Putting Together the Pieces: Supply Chain Analytics Insights on Technology Options

09/07/2016

By Lora Cecere

Founder and CEO Supply Chain Insights LLC

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Contents

Research Methodology

Disclosure

Executive Summary

What Defines a Supply Chain That Works Well?

The Steps to Define a Holistic Supply Chain Analytics Strategy

The Role of Analytics in Developing Strategies for Supply Chain 2030

Making a Technology Selection

Recommendations

Conclusion

Terms to Know

Research Summary of Quantitative Data

Additional Reports of Interest

About Supply Chain Insights LLC

About Lora Cecere

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Research Methodology We are committed to delivering thought-leading content for the supply chain leader. The goal of our

research is to be the first place that supply chain visionaries turn to in order gain unique insights on

technology evolution.

This report is based on qualitative research completed during the period of January-July 2016. In this

research effort, we interviewed 35 analytics technology providers to understand their solutions. This

was followed by interviews with 30 innovative supply chain leaders. Here we share the findings.

To support this research, and take it one step further, we augment these qualitative insights with

quantitative survey analysis collected in preparation for the 2016 Supply Chain Insights Global

Summit. In this research we share insights on the importance of supply chain analytics in Supply

Chain 2030 strategies. (Details on this quantitative survey analysis is shared in the Appendix.)

Disclosure Your trust is important to us. In our business we are open and transparent about our financial

relationships. In this research process we never share the names of respondents, or give attribution

to open comments collected in the research.

Our philosophy is “You give to us, and we give to you.” We collect data from a private network of

qualified participants and openly share the results. The participants of our research always receive

the final reports; and, if interested, we share insights from the studies with the respondents of our

quantitative surveys and qualitative interviews in a complimentary one-hour phone call with supply

chain teams.

This report is written and shared using the principles of Open Content research. It is intended for you

to read and share freely with your colleagues and through social channels like LinkedIn, Facebook

and Twitter. When you use the report all we ask for in return is attribution. We publish under the

Creative Commons License Attribution-Noncommercial-Share Alike 3.0 United States and our citation

policy is outlined on the Supply Chain Insights Website.

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Executive Summary Supply chains are drowning in data, but are low on insights. While the cost of computing memory was

once a barrier to executing an analytics strategy, this is no longer the case. The largest barrier is the

understanding of new forms of analytics.

Historically, the term supply chain analytics was used to describe reporting. This is no longer the

case. Today there are more options and capabilities for supply chain analytics. There is a proliferation

of new technologies flooding the market.

Ironically, despite the explosion of options as shown in Figure 1, the supply chain operating team is

more conservative. It is a skewed distribution. When it comes to decision support, the number of late

adopters outnumber the early adopters three to one. The lack of early adopters, the rapid rate of

change, and the conventional architectural definitions (primarily focused on Enterprise Resource

Planning or ERP-based architectures) are barriers to the adoption of new forms of supply chain

analytics.

Figure 1. Risk Profile of Companies Adopting Decision Support Analytics

Driving change requires education, testing and experimentation. The technology market is shifting:

there is a market evolution of technology offering promising capabilities. The goal of this report is to

share insights on how business users can drive new levels of competitive advantage through supply

chain analytics.

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What Defines a Supply Chain That Works Well? Data access and usability are prerequisites for a supply chain to perform well. Today both are issues.

As shown in Figure 2, 35% of supply chain leaders list the ability to get to data, and use analytics to

drive insights, as a top business pain.

Figure 2. Relative Supply Chain Business Pain

One of the issues is the lack of a comprehensive analytics strategy. The historic focus was on

building analytics within pockets—or functions—of the organization, centered around the applications

of advanced planning (APS), order management (ERP), transportation planning (TMS), sourcing

(SRM), and warehouse management (WMS).

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Why was this a problem? This focus is not holistic. Most companies have only invested in reporting,

without a data analytics strategy. As a result, as shown in Figure 2, data access and usability of

analytics rates as the fourth largest business pain.

As demand and supply volatility increases, the need for data and insights grows. Great analytics also

improves visibility and organizational functional alignment. It is also needed to drive outside-in

business processes. A holistic analytics strategy is the answer to many of the pains ailing the supply

chain today.

