks in the same or different industries may also be beneficial to identify improvement

aspects. At this stage, identifying and defining the problems, relevant data associated with that problems as

well as exploring the data relationships to discover main insights are the main contributors.

Data Collection and Acquisition

After the problems in the supply chain and required data are identified, the next step is to gather these data.

This step focuses on data availability, accessibility and sufficiency. Relevant data is collected from different

sources, such as Enterprise Resource Planning (ERP) system, sensors, machine generated, social media and

external web services. It can be structured or non-structured data, in the format of text, picture, audio or

video.

Data Processing

The collected data may be incomplete, duplicate or contain errors. For example, the same demand data may

be inserted multiple time or timestamp of the demand does not match with the demand fulfilment. It need

to be cleaned before subsequent analyses. This process would include matching record, identifying potential

data inaccuracies, making computations for missing data, removing outliers, removing duplications, and

formatting the data. Simple data analytics techniques can be applied in this step.

Data Modelling and Algorithm Designing

In this step, mathematical formulas, mathematical/optimisation/simulation models and/or algorithms may

be applied to the data to model the supply chain network. It generates insights by identifying relationships

among variables, finding patterns from the data, predicting what is likely to happen and optimising solutions

by using what-if scenarios to evaluate transformative strategies for (re) designing the supply chain network.

Examples of the methods that can be used in this step are illustrated in Figure 2.2.

Figure 2.2. Examples of method for data modelling and algorithm design


11

Data Communication, Visualisation and Reports

Once the data is modelled and analysed using one or more modelling methods and algorithm designs, the

information, along with insights and results from the model, can be reported in many formats for

communication with the relevant decision makers. The supply chain network and its improvement strategies

can be visualised as a GIS map (as in Figure 2.3.), graphs (as in Figure 2.4.), tables or any other report formats.

Supply Chain Innovation

Based on the insights and results from visualization of one’s supply chain and its potential improvement

strategies, the decision makers would be able to take action to transform their network design. It may result

in new incremental or radical innovations in the supply chain network. This innovation would be derived from

the data and the model used. It would be recorded and updated into the system as new knowledge and

insights and can be used for further analysis to derive future innovation.

Figure 2.3. GIS Visualisation of the Green Field Analysis Result

Figure 2.4. Graph Reporting for Model Performance Comparison


12

Chapter 3.

SUPPLY CHAIN ORCHESTRATION PLATFORM –

A CONCEPTUAL DESIGN

In a bid to address digital complexities and pain points of industry players by leveraging the enormous supply

chain data, we propose a conceptual design of a supply chain platform, referred to as the orchestration

platform (as illustrated in Figure 3.1). This platform is structured in a way where it equips stakeholders with

proper recommendations in managing changes of material planning and flow. It integrates supply chain,

logistics operations and technologies to strategically shift supply chain thinking and logistics resources to

create more value and higher returns.

Figure 3.1. Supply chain orchestrating platform conceptual design

The platform aims to tackle the main challenges of today’s supply chain (as illustrated in Figure 3.2.) that can

be summarized as follows:


13

Figure 3.2. Main Challenges addressed through the orchestration platform

Supply Chain Transparency

Coordinated information sharing through the entire chain is still not a common practice, hence making

information available and visible across the supply chain remains as the main challenge. Functional and

geographic silos that do not share information openly often characterise traditional supply chain. Generally,

huge data amounts generated by digital technologies are stored in a complex and non-structural form that

are not machine-readable. This leads to sub-optimal performance of the supply chain which are influenced

by poor demand planning and management, high operating cost due to excessive inventory, high product

return rates (and claims), and poor service levels due to stock-out.

The orchestration platform intends to leverage various cutting-edge technologies (e.g. Big Data Analytics and

Learning algorithms) to provide seamless integration for all processes and activities in the supply chain with

secure information sharing. It would ensure that all parties have the same unified view of the database to

process real-time information automatically. It will permit a supply chain to respond effectively to increased

supply and demand, modal choices and demand volatility. Leveraging on the novel data analytics

technologies and research, the platform would be able to handle big data processing offering a secure

platform for digital supply chain transactions. It would also enable innovative and collaborative business

models.

