Big Opportunities in TinyML : Leveraging AI at the Edge in Supply Chains

by Kumar Singh, Research Director, Automation & Analytics, SAPinsider

What is TinyML ?

If you have not heard of the term “TinyML” before, you are not alone. The good news is that you are probably already aware of the underlying technology behind this terminology but were not aware of this marketing term that was assigned to it, sometime in late 2019. So let us first start by understanding what TinyML is all about, in the simplest way possible. There is however an assumption in the subsequent sections that you are aware of concepts like Internet of Things (IoT), edge devices and edge computing. I believe some of the most innovative applications of the power of TinyML are in the areas of operations and marketing and hence I will use examples from operations and supply chain throughout this article.

Let us start by exploring an example of how a “vanilla” IoT setup works today, in a manufacturing context. A key aspect that we need to keep in mind is that sensors capturing data on manufacturing floors is not new technology. The core setup has existed for decades. The supporting technologies have definitely evolved and sensors are becoming increasingly sophisticated and cheap but the gist is that sensors have been used to collect data on manufacturing floors much before even the term “Industry 4.0” came on the horizon. But in a vanilla setup, all that these sensors do is capture a set of data/data point and relay that data, in near real time, to a central server. So it is not difficult to infer that the true value of this type of setup is not in the hardware (sensor) functionality, but in how you leverage the data you are collecting. While that is not within the scope of this article, data captured from the floor can not only be used for analytics but this near time data relay is fundamental to creating a digital twin.

Now let us visit another example from manufacturing. A camera deployed in a production line is scanning finished parts coming out from the line. There is an “inference” program installed on this camera that can flag any parts that it believes do not align with the quality requirements/specifications. Notice the difference between this example and the previous one. In the previous example, all that the edege device (sensor) was doing was capturing a data point and relaying it back. It was not applying any form of intelligence to the data point it was capturing. In the second example, our edge device (camera) has a Machine Learning (ML) algorithm installed on it, which allows it to apply an inference algorithm on the captured data. This second example, is the example of TinyML. The letters “ML” in TinyML obviously stands for Machine Learning but why the word “Tiny” ?

As you may know, true Machine Learning algorithms are computionally intense. The training and deployment mostly happens on high powered machines. Let us revisit our manufacturing sensor example. Say we want to leverage the data being collected to implement predictive maintenance capabilities. The data collected will be leveraged to build a ML model, that will be trained on a machine with high computation specs, and will run on a proper system. The ML algorithm, in this case, sits on a device with powerful computing capabilities. Now if we come back to our camera example, it is not difficult to imagine (where we are currently in terms of technology),that as compared to a full fledged system, a camera on the edge is a low power device, and hence houses a ML algorithm that is not computationally intense, but is impactful.

So, with that background, we can formalize a simple definition of TinyML:

TinyML broadly encompasses the field of machine learning technologies capable of performing analytics on data captured by extremely low power edge devices, on those devices themselves.

Between advances in hardware & computing power, and the actively engaged open source tools based TinyML community (like TensorFlow Lite)  that has generated exponential innovation in machine learning on the edge, it is now possible to deploy increasingly complex algorithms,  directly on edge devices. The possibilities will only expand from this point so now is the right time to start exploring the opportunities to leverage TinyML capabilities in your operations infrastructure in detail.

TinyML can revolutionize supply chain visibility beyond manufacturing

TinyML has made good in-roads in manufacturing. While in one of the examples that I used in the previous section the sensor was only capturing and relaying the data, sensors today are very much capable of  applying predictive maintenance algorithms on the edge. You can magnetically attach sensors these days to the outside of the turbine and it will analyze detailed data at the edge. These sensors can alert regarding potential issues even before they occur, by analyzing the data they capture. The key areas that still need to be explored deeper are leveraging TinyML for logistics and inventory management. Let us visit few areas where TinyML, if designed and leveraged prudently, can create innovative capabilities.