Today only one in three companies believe their supply chains are working well. While companies

would like to have supply chains that are more proactive, as shown in Figure 3, the majority are

reactive.

Figure 3. Current Characteristics of Supply Chains

When we examine this data more closely in companies that believe that their supply chains are

working well, there is a significant difference (at a 90% confidence level) between companies that can

access data and use supply chain analytics well and those that cannot.

Other significant factors include executive understanding of the supply chain, and cross-functional

alignment. Companies should tackle these change management issues up front (and often) in the

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development and execution of a holistic data strategy. Most of the terms and concepts in this report

will be new to the supply chain leadership team. As a result, companies will need to recognize the

need for change management and human capital development along with the testing and trial of new

forms of technology. As a result, data analytics for the supply chain should not be approached

as a technology project. There needs to be a clear charter that includes aggressive management of

the change management issues.

Figure 4. Characteristics of Supply Chains That Are Working Well Versus Supply Chains That Have

Room for Improvement

Another issue is that the new technologies for supply chain analytics don’t come traditional providers

of supply chain technology. This sets up nine major issues.

1. New Forms of Supply Chain Analytics Providers Have the Goods, but Not the Knowledge.

New supply chain analytics vendors are struggling to learn supply chain. They do not have

supply chain backgrounds and each manufacturing/distribution organization is different. As a

result, the requests coming to them from companies are confusing.

2. Traditional Supply Chain Vendors Have the Knowledge, but Are Slow to Change/Adapt.

Traditional supply chain technology solution vendors are slow to adopt new forms of analytics.

Why? It requires a rewrite of traditional architectures. As a result, the adoption of supply chain

analytics requires companies to redefine current standards for technology purchases.

3. New Capabilities Redefine the Fundamentals. Supply Chain processes are changing. The

shifts are many. New forms of analytics are redefining decision support. The changes are

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anything but subtle. It is redefining supply chain process definitions. Advanced Planning, as we

know it, with concurrent optimization using simplistic algorithms, will be obsolete in five years.

This includes moving from inside-out to outside-in processes (use of channel and supplier data).

The shift from one-to-one to many-to-many architectures will redefine supply chain visibility.

4. Not Always an Evolution. We Are Experiencing a Redefinition of Decision Support.

Traditionally, supply chain planning evolved to complement the decision support tools of finance,

marketing and sales. With the evolution of cognitive and prescriptive analytics, the traditional

enterprise architectural definitions of CRM, SRM, APS and ERP are not sufficient. These

traditional taxonomies will become outside-in and cross-functional using structured, and

unstructured, data to feed deeper analytic engines for prescriptive and cognitive analytics.

5. Talent and Support Is the Missing Gap. While traditional analytics approaches were selected

and maintained by information technologists, the new options for analytics are designed to

empower line-of-business users. The roles between IT and business process owners are

profoundly changed to drive self-administration, maintenance, and support by the line-of-

business leaders.

6. Standards and Master Data No Longer the Formidable Barrier. The Barriers Shift. While

supply chain automation has been hampered by master data issues and the lack of standards,

this is no longer the obstacle it was a decade ago. The use of machine learning and cognitive

computing to detect data anomalies, clean up data, and structure learning from disparate data

sets will make these current issues disappear. Gaining an understanding of this basic shift is

critical to unleashing new capabilities.

7. Supply Chains Do Not Play by the Rules. Should They Have to? With ever-changing

business requirements, it is hard for a supply chain to live within tight and inflexible rules.

Traditional supply chain rules—allocation, inventory matching, deduction and order matching,

Available to Promise (ATP)—were driven by “single if to single then” logic. This limitation made

the rules inflexible and tough to manage. New forms of analytics unleash the opportunities to

have flexible and learning rule sets for “multiple ifs connected to multiple thens.”

8. Buckle Your Seatbelt. Get Ready for the Rate of Change. While traditional architectures are

deployed in major releases, software based on as-is and to-be documentation, this is not the

case with supply chain analytics. Instead, these projects are small with a focus on test and

learn. The technologies will change at a rate of 10 times that of conventional supply chain

applications. There will no longer be a need for maintenance upgrades and ongoing

maintenance support contracts. A major mistake that companies will make is treating new forms

of analytics as a traditional technology project.