Supply Chain Collaboration

Uncoordinated execution by supply chain partners, particularly in the last-mile stage, could result in high

costs, low productivity, and resource wastage. With limited resources, supply chain and logistics activities

have to be managed in innovative ways to ensure timely order fulfilment. Collaboration is a strategic term

for integrating different technologies, processes, resources, and networks to achieve the optimal operations

with an efficient use of resources. One common approach of supply chain collaboration is delivery

consolidation, where data exchange, demand clustering, and resource management of more than one

stakeholder are synchronous.

The orchestration platform would enable information sharing across the supply chain to encourage both

vertical and horizontal collaboration between the stakeholders. Horizontal collaboration for stakeholders

having similar logistics requirements can take advantage of potential distribution synergies, such as


14

distribution consolidation and transportation sharing. For example, using Artificial Intelligence (AI) and

machine learning algorithms, the platform would be able to predict the demand fluctuation and fulfilment

patterns. These patterns can be matched with patterns from other stakeholders for joint

deliveries/fulfilment. It would reduce supply chain and logistics inefficiency by increasing the fill rate of

logistics assets and increase the overall utilisation of the logistics assets.

Supply Chain Flexibility

Fragmentation and a stochastic supply chain network hinders the process and operations flexibility. This

nature of supply chains may require significant time and effort to make simple changes. The orchestration

platform would enable real-time planning of inventory and delivery milk runs to dynamically optimise and

configure the supply chain to accommodate changing parametric values such as change or substitution of

vendor, order quantity, safety stock and lead time. Dynamic optimisation and multi-scenario simulation are

the main tools to help networks self-reconfigure to achieve the flexibility. The platform would enable

flexibility in determining the distribution network and configuration. With the exploration of multiple

scenarios, it would be able to provide more robust solutions that can be evaluated under different kind of

criteria.

Supply Chain Self-Orchestration

With the continuing digital transformation and the ever-changing consumer landscape, long chains,

functional and geographic silos, majority of the current supply chains face difficulties to adapt and respond.

A discrepancy between production quantity, customer sales forecast, and the actual sales may result in lower

sales, while incurring higher out of stock rate and inventory disposal expenses.

The orchestration platform consisting of intelligent engines will seek to understand the customers’ demands

and reduce the discrepancy between production quantity, customer’s forecast, and the actual sales. Using a

novel machine-learning algorithm, it would reveal demand insights and provide suitable forecasting

mechanisms in order to maximise revenues, reduce costs/losses/risks within the chain, increase

responsiveness with minimum investment and manpower usage, and minimise the gap of demand and

planned supply mismatch. Ultimately, it would recommend the “optimum” supply chain network by auto-

configuring the networks and parameters to fulfil the demands. This conceptual design of the orchestration

platform can be applied by adopting both the supply chain modules with the current technologies to seed

new growth niches, boost its capabilities and translate to a stack of modules as illustrated in Figure 3.3. The

conceptual platform has an integrated AI powered engine core with multimethod modelling and optimisation

that provides possibilities for different scenario experimentation, visualisation and decision dashboards to

give rise to a unique control tower.

The features in the orchestration platform are divided into three main features, namely: control tower

interface, intelligent engine and data configuration and controller.

Control Tower Interface

The control tower interface is used to interact with the user and visualize the information and results to the

users. The functionalities in this feature can be divided into four groups, namely:


15

AS-IS Visualization and Modelling Interface

GIS visualization for supply, demand and existing supply chain are the core for this AS-IS interface. It

graphically shows the existing supply chain to identify bottleneck, risks and insights for improving the

supply chain.

Figure 3.3. Orchestration Platform Modules

To-Be (Ideal) Modelling Interface

To-Be (Ideal) interface would be used to produce ideal (optimal) scenarios for a particular supply

chain, without considering constraints from the industries or companies. For example, this interface

will be used to conduct Green Field Analysis (GFA) to identify potential locations for additional

warehouses in a particular area or identify risk analysis for a specific supply chain design. It would be

integrated with intelligent engine feature to develop models using various techniques such as

simulation, optimization and machine learning.

To-Be (Real) Modelling Interface

To-Be (Real) interface would be used to improve the To-Be (Ideal) scenarios for implementation

purposes. The scenarios would be generated by considering real constraints from the industries and

companies, such as limited funding for constructing a new warehouse or land-use regulation for a

particular location.