Warehousing : Opportunities to leverage TinyML in warehousing range from monitoring dock bottlenecks to identifying safety issues in the warehouse. The best part is that you can build these capabilities incrementally and then integrate them in a platform, to create a “Smart Warehouse” platform, powered by TinyML. Many aspects that are currently housed across multiple systems, can be centralized in this type of platform, and combined with TinyML powered edge devices, can help control warehouse applications much more efficiently. While relatively simple analytics happens on the edge devices leveraging TinyML, the central algorithm takes care of the co-ordination and centralized planning, plus strategic planning.

Inventory Management : While smart edge devices that monitor and flag storage conditions are already being used on shop floors of many warehouses, opportunities to expand usage of TinyML are plenty. When you think about possible applications of  any technology, a good starting point is what are some of the challenges those on the shop floors run into. As an example, no matter how sophisticated a warehouse technology is, misplacement of inventory within the four walls of a warehouse is a frequent phenomenon. TinyML powered edge devices can alert putaway personnel in real time if a product is being placed in the wrong aisle or within a wrong bay in the aisle. This is the simplest of applications and it is not difficult. to imagine that solving more complex problems using TinyML will yield more significant benefits, with some having the capability to transform inventory management in warehouses.

Transportation : Transportation has been an early adopter as far as digital tracking goes. Telematic tracking devices have been in use for a long time in transportation fleets and these have now been augmented with more advanced sensors and trackers. But like inventory management, there are many unexplored opportunities. We know that pattern recognition and incident detection are among the capabilities ML algorithms can help build and these can be leveraged in TinyML powered edge devices, powered and supported by a central algorithm sitting on a central server. As an example, TinyML powered edge devices, in tandem with a central algorithm, can help study traffic patterns to optimize and plan routes based on peak traffic hours, maintenance, and construction, thereby helping develop dynamic routing capabilities that do not exist today (or maybe exist in marketing white papers only).

What does this mean for SAPinsiders ?

  • TinyML is not a new concept, the marketing term associated with the technology is relatively new. SAP ecosystem has partners that have helped clients leverage SAP technology portfolio and open source tools like TensorFlow Lite to build TinyML based solutions. Opportunites to leverage this galore in both operations and marketing.
  • There is a fundamental foundation that needs to be built before embarking on building this type of advanced analytics solution. Setting up that foundation in terms of people, processes and technology is critical. Think about it this way: You can’t make a leap from having BI capabilities to building a solution like this. While you may end up implementing the technology, your people and processes will not be able to support the solution and the entire initiative may turn into a failure.
  • Plan an incremental roadmap, based on where you are in terms of your current state, in functions mentioned above. While external partners can definitely provide support, your people are the ones who are closest to the process. Start leveraging their expertise much earlier in the planning process.
  • Stay tuned for our “Modernizing Inventory and Logistics Tracking” research report coming out in July 2021. That will provide you useful insights on the impact IoT is making on the world of digital tracking in supply chain operations. The report also explores other digital tracking approaches that SAPinsiders are exploring, like RFID technology.



Kumar Singh, Research Director, Automation & Analytics, SAPinsider, can be reached at



A Corporate Rivalry Strategy Lesson from the Cold War Era

By Kumar Singh, Research Director, Automation and Analytics, SAPinsider


A lesson from the collapse of U.S.S.R

Among many other factors that led to the collapse of former Soviet Union, a key one in my perspective was the economic strain on the erstwhile Soviet Union’s economy. Regan, very strategically, led U.S.S.R into the game of matching nuclear arsenal and programs like star wars. The then U.S.S.R did not have the economic foundation to sustain that.

But the key question is – what was the end objective that U.S.S.R had in mind when it took those not so prudent steps ? The simple objective was that it wanted to match the capabilities of its rival. Now there were two aspects to it in my opinion. One was, that it actually was anxious about U.S developing an edge, in terms of military capabilitis. Second aspect was, it did not want the world, and its allies, to see it as lagging behind U.S in military capabilities.

And to address both of these, it did not need to actually spend like crazy. Military might is not always about the number of WMDs. It can also be about how many you have, where you have them, what is your tactical execution plan (we hope it never comes to that again) when the time  comes to execute. If the goal eventually was that the capability has to be enough for MAD (Mutually Assured Destruction), U.S.S.R could have done it with very little stockpile. And the same goes for “Nuclear Deterrent” aspect. For U.S, it did not matter if U.S.S.R had equal or more warheads. What was critical was if the damage they could do would be substantial.

