Cloud, saas and analytics driven value chain business transformation version 1.1

Salil AmonkarValue Chain Business TransformationIndustry Expert and Thought Leader

The convergence of Cloud, SaaS and Machine Learning represents a new

opportunity to drive significant Business Transformation in the Value Chain

Cloud, SaaS and Machine Learning

for Value Chain

Transformation

Contents

2 Title of the book

3

4

5

8

10

11

12

Note from the author

Fundamentals of Value Chain Best Practices

Impact of Cloud SaaS and Analytics on Value Chain

Characteristics of solutions for Value Chain Business Transformation

Review of Market Place Solutions

Summary

References

Note from the author

3 Cloud, SaaS and Advanced Analytics for Value Chain Business Transformation

Dear Readers,It is my privilege to share with you insights from the business transformational experience

I have gained working over nearly three decades with various High-Tech companies in the Silicon Valley, Discrete Manufacturing and Retail companies in Europe and Asia.

The pace of change brought forth by the disruptive effect of cloud and SaaS is occurring at a much faster pace than that can be matched by enterprises. The high-tech industry has gone through multiple transformations, from mainframe to PC to client server architecture during the pre Y2K days, to the post-Y2K era of the Internet and now, with Cloud and SaaS. With this evolution now data can be securely accessible anytime, anywhere by anyone. Expectations around when, how and how much the customer should expect to pay for all of this has also seen tremendous change.

Example of this change can be noted in how high tech companies are forced to react in terms of their offerings and the monetization models that they need to focus on. An example of this change in the value chain context is retail companies trying to rapidly build their Omnichannel capabilities as they realize that driving a customer to a decision in their favor rests on a brand’s ability to engage the customer on multiple fronts all with a consistent customer experience. These and other dramatic shifts on the business fronts will drive most enterprises to look at opportunities to ensure that their Value Chain processes are able to keep up with this change.

In my role as Management Consultant focusing on leveraging Technology to drive Business Transformation and having worked with several companies in implementing successful business transformational initiatives I have gained a few insights that I would like to share with all. I have used these insights in driving our own internal Cloud SaaS product design which has been primarily based on the needs expressed by our customers that we see as remaining unfulfilled.

My organization Pluto7 focuses on business transformation for Value Chain through Cloud SaaS and Analytics and we have developed our own cloud-based supply chain management subscription offering - Planning in a Box. Our collective experience has also contributed to thoughts expressed in this paper.

- Salil Amonkar

Fundamentals of Value Chain Best Practices The fundamentals of Value Chain Planning and Supply Chain Planning can be applied conceptually to

all industries. These are the four key areas namely Plan, Source, Make and Deliver, except that for service supply chains the make and deliver is replaced by serve and deliver.

Many publications have been written by many thought leaders and experts on Top 10 Best Practices for Value Chain and Supply Chain Management. After having read many of these and, coupled with my experience of having successfully deployed these as solutions across multiple customers, the following represents my personal view on how these apply to the above five areas:

1. An effective Value Chain is one where the corporation passes value on to the customer, with the least possible cost to the business.

2. Plan is the starting point and driver of the Value Chain/Supply Chain Planning processes and how this is managed has cascading impact on rest of the Value Chain. Best practices around Plan process area have to do with Forecasting, Collaboration and Metrics.

3. Best practice(s) around Sourcing Operations are key to having the necessary flexibility to follow demand changes while meeting lead times and cost constraints.

4. Make implementations across the industry have focused on best practices like Lean and Agile Manufacturing and usage of Technology to drive automation in factory management and related logistics.

5. Finally but not the least the effective external collaboration between partners, delivery scheduling and streamlined logistics (forward and reverse logistics) are key to a good delivery process. Cloud SaaS and Analytical solutions using Predictive Analytics and

Machine Learning provide a disruptive and significant potential when used innovatively to drive Forecasting with high forecast accuracy, easy collaboration and provide business real time data for KPI management.

Similarly using Data Sciences and Machine Learning to determine supply patterns can be used to achieve flexible sourcing operations at lower cost.