16

Dynamic Planning and Monitoring Interface.

This interface can be used for transportation digitalization by providing a dynamic planning and

monitoring of the operation supply chain and logistics activities based on the To-Be (Real) supply

chain design.

Intelligent Engines

The orchestration platform would be equipped with intelligent engines to generate scenarios and solutions

that will be presented by the control tower interface. Specific engines for supply chain as well as core

intelligent engines are integrated in this platform. Different engines (or different combination of engines)

would be selected to solve a specific supply chain problem. For example, supply chain network design tool

would be selected to determine alternative location for new warehouse, while optimization algorithm in

scheduling and routing tool would be selected to produce cost-effective delivery routes.

The integrated engines would create digital twinning of the “physical” supply chain network for evaluating

possible improvement scenarios and solutions. The results from these intelligent engines would be sent to

and presented in the control tower interface.

Data Configurator and Controller

This feature would capture the data and information from different data source (such as transaction

database, social media or sensor data) and store it in the one integrated database. Due to the variability of

the data, some data may need to be cleaned before it is used by the intelligent engines.

Interplay of Modules in Supply Chain Self-Orchestration

To tackle certain business challenges, different modules in the self-orchestration platform can be used.

Examples of solution methods for re-designing the supply chain network are illustrated in Figure 3.4. Several

control tower interface features combined with different intelligent engines such as data visualization, data

analytics, simulation, optimization and supply chain network design tool are applied to produce the “optimal”

supply chain network.


17

Figure 3.4. Modules for (Re) Designing Supply Chain Network

Value-Added GIS Visualization: A GIS visualization module is used to visualize key information (such as

demand) to provide preliminary insights.

Supply/Demand Clustering: Using the visualization and data analytics engine, demand can be clustered

to identify demand delivery patterns.

Green Field Analysis: Next step, Green Field Analysis (GFA) is use to identify the number of suitable

locations for supply chain nodes based on its relevant cost structure.

Supply Chain Network Design: Network Optimization and simulation are then used to find the best

configuration of a supply chain network structure as well as the flows based upon an objective function,

which typically maximizes profits.

It is important to stress that having a continuous set of data available and accessible by the intelligent engines

is necessary before we can use the orchestration platform. We can start by using a minimum set of data to

solve a particular problem in the supply chain network. The data template would be provided in the

orchestration platform. This data can be progressively and continuously populated for solving a more

complex problem. The data can be used not only to model the supply chain network but also to conduct

experiments to identify and evaluate the transformative strategies. The quality of data would determine the

quality of the models and results that the orchestration platform generated.

For the sake of completeness of the end-to-end supply chain, the platform will also allow downstream add-

on modules and new research concepts integrated within an industry robust data architecture.


18

Chapter 4.

SUPPLY CHAIN ORCHESTRATION PLATFORM

STEP-BY-STEP

An example on how the proposed supply chain orchestration platform is used to tackle a supply chain

network problem is described in this chapter.

Required Data

Minimum set of data required for this orchestration platform are as follows:

1. Network Distribution Data

Relevant data on the existing network and distribution (consist of locations of facilities, costs, capacities,

available resources), facility costs, transportation assets, transportation costs and existing routes need to

be collected.

2. Transaction Data

Daily transaction data for supplies, demands and delivery schedules are needed. It can be extracted from

the Enterprise Resource Planning (ERP) system and stored in a particular Database Management System

(DBMS) such as MySQL or MS SQL Server. Sensor and telematics data from the vehicles or other logistics

assets can also be included to present the actual movement of the goods, vehicles and logistics assets.

3. Other data

Company policies and considerations are needed to determine the implementable solutions.

Due to incomplete data, several assumptions may need to be used. For example, the demand data may only

include the weight of the delivery demand. The dimension of the delivery demand is not available. We use a

certain formula to convert the weight into the dimension.

Modelling Steps

The supply chain modelling in this orchestration platform would include the three necessary development

steps, namely: 1) “AS-IS”, 2) “TO-BE” (Ideal) and 3) “TO-BE” (Real) model development as illustrated in Figure

4.1.


19

Figure 4.1. Modelling Steps

“AS-IS” Model

The “AS-IS” model is developed to understand the existing supply chain network conditions, including the

configuration, operational requirements and bottlenecks. This “AS-IS” model is used as the benchmark for

any proposed adjustments. Thus, this model is also called the “Base” model.