That maks me think that U.S.S.R trying to match the exact number was more cosmetic. They being the leader of the “other world block”, did not want to be seen as lagging. Now, this is not the aspect we are going to explore here, but  my take on this is that they could have created this “mirage” without actually spending, by clever “marketing”.

But our lesson is in the first part- that U.S.S.R did not need to match U.S, warehead for warehead, if the end objective was to have a nuclear deterrent and MAD. The end goal was to outcompete. It could have been achieved without matching U.S warhead for warhead, or program for program. And herein lies a corporate lesson. When two corporate giants compete, an important strategy is accquisitions. But that strategy need not be focussed on “We need to match their portfolio or capabilities, product by product”.

Example: Salesforce vs Microsoft

The theme of this article originated when I was doing my weekend reading this morning. I came across an old article about Salesforce’s accquisition of Slack and how it “matches” a product in Microsoft’s portfolio, MS Teams. The amount that Salesforce paid for Slack was steep. And that made me think- rather than investing that amount to “match” a capability that Microsoft had, could Salesforce have used that to make certain accquisitions that could have actually helped it build capabilities Microsoft did not have ? Agreed that messaging platform is a required component in a portfolio but is it a strategic component ? With that money, Salesforce could have bought a couple of companies that come to my mind, which, when combined with their existing products (and Tableau), would have helped build a unique “solution” offering. The “matching” game, in my opinion, specifically due to the price paid to accquire Slack, was not prudent. Salesforce could have developed a messaging product, for the purpose it needed to serve, at a fraction of that.

But the earlier accquisition of Tableau was a masterstroke. I believe Salesforce can use that product to do many amazing things, and combined with some addition product development and few strategic accquisitions, it can build new market segments as well. The fact is, Tableau needs to be the central element of their strategy in this rivalry. Tableau is seen as a visualization tool or a BI tool by many. I see tools like Tableau having much more potential with some additional development and integrations.

The difference with U.S.S.R analogy however is that in the corporate world, the goal is not to match, but to develop an edge. Size, in today’s digital world, does not matter, if you can generate unique solutions and capabilities. I always insist that when you want to compete with companies larger and stronger than you, the goal should not be to compete on their turf. Build your own turf and draw them in.

Now looking at this from Microsoft’s perspective, it sits on much more cash than Salesforce. There are few key investments it can make to thwart any attempts by Salesforce to create additional turfs where MS does not have expertise. That is always a better step since once the turf (new capability, practice or solution area) has been developed, you then get drawn into the “matching” game.

The key is – avoid the “matching” or “catch-up” game.

I am pretty sure that much smarter folks at both these companies already have much smarter plans than those suggested in this article. From the perspective of SAPinsiders community, these rivalries are always benefitial to the end user community, as it means better, innovative and unique products will be available for them, which will help them run their organizations more efficiently and strategically.


Kumar Singh is a Research Director with SAPinsider and can be reached on

Understanding the difference between Business Intelligence (BI) and Artificial Intelligence (AI)

In the world of technology, it will be an impossible task these days to find someone who is not aware of the term data science.

With the emergence of data science, Artificial Intelligence (AI) and Machine Learning (ML) tools appreared on the horizon. The world was introduced to the significant impact AI and ML tools could make to the way businesses plan and operate.

Among all this hype, BI, which was the poster child of the world of analytics till then, started to lose its glamour.

However, the fact is, descriptive analytics is one of the foundational capabilities and is in no way inferior to other type of tools and algorithms. The fact is, an organization strategically needs to define what type of analytics it will be leveraging across its functions and processes.

Eventually, you will be leveraging a portfolio of tools. And for some aspects, descriptive analytics is all that you need and anything more than that may be an overkill.

This content is available to SAPinsiders. Please click to create an account or log in.

Login Create An Account

Leveraging Machine Learning for Demand Forecasting

Until recently, conventional time series forecasting methods have been predominantly used for forecasting in demand planning. A majority of demand forecasting tools in the market leverage these methods in their solutions. With advances in technology and computing power, the sophistication of these time series algorithms has increased thereby increasing forecast accuracy.