9. Don’t Limit the Technology. Answer Questions That You Do Not Know to Ask. To fully use

supply chain analytics, we must be open to the use of new forms of decision support in the form

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of prescriptive and cognitive analytics. Companies must divorce themselves of their

spreadsheets and allow analytic engines to manage the heavy lifting. This is an issue for many

companies that are deeply wedded to spreadsheet-based deployments and spreadsheet labor

ghettos. Working with machine learning and cognitive computing enables companies to answer

the questions that they don’t know to ask.

As companies drive competitive advantage through supply chain analytics, the first step is

recognizing that they are no longer hamstrung by the issues of the past.

The Steps to Define a Holistic Supply Chain Analytics Strategy Supply chain analytics requires defining a layer to lie between operational applications—Advanced

Planning, Enterprise Resource Planning, Product Lifecycle Management (PLM), Supplier

Relationship Manager, Supply Chain Execution (SCE), Transportation Management Solutions, and

Warehouse Management Solutions—and workforce productivity technologies like search engines

such as Firefox, Chrome, and Internet Explorer, and collaborative applications like Yammer and

Socialcast, and Microsoft Office technologies like Word, Excel, and Access. Traditionally, the focus

of supply chain leaders was on functional/operational technologies. As a result, there has not been a

holistic focus like the one which is outlined in Figure 5.

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Figure 5. Defining a Holistic Analytics Framework

To define this holistic analytics strategy, start by defining the problem to be answered. Then sort

through the alternatives. This section is designed to be a guide:

A) What Data Types Are Needed to Answer the Problem? When solving business problems and

driving new insights, the first question to ask is “Which data set is required to yield the greatest

insights?” Data comes in two types: structured and unstructured data. Most of the automation in the

supply chain today is based on structured data. (Structured data fits well into rows and columns.

Examples of structured data include transactional and planning data.) Unstructured data does not fit

into relational tables. These unstructured data sets include, but are not limited to, data types like

social sentiment, call-center data, commercial drawings, contracts/partner agreements, maps,

ratings/review data, pictures, warranty information, and weather data.

1. Structured Data. If the data is structured, build analytics on top of relational databases like

Microsoft, Oracle, SAP HANA and Teradata.

2. Unstructured Data. If the data is unstructured then the system requires the use of non-

relational database open source code through Cassandra, Cloudera, and MongoDB.

B) How Fast and When Does the Data Need to Move? (Not All Data Needs to Move at the Same

Rate or First Class.) What Are the Required Data Structures? The second step in the definition

of data infrastructure starts with mapping data flows. Some questions are, “Where does data need

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to pool into lakes for discovery? When does data need to stream in real-time, based on sensor data

outputs?” While streaming data needs to move in real-time flows, most data in the enterprise can

move at a slower rate to support batch processes. Columnar database structures help to move

transactional, and frequently used data, more quickly. (Columnar architectures like SAP HANA

brings frequently used data, based on columns, into memory). However, columnar database

structures do not work as well for hierarchical and time-phased data architectures of supply chain

planning. Here are some details:

1. Streaming Data Architectures. Stream processing is designed to analyze and act to drive

real-time “continuous queries” (i.e. SQL-type queries that operate over time and buffer

windows). Streaming data analytics are defined by the ability to continuously calculate

mathematical or statistical analytics, on the fly, within the stream. These streaming-data

processing solutions are designed to handle high volume, in real-time, with scalable, highly

available and fault-tolerant architectures. This enables the analysis of data in motion. This is

in contrast to the traditional database model where data is first stored and indexed with

subsequent query processing. Stream processing takes the inbound data while it is in flight,

as it moves through the server. Stream processing also connects to external data sources,

enabling applications to incorporate selected data into the application flow, or to update an

external database with processed information. There is no standard definition of streaming

data architectures for supply chain. These architectures are evolving. The most advanced

companies are using the open source code defined by Apache Spark.

2. Data Lakes. A data lake is a storage repository holding large amounts of raw data in a

native format—structured, semi-structured and unstructured data. The data is formatted

when called for use, and is formatted for analytics/visualization based on business

requirements. Data lakes are primarily used in supply chain for discovery and insights on

commercial, quality or supplier data. Nonrelational database technologies based on Hadoop

are being used for discovery on structured and unstructured data.