Using the available data, the current supply chain network can be visualised and modelled. This visualisation

and modelling will be used to understand the existing situation and identify potential aspects that can be

improved in “TO-BE (Ideal)” and “TO-BE (Real)” model. Examples of this visualisation are presented in Figures

4.2. – 4.4.

Figure 4.2. visualizes the demands (in blue dots) and demand patterns. The demand can be grouped into

several clusters with different central of gravity (in yellow dots). The dot size represents the number of

demand. The bigger size of the dot, the higher the demand.

Figure 4.2. Demand Central of Gravity

Figure 4.3. visualizes the current distribution model (i.e. good flows) from the warehouse (yellow dots) to the

customers (blue dots).


20

Figure 4.3. AS-IS Distribution Network

Figure 4.4. visualizes one example of the exiting delivery route to deliver the demands. It applies the milk-

run distribution for several customers.

Figure 4.4. Existing Delivery Route

“TO-BE (Ideal)” Model

The “TO-BE” (Ideal) model serves as an intermediate model derived from “unconstrained” supply chain

network situations. This step would produce an optimised solution based on the model.

To develop the “TO-BE (Ideal)” model, we use the Green Field Analysis (GFA). GFA is a Geographic Information

System (GIS)/centre of gravity-based approach, which seeks to find the optimum number of storage/freight

facilities as well as to define the approximate locations for these facilities. Computations are typically based

on minimum transportation costs (calculated as “Distance” * “Product Amount”) in consideration of

aggregated demand for each customer and product, customer locations (direct distance between customers

and DCs/Warehouses), and service distance (or number of facilities to locate).

In order to build a GFA model for a particular supply chain network, several inputs are required. These inputs

include a list of products, customer locations, and the aggregated demand for each customer and product.

Typically, the user is further required to preselect a maximum service distance between to-be facilities and


21

customers or a fixed number of to-be facilities (without service distance constraints). For simplification, GFA

would only consider straight routes between the customers and facilities or the facilities to another facility.

Figure 4.5. shows an exemplary GFA result. It shows three proposed locations for logistics facilities (in green

dot) to serve the demands (blue dots) in Surabaya. The GFA model is built using a simulation software based

on two years of operations information on historical demand (by location, amount and time distribution),

product flows and costs. The number of facilities can easily be adjusted to analyse the impact on the overall

cost-to-serve. Transportation costs and cost reductions that correspond with the changed number of facilities

are illustrated in Figure 4.6. The figure shows that transportation cost (in blue line) can be reduced by adding

the number of facilities.

Figure 4.5. Illustration of Green Field Analysis Result

Figure 4.6. Transportation Costs and Cost Reduction Comparison


22

“TO-BE (Real)” Model

The “TO-BE” (Real) model is the final model that includes real-life constrains set by the industry or the

company itself. This model is an adjustment of the “TO-BE” (Ideal) model.

The GFA results may not be able to be implemented directly. It requires adjustment to align with company’s

policies and considerations. Hence, a “TO-BE (Real)” model is developed using Network Optimization (NO)

and simulation by considering the real implementation constraints such as infrastructure.

Network Optimization (NO) is used to find the best configuration of a supply chain network structure as well

as the flows based upon an objective function, which typically maximizes profits. Considerations for the NO

are:

1. Transportation cost that is driven by material flow. The larger the material flow, the higher the

transportation cost.

2. Fixed cost, the daily cost of operating the DCs. Calculating the daily operating costs per DC, the fixed

cost components that drive facility-operating costs were derived from actual cost figures.

3. Outbound Processing Cost includes salaries for delivery men.

4. Inbound Processing Cost includes salaries for warehouse operators.

In order to define the implementable network configuration, realistic industry constraints, such as the

maximum distance travelled per deliveryman (100 km) in a day and land restriction for warehouse, can be

included. These constraints are taken based on industry landscape and company’s policies. Based on these

constraints, Network Optimization (NO) will be re-developed and rerun to produce an implementable

network configuration. NO result is illustrated in Figure 4.7. This network configuration will minimize the

overall supply chain cost, with no change to the service level as compared to existing supply network

configuration.