However, with the advent of machine learning (ML) tools and increased interest in exploring them to improve forecasting approaches and methods, many vendors are increasingly incorporating ML-based forecasting methods in their tools. A key aspect to remember is that most of the frequently used ML algorithms have been around for decades now. Technology and computing power today allow us to leverage them easily in a variety of ways and applications. This article shares some Machine Learning approches that are being used by SAPinsiders.

This content is available to SAPinsiders. Please click to create an account or log in.

Login Create An Account

Sentiment Analytics for Marketing Managers : A Primer

As the world of marketing starts getting dominated more and more by social media, there can be several examples of instances where opinions or “sentiments” expressed by customers online can have a significant impact on an organizations brand. This influence of sentiments expressed by customers through words is not only important from sales perspective, but also from a broader marketing and product strategy perspective. New product introductions or new feature introductions are examples of additional areas where insights captured from sentiments expressed by customers on social media can have transformative benefits. This article introduces non-technical marketing managers to the concept of sentiment analysis so that they can leverage this powerful tool across their marketing portfolio.

This content is available to SAPinsiders. Please click to create an account or log in.

Login Create An Account

The Art and Science of Customer Lifetime Value (CLV) Analytics

As organizations become more and more customer focussed, developing marketing strategies that help address the needs of demanding customers is becoming more and more complex. While there are several aspects that go into formulating a winning marketing strategy, common sense dictates that one parameter of success is the money you will make from a specific marketing campaign. And no other metric helps you capture that better than the Customer Lifetime Value (CLV). In this article, we will explore the basics of this metric but specifically deep dive into how you can combine data science and strategy to fine tune this metric better.

This content is available to SAPinsiders. Please click to create an account or log in.

Login Create An Account

Leveraging Advanced Analytics to Create Smart Factories

by Kumar Singh, Research Director, Data & Analytics, SAPinsider


Harnessing the true value of Industry 4.0

The promises that Industry 4.0 foundational technologies bring to the world of supply chain and manufacturing are many and are attractive. Smart factories and smart manufacturing processes will be an integral component of almost all Industry 4.0 networks. Embedded seamlessly in an Industry 4.0 network, these smart factories will not only help realize optimal, flexible, and agile manufacturing within new deployments, they will also share important data points with the overall network. This seamless exchange of information will ensure that the entire network leverages the full value of having Industry 4.0 capabilities in place.

The journey to smart manufacturing revolution

Building smart manufacturing capability is not easy though. It is a journey that needs to be carefully planned and orchestrated across a diverse set of data inputs like people, processes and technology. Each of these elements need to be in complete sync with each other to realize the vision of a true smart factory. As mentioned previously, a true smart factory will not only completely transform the manufacturing paradigms of an organization but also transform related functions like marketing, engineering, and operations.

One very critical aspect of starting your journey towards building smart factory capability is to understand what exactly that capability means. Often, manufacturing automation and smart manufacturing are used interchangeably by organizations when highlighting these capabilities. However, it is critical to understand that automation, while it may lay the foundation, is not smart manufacturing. Then there is another notion that smart manufacturing is about getting near real time visibility of your manufacturing operations. Again, while this may be an essential component of developing smart manufacturing capability, on its own, it is not an indicator of smart manufacturing capabilities. Smart factories are combination of many aspects like automation, near real time visibility, and most important of them all, inferring something useful out of that data,  aka analytics. SAPinsider recently had the opportunity to sit with Intel’s Industrial Edge Insights Director, Bridget Martin, to discuss the key aspects of smart manufacturing platforms and how data science plays a key role in developing smart manufacturing capabilities.

The real value is in the data

The value of insights generated from data is not new in the world of manufacturing. Best of breed companies have been leveraging analytics on data generated and captured by their manufacturing operations to minimize manufacturing related risks, improve manufacturing efficiency, reduce manufacturing waste, improve product quality, and at a strategic level, make better business decisions. However, a key aspect of these analytics approaches in the past was that they were performed on latent data and hence, many actions that companies took based on the insights generated, were reactive. As businesses evolve rapidly in today’s digital age, organizations can no longer afford latency. Consider the example of product quality. If a product quality issue goes undetected and the product gets in the hands of customer before the data driven insights identify the issues, the impact on brand and customer loyalty can be huge. In the “Amazon age”, customers are extremely demanding, and products are getting commoditized so fast that it may not take much for your customers to switch to your competitors. And this is where the need for near real time monitoring and analytics of data generated by manufacturing processes comes into prominence. What organizations need are solutions that can not only help them get near time visibility into their data, but also be able to leverage the data to transform processes and business models.