3. Data Warehouse. A data warehouse is used to normalize, harmonize and structure data for

reuse and enrichment. The most common use case is harmonization and synchronization of

structural data.

4. Hierarchical Data Structures. The organization of data into a one-to-many

representation/schema for analysis. In supply chain, the most common use of hierarchical

data structures is for forecasting and demand management.

5. Transactional Data Formats. Transactions, or small events, are recorded into relational

database structures. Relational data structures in Enterprise Resource Planning are

architectures that are designed to record and retrieve business transactions within the

organization to a common system of record.

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6. Time-Phased Data Structures. These data structures format over time for analysis. Time-

phased data is the basis of supply chain planning. In the design of supply chain planning,

the focus is on the reuse of rows. In contrast, the management of transactional data is more

focused on column reuse.

C. What Is Required to Improve Insights? Which Types of Intelligence/Data Models Yield the

Best Results? The goal is to drive insights by adding intelligence to better understand data patterns.

The progression of supply chain analytics moves through four steps. While descriptive analytics

enables the visualization of data, predictive, prescriptive and cognitive analytics brings progressive use

of optimization and machine learning to drive insights from data. These technologies are packaged into

engines requiring data inputs, data model definitions and data outputs. The use of analytical engines

enables the sharing of new insights. The evolution of capabilities is shown in Figure 6.

Figure 6. Evolution of Progressive Levels of Analytics in Supply Chain Engines for Analytics

1) Descriptive. Visualization of data is the first step. With the use of descriptive analytics

companies can easily gain insights of data relationships without the deployment of engines.

2) Predictive. The use of predictive analytics enables the use of different forms of algorithms

to drive new insights. This includes linear optimization, genetic algorithms and stochastic

optimization. In each case, there is a well-defined set of inputs, engines and outputs. These

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run as batch processes within the supply chain. The use of predictive analytics is most

commonly used on historic data.

3) Prescriptive. Prescriptive analytics not only gives an output, but will also give

recommendations to exceptions. The most commonly used forms of prescriptive analytics

are in transportation routing based on road construction, traffic delays, and

4) Cognitive Learning. The use of machine learning to learn from data. Cognitive computing

starts with a rules-based ontology that structures the learning and then drives insights over

time based on hypothesis engines.

While predictive analytics is limited to structured data, prescriptive and cognitive analytics are

evolving to be used on nonrelational databases based on structured and unstructured data.

D. Delivery Mechanisms. Parallel processing and cloud-based delivery increases the options for the

buyer of analytics. While the traditional approach was licensed software with a maintenance agreement

of 18-22%, cloud-based deployments increase the options and reduce the costs.

1) License. Purchase of applications and implementation of the analytics through a license

deployment model including maintenance and implementation fees.

2) Hosted. Licensed software is run and managed by a third-party and deployed via cloud-

based architectures. In the selection of hosted software clearly understand the instance

architectural definitions and the limitations due to server sharing.

3) Software as a Service. Solutions deployed natively over the internet. These cloud-based

architectures redefine maintenance and software upgrades. These applications are usually

purchased based on a number of users to use the software over a predetermined period of

time.

4) Business Process Outsourcing. In Business Process Outsourcing (BPO) services, a third

party drives the business process on behalf of their client. They use analytics through cloud-

based deployments to design, manage and maintain analytics software. The use of a third

party to provide not only software, but also the management and support team to run the

software to drive the analytics. Cloud-based services also enable the use of BPO as an

option.

F. Organizational Design. Determine how the solution will be used and who will maintain the data

models and inputs.

1) IT-Based Deployments. In traditional deployments, the software is maintained and

upgraded by informational technology teams. By definition, the use of relational databases

and reporting based on Enterprise Resource Planning (ERP) are IT intensive.

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2) Self-service. To maximize the value of new analytics solutions, focus on self-service

deployments. These designs allow business leaders to drive their own reports and queries.

While the traditional analytics deployments were very dependent on DBAs (Data Base

Administrators), newer forms of analytics allow business users to own their applications.