Finally Machine Learning and Artificial Intelligence provide capabilities to streamline Distribution and Logistics processes in a way that has not been possible before.

Fundamentals of Value

Chain Best Practices

conceptually do not

change, what changes is

how technology advances

can be leveraged to

achieve the same


Figuratively speaking Value Chain (Supply Chain) is the infrastructure like combination of roads, train tracks, air routes, sea routes , stations, airports etc. and Forecasting sets the plan on how organizations will move traffic through these. However as in case of real highway experiences that we go through in everyday life; similarly weaknesses in forecasting cause slow downs and impeded movements in the Supply Chain. The key to Forecasting is learning from past trends. At the heart of machine learning is an algorithm that "learns" from data which can be used to drive ever more accurate predictors of demand which in turn will drive value chain planning. This game-changing capability is possibly the opportunity to reduce costs and reduce confusion. Using Supply Chain jargon it can significantly help reduce the impact of the “Bull Whip” effect which causes small demand changes to have significant impact on back end supply chain.

Using artificial intelligence derived from machine learning based models to establish a variation over the normal baseline that is typically generated by today’s forecasting models enable exception based demand management leading to higher forecast accuracy. This is done by ensuring that appropriate visualizations are provided that clearly highlight the exceptions.

Using Cloud SaaS based approach to facilitate feeding of multiple forecast input data feeds in a collaborative manner helps enhance the forecast accuracy since more data and more variations enable the machine learning models to be even more effective.

5

Plan – Forecasting, Collaboration & KPIsMachine learning based Artificial Intelligence models applied to forecast data and coupled with appropriate visualizations that highlight only those demand items that need to be managed help drive forecast accuracy. It provides the ability to react to quick changes in demand that otherwise cannot be detected by today’s conventional methods. Using KPI’s such as Forecast Accuracy, Forecast Bias is recommended.

Cloud, SaaS and Advanced Analytics for Value Chain Business Transformation


Forecasting is learning from past trends.

Machine learning with algorithms that learn from

data can be used to drive accurate

prediction of Forecast which in

turn can drive Value Chain



The Technology advances in Cloud SaaS and Advanced Analytics based on Artificial Intelligence models based on machine learning have significant impact on transforming the Source, Make and Deliver Functions in the Value Chain.

While having a good Sourcing Strategy is an important piece in the Supply Chain its operational implementation is dependent upon significantly on usage of technology. Effective collaboration is key for sourcing and effectiveness of current on premise sourcing solutions is largely dependent upon the integration of such solutions across enterprises. Typically these have come at a cost resulting in not all partners being able to leverage their functionality and thus causing gaps in collaboration. Typical example of these are gaps in consigned inventory, visibility of customer owned inventory and so on.

Cloud SaaS solutions enable cost effective approach to bridge these gaps. In addition the ability to use the large data collected in the sourcing value chain and process it with machine learning models enables the detection of trends that can then be compared with demand management leading to improved ability to react to changes and avoid costly situations. This is done by not only leveraging the Cloud SaaS Advanced Analytics solution to determine the trends but also enable efficient and timely collaboration within all partners in the Sourcing Value Chain through actionable information available anywhere (i.e. desktop, mobile).

Cloud SaaS Advanced Analytical solutions

not only improve ability to react to

changes in demand but also enable

efficient collaboration within

Sourcing Partners with actionable

information available on desktop and

mobile



The Technology advances in Cloud SaaS and Advanced Analytics based on Artificial Intelligence models combined with the developments in the internet of things and internet of everything space is making the fully automated factory of the future a possible reality today.

IOE-IOT sensors sensing inputs from inventory in multiple locations such as in-transit, consigned sites, supplier docks, receiving stores, supply carousels, distribution centers feeding into the manufacturing execution systems, operations planning systems running machine learning driven artificial intelligence algorithms can help manage optimized inventory levels.

Similarly taking sensor data from assembly lines and using machine learning to not only achieve finely tuned statistical process control (quality control) but also predict potential failures before they even occur thus eliminating potential for costly shut downs, rework and obtain significant productivity on the shop floor is now possible.