As Bridget Martin, states: “There is a need for ready-to-deploy reference software platforms designed for near realtime, minimal latency video and time series data analytics that enable factory owners to automate and advance their operations—without replacing existing machinery, production lines or processes (“plug and perform”). Factory upgrades can be delivered via software updates that factory owners can manage remotely and deploy automatically, potentially reducing costs, saving time, and expanding capabilities more easily.” Organizations realize this need, and so do the solution providers. More and more platforms are emerging in the market that promise to help companies realize their smart manufacturing vision. But one aspect that is obvious is that not all of them are the same in terms of capabilities. And the most significant capability that differentiates these solutions is the “smartness” that the solution contains, which is in the form of algorithms built into these solutions.

Smart manufacturing platforms fueled by algorithms

As mentioned above, an ideal platform should not only aid in automation but should help take the capabilities significantly further, by embedding algorithms in the platform that organizations can use to gain insights, optimize processes, and even develop new capabilities, like smart quality management programs. These algorithms can range from conventional optimization algorithms to advanced deep learning algorithms. Since these platforms cover end to end manufacturing processes, the portfolio of algorithms available in these platforms need to be diverse as well.

Bridget highlighted this in her quote: “The ideal software reference design should include sample algorithms for various use cases or easily plug in third-party/open source developed algorithms, and even enable customers to develop their own algorithms if needed with its built-in training and learning tools. Designed exclusively for manufacturing environments, the platform should have unlimited manufacturing use cases—whether discrete process applications like electronics or auto manufacturing or process automation applications in the Oil and Gas sector.”

Interoperability adds to the complexity of Industry 4.0 world. To develop a true Industry 4.0 network, platforms need to “talk” to each other and hence this is a feature that is very critical. Bridget quoted some example features like: “Capability to push and publish AI analysis to local applications or the cloud. Should have containerized microservices which are easy to modify and customize for a factory owner’s unique applications. It should be easily adapted, extended, and scaled across operations.”

Transformation example: smart quality management at Audi

During the discussion, Bridget highlighted an example of the transformative capabilities of a smart manufacturing platform. The example was around how Audi was able to leverage a smart manufacturing platform to develop a smart quality management process. “At Audi, an individual car has a significantly large number of welds. Using a smart manufacturing platform, Audi was able to automate and expand its quality inspection processes to inspect 100% of welds in the factory and more efficiently, with an estimated 30-50% immediate reduction in labor costs. For welds outside the quality guardrails, Audi can easily tell where they are in the factory and act more quickly to address them. “

What does this mean for SAPinsiders?

The road to building smart factories is not straightforward. Careful planning and strategic selection of tools and external partners will be critical to developing this capability which most manufacturing organizations are aiming to build to successfully compete in the digital age. Some aspects that you need to be cognizant of are:

  • Evaluate your foundations. At the core of smart manufacturing capability are four key ingredients-Connectivity, automation, visibility, and analytics. These four key ingredients must come together, building the foundation so it is imperative that you evaluate your current state in these areas, to understand where you stand, what is the delta and what needs to be done to cover that delta.
  • Invest in your people and business processes. People and processes are as important as technology in any initiative and more so when you want to build any new capability enabled by technology. While it may not always be required, sometimes you may need to redesign your processes to ensure that you will be able to leverage the full value of any smart manufacturing platform. Human machine collaboration is going to be a critical aspect of Industry 4.0 networks, so you need to ensure that your manufacturing talent has the skills to leverage the fill value from these platforms.
  • Take time to evaluate solutions. The smart manufacturing platform that you leverage will be one of the central components of your smart manufacturing capability to you must put together a comprehensive evaluation criterion in order to select an optimal solution.