3) queries, plan, or

4)

The Role of Analytics in Developing Strategies for Supply Chain 2030 As companies plan for Supply Chain 2030, and build strategic plans, analytics plays a major part in

the development of new capabilities. The relative focus of disruptive analytics is shown in Figure 7.

Figure 7. Role of Analytics in the Development of Supply Chain 2030 Strategies

This evolution of Supply Chain 2030 strategies will be built on multiple, not one analytics technique.

The portfolio of analytics defining Supply Chain 2030 are shown in Figure 8

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Figure 8. Most Important Analytics Techniques in the Evolution of Progressive Levels of Analytics for

Supply Chain 2030

Making a Technology Selection Today there are more options. The traditional world of analytics—with well-established terms and

technologies-- is redefined. The first step is formulating the business questions. Here we share

answers to frequently asked questions:

Which vendors should I consider for data visualization? Consider FusionOps, Qlik, Spotfire, or

Tableau Software. (Trifacta enables data wrangling from Hadoop into a Tableau visualization layer.)

In mining unstructured text and making it usable in supply chain processes, which vendors

should be short-listed? Clarabridge and SAS Institute.

In choosing a vendor for streaming data, which vendors should I evaluate? Savi Technology,

and SAS Institute.

When getting data out of SAP tables to drive insights, which vendors should be considered?

Data wrangling and data extracts from SAP are easier when using Every Angle and Trufa.

In supply chain planning, which vendors have a natively cloud-based environment? This

includes o9 Solutions, Kinaxis, and Steelwedge. Look for Oracle to release a cloud-based planning

solution in Q1 2017.

I am working with complexity and would like to move to attribute-based planning. Which

solutions can help to plan products at an abstracted level of attributes to see beyond an

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item-at-a-location view (SKU) level of planning? Consider the use of attribute-based planning

technologies from Adexa or Logility.

What technologies can I use to build a discrete event simulator of the supply chain to test

the feasibility of supply chain design? Consider the solutions from AnyLogic, Arena or

LLamasoft.

My goal is a self-correcting and learning supply chain. Which artificial intelligence (AI)

solutions can help? One of the most exciting technologies is the use of machine learning to sense

anomalies and redefine master data management through rules-based ontologies and outlier

detection. Consider the use of technologies from Enterra Solutions, IBM Watson, Rulex and

TransVoyant.

I am looking for a simple, easy to use solution for planning based on spreadsheets, but I

would like to have the spreadsheets anchored to a system of record. What do you suggest?

Consider using Boardwalktech or John Galt.

I would like to build a set of customized set of optimizers to solve a supply chain problem.

What do you suggest? Use the enterprise-class platforms from Opalytics and SAS Institute.

Recommendations Build analytic strategies with the goal in mind. Think past pockets of analytics surrounding

conventional applications and build a holistic vision on how data should pool, stream and transform

the supply chain. Here are seven recommendations to consider in building your strategy:

1. Ask the Right Questions. Think Beyond Existing Paradigms. A frequent mistake companies make

is to focus only on technology. Start with the goal and work backwards. Train employees to ask the right

questions and use analytical approaches to drive root cause analysis. Use new forms of analytics to

unleash new insights.

2. Define the Metrics That Matter. Define a balanced scorecard and use analytics to drive frequent

updates of cross-functional metrics. In selecting the metrics for a balanced scorecard work with the

financial teams to set realistic targets. (Use of the Supply Chain to Admire report analysis can help you

to understand what is feasible based on industry-specific outcomes.)

3. Get Good at Managing Data. Data volumes are increasing and cleanliness issues abound. Get good

at using new forms of analytics to clean and parse data. Use cognitive and prescriptive analytics to

redefine master data management.

4. Focus on Self-Service. Take Ownership of Supply Chain Analytics. To maximize the value of

analytics, implement solutions that can be administered by line-of-business users. Partner with

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Information Technology (IT) teams to make self-service a goal. To implement this strategy, focus on

solutions like Anaplan, Halo, Qlik and Tableau which can be managed by business teams.

5. Not All Data Needs to Fly First Class! Move data at the speed that the business requires. Costs

escalate as data speed increases. While many supply chain strategies state that the data needs to

move real-time, the use of streaming data architectures (which are expensive) should be only used for

sensor and real-time event data.