Similar to sourcing which focuses more on inbound logistics Cloud SaaS solutions enable cost effective approach to bridge the collaboration barrier between Manufacturing and Distribution. By collaboratively collecting data from value chain and processing it with machine learning models enables the detection of trends that links demand management, order management, order fulfillment to logistics carriers and distribution centers leading to improved ability to react to changes in value chain and avoid costly situations. One example of this is the ability of such solutions to greatly simplify Omni Channel distribution.

Cloud SaaS Advanced Analytical solutions are the basis of creating the Factories of the Future

today and Omni Channel Retail Supply Chains



Here are some key characteristics of transformational value chain solutions.

Almost everyone of the solutions that I have been involved in that have delivered innovation and transformational business value has taken the crawl, walk and run approach.

Innovation requires out of the box thinking and also challenging the norm and status quo. It is important to build credibility in the key stakeholders to remove skepticism that generally exists. It is important to also prove that the solution meets its stated goals. The best way to do this is to pick a important but tangible out-come to focus on as the initial scope of the innovative solution and focus on accomplishing this. This is the crawl part. Although we are crawling it is also very important to keep this phase relatively short term in nature. Example of this is when proving out the use of machine learning models for demand forecasting, it may make sense to pick one product family where issues have been observed with forecast accuracy or rapid demand changes occurring. Taking the results of this proof of concept phase lays the confidence building foundation to plan the transition to most of the products thus leading to walk and run phases.

Best practices on machine learning models recommends usage of proving the model locally first by using training data and then only moving it for the actual production usage. Taking the same earlier example use actual data from selected product families to train the machine learning algorithm, check the results of the demand forecast for target products in the product family with additional sample data, review the recommendations and then once proven leverage the model for rest of the entire data set

Innovative solutions require crawl, walk and run approach to

build credibility by taking the

important crawl step but in a very rapid timeframe. Best Practices

around machine learning adaptive

models involve training the

models locally and iteratively



Solutions should make it easy and economical to collaborate within various partners involved in the business process.

Most of the current solutions do not make it easy to collaborate within the various partners involved unless the partners are also using same or almost similar solutions or integrations with their associated costs are built between the solutions.

Cloud and SaaS solutions provide a easy way to get around this problem by providing a low cost approach to get access to selective information that has to be shared between partners. Simple functions like capturing or viewing data can be easily provided at very economical cost while maintaining security. Providing the ability to access, input and view information on any media (laptop, smart phones, iPads and similar mobile devices) and anywhere securely changes the way collaboration takes place. Solutions are evolving where a Supply Chain Operations controller is reviewing shortages of key products on exception bases and is able to within the same application either have an email, phone or chat conversation with all the players in the Supply Chain to manage this on business real time basis without having to leave their current user interface.

Exception based information delivery for Demand Supply balance is a fundamental best practice to avoid delays in responding to Supply Chain events that typically occur in today’s Supply Chains due to data analysis paralysis, multitude of analytical tools that do not match, visualizations that do not give business real-time data or solutions whose business rules are not able to keep up with the rapid changes in business scenarios in the Value Chain. The only way to handle these situations is by leveraging the analytical capabilities provided by Artificial Intelligence and Machine Learning based solutions.

Cloud SaaS solutions provide

the ability to access, input and view information

on any media securely thus significantly

changing the way Value Chain

partners collaborate with

each other.

This is further accentuated by exception based

analytical solutions

powered by machine learning

Example of Company who has achieved benefits Before Predictive Commerce they had significant operational issues due to low forecast accuracy as well as unexpected product requests from customers.• They went through crawl walk run approach of using machine learning

for improving E-commerce product recommendations• Followed this up by using machine learning to improve forecast accuracy

and supply chain operations• Results

ü 8% ROI improvement over 1 yearü Net Revenue uplift improved by 35% in 1 year ü Lost sales reduced by 30% in 1 yearü Forecast accuracy improved to 92% in 1 year

Current Solutions miss the mark on Cloud SaaS and Advanced Analytics

Leading Edge Innovative Solutions are now in play

The key statement that I encountered most of the times that I have talked with Supply Chain leaders, Business Operations Managers/Directors/Planners is that while most systems promise efficient planning in reality exception based planning management becomes a distant dream as they and their teams spend time mostly on managing multiple reports/dashboards, reconciliation of information instead of focusing on business actions that are needed. Even if this information comes in then it is usually not business real-time and late in the game.