Kumar Singh, Research Director, Data & Analytics, SAPinsider, can be reached at

About Intel® Edge Insights for Industrial (Intel® EII)

Intel® Edge Insights for Industrial (Intel® EII) is a pre-validated, ready-to-deploy reference software designed for near realtime, minimal latency video and time series data analytics that enables factory owners to automate and advance their operations—without replacing existing machinery, production lines or processes. For more information, visit Intel Edge Insights for Industrial

The Importance of End-to-End Planning in Modern Supply Chains

by Kumar Singh, Research Director, SAPinsider   As supply chains become more global, complex, and interconnected, they become increasingly difficult to manage and plan. Fortunately, technology now provides us solutions that can help us tame many of these complexities. A key category of imperative supply chain solutions is end-to-end-supply chain planning solutions. These solutions plan…...

This content is available to SAPinsiders. Please click to create an account or log in.

Login Create An Account

Is a Resilient Supply Chain Enough ?

by Kumar Singh, Research Director, Data & Analytics, SAPinsider



What is needed for today’s VUCA (volatile, uncertain, complex, and ambiguious) world is a supply chain that is both resilient and agile

It is an indisputable fact that supply chains across the world were significantly disrupted by the pandemic. The interruption was across industries, magnified due to the global footprint of supply chains today. It was the chaos from this disruption that spilled into the day to day lives of people across the world, making them cognizant about the importance of supply chains. Among all the chaos due to these disruptions, we repeatedly heard the cry for building resiliency with our supply chains.

Is building a resilient supply chain enough?

Resilience in a business context is not a new concept. An analysis of businesses that have survived and thrived during peaks and troughs will tell you that the ability of an organization to successfully confront the unforeseen has always been a core element of success. If we formalize the definition of resiliency in supply chain context, it refers to the ability of a supply chain to bounce back when hit by an unforeseen event.

The threat scenarios that supply chains face today are greater than ever, which has led to the concept of building resilience within supply chain becoming more critical. As supply chain leaders work to understand what exactly they need to do, to build resilient supply chains, there is one question that they need to ponder upon – is resiliency itself enough to handle supply chain disruptions? SAPinsider recently had an opportunity to discuss this topic with solution experts and thought leaders from Reveal supply chain solutions and get their perspectives on why supply chain leaders need to think beyond resiliency.

Defining supply chain resiliency

At a more granular level, Resiliency” is characterized by the following key attributes:

  • Ability to resist/survive disruptions
  • Ability to return to original form/state post disruption
  • Establish multiple lines of supply to build redundancy

Prof. Yossi Sheffi, thought leader in supply chain resiliency and a professor at MIT, quotes the following in his book “The Resilient Enterprise”: “Supply chain resilience no longer implies merely the ability to manage risk. It now assumes that the ability to manage risk means being better positioned than competitors to deal with—and even gain advantage from—disruptions.” Many best-in-class organizations have invested in building resilient supply chains during the last two decades but have still run into supply chain challenges. This poses a key question pertaining to supply chain resilience- Is merely building the ability to manage and respond to supply chains risks and disruptions enough?

This sentiment is shared by experts, practitioners and thought leaders in the field as well. Martin Rowan, managing partner at Reveal expresses similar sentiments when he states: “Resiliency makes for a “good” supply chain however it can be very costly to have redundancy build in the processes and inventory to ensure disruptions have minimal affect.  Efficiency allows for the business to run lean and keep just in time and just enough supply for normal day-to-day operations.  However, agility turns your operations into a “great” supply chain when the disruption occurs, we can act fast and with determination to be able to adjust, correct and respond to constant course corrections.”

Agility is the secret sauce.

If resiliency is not enough, what additional aspects do organizations need to embed in their supply chains to align with today’s reality and competitive landscape? That missing ingredient is agility. Agility in the supply chain context means adapting to the situation rapidly and making constant course corrections to meet the changing conditions.  An agile supply chain is defined as a supply chain that can respond quickly to sudden changes in demand and supply. They handle unexpected external disruptions smoothly and efficiently. And they recover promptly from shocks such as natural disasters and epidemics. Prof. Hau Lee, professor of supply chain at Stanford university and the author of the HBR article “The Triple-A Supply Chain” defines the following criteria for building agile supply chains:

  • Continuously provide supply chain partners with data on changes in supply and demand so they can respond promptly
  • Collaborate with suppliers and customers to redesign processes, components, and products in ways that give you a head start over rivals
  • Finish products only when you have accurate information on customer preferences
  • Keep a small inventory of inexpensive, non-bulky product components to prevent manufacturing delays

Ed Elsbury, Associate Partner at Reveal shares his take on the importance of agility: “What we need to strive for in an agile supply chain is what the definition of agility suggests- “The ability to understand quickly, then move fast and easily…”.  This means we have the systems and data in place with the right human capital to make small and quick adjustments, course correct to meet different outcomes while failing and succeeding fast.  When organizations raise their level of Business Maturity, they can utilize their existing technology (SAP) to meet the agility need.”

The need for both agile and responsive supply chains

What is needed for today’s VUCA (volatile, uncertain, complex, and ambiguous) world is a supply chain that is both resilient and agile. And the capability of having seamless visibility into your end-to-end supply chain, combined with the ability to plan and respond quickly will be the key to develop a hybrid (resilient and agile) supply chain. Indeed, technology plays an important role in this. As an example, the key aspect of visibility that builds the foundation of a responsive and agile supply chain, will be driven by technology.

And this is where supply chain visibility platforms come into picture. An ideal supply chain visibility platform would go beyond visibility and will also have powerful analytics capabilities embedded within. These analytics capabilities are critical since what you need is not only visibility but also insights that can help you make critical decisions on time. This is made possible by increased insights into critical decision factors and key performance indicator factors.

But people and processes will play their role as well. As Sean Elliffe, Senior Partner at Reveal points out: “In SAP terms, it means you have the functionality in place, the trust in the data, the people and knowledge to act fast to make the relevant changes to the master data that will dictate to the materials how to perform in a crisis (or pandemic).  Imagine a place, where you can see demand increase or decrease in a particular product, and in an instant change the demand signals, the master data (i.e. business rules) to increase (or decrease) supply.  In an instant the supply chain adjusts.  It requires a single source of the truth, trust in the data and an organization that responds to the new system recommendations. That is agility. That is maximizing your SAP investment to its fullest.”

Last, but not the least is having the right external partner to help build these capabilities. From SAP ecosystem context, it is important to have a partner that not only helps you understand how to put your SAP investment to good use by helping you educate on supply chain planning nuances but also offers solutions built around their deep expertise, customized for SAP landscape.

What does this mean for SAPinsiders?

The capability of having seamless visibility into your end-to-end supply chain, combined with the ability to plan and respond quickly will be the key to develop a hybrid (responsive and agile) supply chain. Here are some key considerations:

  • Map your supply chain. A crucial first step is to understand your end-to-end supply chain. Make sure you have a current repository of data around your existing supply chain that will contain documents like process maps, standard operating procedures, system schemas, standard configurations for planning systems etc.
  • Invest in your people and business processes. People and processes are as important as technology in any initiative and more so when you want to build a resilient and agile supply chain. Build a foundation by making sure that the processes you want visibility into are best in class. Map your business processes and re-engineer them when required. Force fitting solutions on archaic processes will not yield the transformational results you desire. You must ensure that those who are working on implementing solutions as well as those who will interface with these solutions in the future, understand the technology well.
  • Redesign your supply chain when required. Your supply chain design is a key element of building resiliency and agility. Invest in a comprehensive supply chain network design and optimization initiative and make changes to your network if required. The changes may be needed in many areas, ranging from tactical (shop floor layout) to strategic (network footprint).
  • Invest in an end-to-end supply chain visibility tool. Take your time to understand what type of visibility solution you need, based on your unique business nuances, and develop a standard methodology to evaluate candidate solutions. Make sure you incorporate inputs from teams across your end-to-end supply chain to ensure that the solution will serve its true purpose.



Kumar Singh is a Research Director for Data & Analytics with SAPinsider and can be reached at


ABOUT Reveal

As an international advisory firm, Reveal helps Supply Chains fulfill their promises to deliver the right products, at the right place, at the right time. They use their experience, products, and proven methods to empower organizations to use powerful capabilities already built in SAP and gain visibility and insights into their end-to-end supply chain.