6. Rethink Old Problems and Apply New Solutions. New forms of analytics offer opportunities to solve

old problems. Use technologies like Enterra Solutions and Rulex to manage data anomalies and

redefine master data management. Work with Cloudera, Infosys and Hortonworks to use Hadoop and

open source standards to combine structured and unstructured data.

7. Think Beyond Three- and Four-Letter Acronyms. While traditional data architectures center on

reporting on SCM and SCE architectures, think beyond APS, CRM, ERP, SRM, TMS, and WMS

requirements. Connect traditional architectures to workforce automation by thinking holistically about

data requirements and analytics.

Conclusion New forms of analytics offer great opportunities for companies that can think beyond traditional

definitions. However, it is not about technology for the sake of technology. Business value is

maximized when companies build with the goal in mind with a focus on answering the right business

questions.

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Terms to Know Getting clear on terms is the first step to driving a supply chain transformation. To help teams to gain

greater value from the report, here we provide definitions of the terms used within:

Artificial Intelligence.

Artificial Intelligence. The use of computer-based learning that includes cognitive learning

analytics and machine-to-machine learning using sensor data. Artificial intelligence is the backbone

of the autonomous supply chain.

Apache. Open source code development through the Apache Software Foundation. The group,

founded in 1993, was founded by eight developers. Over the last three decades the group has

managed a governance structure to release code to support improvements in analytics including,

but not limited to, Hadoop, Hive and Spark.

Apache Spark. An open-source cluster computing framework to enable extreme computer

parallelism and streaming data architectures with fault tolerance.

Autonomous Supply Chain. The use of analytics to enable robotics and self-driving vehicles.

Cloud-based Software. Data deployed through cloud-based deployments. This can include hosted

applications and natively configured Software-as-a-Service (SaaS architectures).

Cognitive Learning. Analytics that learn over time, simulating the human brain. Cognitive

computing is a form of Artificial Intelligence. Within the engines, the data relationships are often

represented by a rules-based ontology to test hypothesis engines to drive learning on structured

and unstructured data.

Data Lake. The pooling of data of different forms into a repository to enable discovery. The use of

prescriptive and cognitive learning on top of a data lake can answer the questions that we do not

know to ask.

Data Wrangling. The handling, cleaning and formatting of large amounts of data to submit for

processing.

Discrete Event Simulation. Modeling of discrete events in a simulation environment.

Decision Support Technology. The use of mathematics and optimization to improve decisions.

Common forms of decision support technologies are supply chain planning, price management and

trade promotion management.

ETL. A technology that Extract(s), Transform(s) and Load(s) data into data warehousing

technologies. The data can be homogeneous or heterogeneous.

Hadoop. An Open Source software framework for distributed storage and processing of very large

data sets on computer clusters. The approach is built with the assumption that hardware failures are

common. It is defined by the Hadoop Distributed File System (HDFS) and a processing layer

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termed MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a

cluster.

Hosted Solutions. Centralized software clusters software supporting remote users over the cloud.

The solutions can be hosted internally or externally, but the architectures are fundamentally different

than Software as a Service (SaaS). Software as a Service is a form of cloud-based software that is

designed for the cloud and enabled by cloud-based services. The fee structures are subscription—

typically with a three-year license agreement—and maintenance is automatically downloaded.

Impala. Impala is a modern, open source MPP SQL engine architected from the ground up for the

Hadoop data processing environment. Impala provides low latency and high concurrency for

BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Apache.

Hive. Open source analytics for query on large data sets in nonrelational database structures.

Hadoop. An open source software framework for distributed storage and processing of large data

bases on computer clusters based on commodity hardware. The core is a distributed file system

(HDFS) and processing layer termed MapReduce. Hadoop splits files into large blocks of data and

distributes the data across nodes.

Machine Learning. A subcomponent of cognitive computing, machine learning enables learning

from pattern recognition.

Nonrelational Database. While traditional supply chain applications are written using relational

data bases with referential integrity between rows and columns based on a data model,

nonrelational databases are being increasingly used for large volume and real-time web

applications. These are termed NoSQL. Nonrelational database architectures are 30-40% less

costly than relational database structures.