Figure 4.7. Optimized Network Configuration


23

Real Time Scheduling and Monitoring

The ideal network configuration resulting from NO and simulation model would improve the efficiency of the

supply chain and delivery fulfilment. However, supply chains are highly susceptible to the disruptions which

may have different occurrence frequency and consequences. It would affect the level of service of (even the

best) supply chain network. It would increase delivery lead time and failure to securely deliver the goods.

To anticipate it, a tool named Smart Analytics Routing Application (SARA) is used to visualise, schedule and

monitor delivery schedules for not only the effectiveness, but also the robustness of the supply chain network

and logistics, fostering the creation of fast responses to disruptions.

Using the aforementioned algorithms and further analytics, SARA would compute the best routes and

schedules that may fulfil the areas of interest. These routes will be visualised on the map (as illustrated in

Figure 4.8.), together with their details, including the risk score, being shown to the users. By default, SARA

displays the best and optimised routes based on the computed score index. At a quick glance, insights on the

best alternatives can be gained to adopt for the delivery plans. When used on pre-existing areas of interest,

possible costs, time or distance savings can be identified if the suggested routes differ from the current routes

being adopted. Furthermore, the routes can be viewed based on their top priority of concern. For instance,

the top routes may be generated based purely on the risk index, should risk be the only concern. A few routes

can also be compared at the same time (as illustrated in Figure 4.9.).

Figure 4.8. Route Visualisation in SARA


24

Figure 4.9. Route Comparison in SARA


25

Chapter 5.

Key Takeaways and Future Work

In this whitepaper, we address the challenges in digital transformation for supply chain and logistics industry

through a self-orchestration platform that we have researched and would continue to explore, develop and

expand to improve efficiency and effectiveness of a portfolio of logistics assets in digital transformation era.

Consisting of various supply chain modules and techniques, this platform will foster supply chain

transparency, collaboration, flexibility and self-orchestration to efficiently and effectively cope with the

complexities in the digital supply chain. Ultimately, using this platform, the intended outcomes are to achieve

an efficient way of analysing and visualising data from various sources more time-efficiently schemes to offer

optimal prices. Supply chain planning and scheduling can be self-configured according to the updated

information. This is needed to cope with variable supply and uncertain demand to mitigate risks in the supply

chain. The practices of dynamic pricing scheme to offer the optimal price can also be achieved by the

platform. This helps to maximise company revenue. Furthermore, multiple scenarios with potential policies

and planning rules can be performed on changing parametric values through scenario planning to ensure the

best option among all possibilities will be selected.

In addition to the various supply chain modules and techniques, this self-orchestration platform will be

equipped with big data analytics and machine learning techniques utilising a safeguarding blockchain

infrastructure (as illustrated in Figure 5.1.). It would enable the companies to take the leap into digital supply

chain transformation.

Figure 5.1. Self-orchestration Platform Towards Big Data and Blockchain Infrastructure


26

The platform is intended to serve as a digital twining of the “physical” supply chain network that provides

virtual environment for evaluating new solutions and scenarios. It enables the user to conduct sandbox

testing by changing only a particular aspect in the supply chain by isolating and only changing this aspect to

understand its impacts to the overall supply chain as well as to conduct the overall performance test of the

supply chain by changing multiple aspects in the same time.

Several examples of the platform implementations in different supply chain problems will be presented in

“The Logistics Institute - Asia Pacific Advanced Executive Programme in Supply Chain Innovation”. It consists

of three modules, namely: Transformational Strategies for Supply Chain & Logistics Management;

Optimization, Simulation & Modelling in Supply Chain & Logistics Management; and Project-Based Module

in Supply Chain Innovation & Solutioning.

And lastly, this is an ongoing work and we hope that you, the reader, in turn will be motivated to collaborate

with us to develop the self-orchestration platform to further enhance the practice and alignment of business

asset innovation in e-commerce.


27

The Logistics Institute – Asia Pacific National University of Singapore 21 Heng Mui Keng Terrace, #04-01

Singapore 119613 Tel: (65) 6516 4842 Fax: (65) 6775 3391 E-mail: [email protected] Website: www.tliap.nus.edu.sg

Documents

Digital Twinning of Supply Chains: An Orchestration Platform for … … · This orchestration from an as-is to a to-be faces immense data challenges as one moves through several