Very few solutions are truly out there that can effectively provide this capability although it is my expectation such solutions will come up.

Some of these provide good solutions for point solutions like Anaplan for Demand Management and Financial Planning and Analysis, Apttus for Quoting.

We took a note of this and came up with our own Cloud and SaaS solution planninginabox.com that is designed from ground up to solve the above as well as facilitate the following:

Ability to easily integrate within existing architectures while managing security with multiple means of data input.

Ensure effective collaboration by enabling the user to collaborate via email, chat, call on either laptop, mobile devices while being in the same user interface.

Have pre-built adaptors to process IOE-IOT data , and have it drive the Advanced Analytics with leading edge visualizations.

Finally but not the least leverage artificial intelligence and machine learning for significant business transformation.


Review of Market Place Solutions

Traditional solutions provided by Oracle, SAP, IBM and highlighted by Analytical firms like Gartner, Forrester although are well established are constrained by the fact that they are predominantly architected on the on premise model and even though there is a push by these companies to provide the Cloud and SaaS based versions these and are characterized by following:

They need significant integrations to truly leverage information across multiple organizations.• Except for IBM which has its Watson product none of them have significantly demonstrated Artificial Learning and Machine Learning capabilities that can be leveraged to enhance Value Chain transformation.• Although many of them provide robust end to end capabilities within the enterprise looking at the current state of such solutions within the enterprise I have observed that most of the times it results in users struggling to get actionable information as a result of static business rules that are typically embedded in such solutions.

Some of the promising enterprise solutions such as Anaplan while eliminating most of the above issues still have gaps in functionalities like ability to easily provide processed information for use by other solutions that can implement machine learning capability on top of its data or have weaknesses in the solutions that help manage the back end of Supply Chain Management while they have mastered the solution for front end with their Demand Planning and Forecasting, Financial Planning and Analysis solution as an example.

Value chain Best Practices have developed over a long time and for most purposes can be characterized into the following key areas:Plan: - Forecasting, Collaboration and MetricsSource:- Sourcing Operations Make:- Lean and Agile Manufacturing, TechnologyDeliver:- External Collaboration, Delivery Scheduling, Forward and Reverse Logistics

Fundamentals of Value Chain Best Practices conceptually do not change, what changes is how technology advances can be leveraged to achieve the same.

Cloud SaaS solutions provide the ability to access, input and view information on any media securely thus significantly changing the way Value Chain partners collaborate with each other. This is further accentuated by exception based analytical solutions powered by machine learning. This provides an opportunity to drive significant business transformation in the Value Chain.

The need for this transition is crucial but it not easy. Many complex offerings have not yet made that transition (Oracle, SAP) and while many are emerging (Anaplan, Apttus) these do not cover all the gaps which has led Pluto7 to offer our own Cloud based subscription model of Supply Chain Analytics solution www.planninginabox.com which addresses some of the business pains highlighted in this paper.

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Summary

Following are the articles that have been referenced by the author when creating this content

• Top 10 Supply Chain Best Practices – Multiple articles, Author’s experience, Pluto7 team

experience

• Forecasting best practices by BetterVu

• Machine Learning - A Giant Leap for Supply Chain Forecasting –Patrick Smith in 2015

• Democratizing Data Science through Automation by Data Informed

• Application of machine learning techniques for supply chain demand forecasting – Article

by Real Carbonneau, Kevin Laframboise, Rustam Vahidov, Concordia University

• Ultimate guide to machine learning by Apttus

• The Anaplan Platform explained by Anaplan

References

Date post:	22-Jan-2018
Category:	Technology
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