Open Source Software. Supporters/programmers voluntarily write and exchange software. Apache

is a type of open source for big data supply chain analytics. Governance of the code is based on a

formalized system of software committers.

Relational Database. The representation of data in rows and columns into tables with referential

integrity. The majority of supply chain analytics are based on relational database infrastructures.

Rules-Based Ontology. In cognitive learning, a rules-based ontology is a representation of the

relationships of data elements and rules/beliefs to initialize cognitive computing.

Self-Service. The design of analytics to enable the management and configuration of analytics by

line-of-business users to minimize the involvement of Information Technology teams in day-to-day

business support.

Sentiment Data. The mining of unstructured data to visualize patterns. Data includes social,

customer service, and warranty data.

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Simulation. The use of analytics to understand the feasibility of network design through discrete

event simulation where events and outcomes are simulated minute-by-minute and hour-by-hour

analytic routines to see relationships in complex data.

Streaming Data. Data that is moving real-time in streams requiring the use of an architecture for

streaming data. Streaming data is generated by sensors, telematics,

Unstructured Data. Data that does not neatly fit into rows and columns. Unstructured data includes

GPS data, maps, pictures, quality data, social sentiment, weather, warranty,

Visualization. The use of analytics to display the patterns and status of operational data to enable

action.

“What-If” Analysis. The design of an optimizer in decision support to run multiple scenarios—

changing inputs and tweaking data models—to determine the best output.

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Research Summary of Quantitative Data As a part of our research method, we source survey respondents through open research

techniques—sharing of links with conference attendees, on LinkedIn and through direct mail

campaigns. This research was collected from the pre-event survey for the Supply Chain Insights

Global Summit held on September 6-9, 2016.

Consistent with the philosophy is that “respondents give to us and we give to them.” All respondents

participating in this survey were given the results of this study at the conference and invited to share

in a roundtable discussion with other survey participants to gain additional insights.

In our research, the names, both of individual respondents and companies participating, are held in

confidence. Here we share the demographics to help the reader of this report gain a better

perspective on the results. The demographics and additional charts are found in Figures A and B. At

the bottom of each image are the specific questions asked in the survey along with the survey

demographics.

Figure A. Summary Overview of Pre-event Study

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Figure B. Demographics of Line-of-Business Respondents

Additional Reports of Interest

Prior Reports in This Series

In the Putting Together the Pieces report series, we try to help organizations better understand

technology markets. This is our third report in this series. To gain additional insights on the topic of

procurement and sourcing analytic solutions to build Market-Driven Value Networks, consider these

additional reports:

Building B2B Networks: Who are the Players?

Inventory Optimization in a Market-Driven World

Putting Together the Pieces: Sales and Operations Planning 2015

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About Supply Chain Insights LLC Founded in February, 2012 by Lora Cecere, Supply Chain Insights LLC is beginning its fifth year of

operation. The Company’s mission is to deliver independent, actionable, and objective advice for

supply chain leaders. If you need to know which practices and technologies make the biggest

difference to corporate performance, we want you to turn to us. We are a company dedicated to this

research. Our goal is to help leaders understand supply chain trends, evolving technologies and

which metrics matter.

About Lora Cecere Lora Cecere (twitter ID @lcecere) is the Founder of Supply Chain Insights LLC and

the author of popular enterprise software blog Supply Chain Shaman currently read

by 5,000 supply chain professionals. She also writes as a Linkedin Influencer and

is a a contributor for Forbes. She has written four books. The first book, Bricks

Matter, (co-authored with Charlie Chase) published in 2012. The second book, The

Shaman’s Journal 2014, published in September 2014; the third book, Supply

Chain Metrics That Matter, published in December 2014; and the fourth book, The

Shaman’s Journal 2015, published in September 2015.

With over 12 years as a research analyst with AMR Research, Altimeter Group, and Gartner

Group and now as the Founder of Supply Chain Insights, Lora understands supply chain. She has

worked with over 600 companies on their supply chain strategy and speaks at over 50 conferences a

year on the evolution of supply chain processes and technologies. Her research is designed for the

early adopter seeking first mover advantage.