Supply Chain Analytics: Out of obscurity, into new possibilities !

January 26, 2023by admin0

How about a wake-up call for organisations to motivate rethinking and redefining of the work they actually do? When challenged to redefine work, most of us who do it would share completely disparate perspectives. Ask a Demand Planner working with highly advanced technologies (demand sensing, sophisticated automation of forecasting, descriptive analytics and data lakes), for various consumer product organisations, and s/he will tell you why, when the chips are down, the most essential technology that s/he uses is Excel!

Research shows that 72% of planners depend principally on Excel and desktop analysis, in spite of the rollout of technologically-superior planning platforms in 92% of manufacturing units with over $5B revenue. One might question “Why?” The answer: One has to reassess work to enhance demand planners’ ability to model. All in all, the current architectures are built into back-office IT deployments with many layers of security, integration and batch processing. As a result, they provide business leaders with a record system for planning information, but they do not serve the primary user too well.

If one were to question a planner: Would NoSQL, Python and R provide an improved environment for modeling with a digital twin deployment? Their answer most probably would be that they have no idea what the terms mean.

Let’s take as an example: A company needs to formulate a supply chain strategy. After maybe a decade of focus on IT standardisation and building planning into tight integration with SAP ERP, they are underperforming on both margin and growth. This is despite a purchase of software worth $12-14M (including implementation). When asked about actually using all of this, the response might be: “We don’t use the solution as it is too black box. After implementation, we appointed no business or product owners, and our organisation saw multiple reorganisations. So the ownership and knowledge got lost somewhere along the way. The solution couldn’t be maintained much and due to network changes the error reduction level deteriorated. The Supply Chain Executives did not show much interest in the project, and consequently did not appreciate the value.”

Wow! Our view on this? We speak analytics but wrestle with embracing the potentialities of the Art of the Possible. The battle is twofold: helping workers to imagine the future using evolved analytics forms, and using technology to upgrade work. Most companies have an issue with alignment between business and IT. IT would be focused on standardization, while business is under pressure to use already-deployed technologies. Leadership support and understandings also pose challenges, and there exist issues of employee skill levels to understand the possibilities of new technologies.


Using Analytics to Mitigate Obscurity

Across the world, people are managing the supply chain through the Pandemic in phases, each of which is first-time. Based on scientists’ research and taking stock of Global Vaccination data, we may assume that we are about 40-50% of the way through the response to the Pandemic. One view could be that we are in the Third Phase.

Supply chain investments over the past ten years were focused on improving black-and-white processes: Order-to-cash and procure-to-pay. Any investments in transactional automation or ERP did not help at all, as the world became murkier with increased demand-supply variations.

Over the next few months, disruption and shortages would emerge. In the yet-to-come pandemic phases, organisations will discover that their current processes are not quite up to the challenges in a world of increasing obscurity.

Let’s take a look at the past to throw light on the future:

Phase 1. Lockdowns and Incredulity. Beginning of 2020-February 2021. We went into the pandemic with 21 more days of inventory than we had in 2007. As buying behavior patterns of consumers shifted, the focus lay on design and execution of the middle and last miles. During this period, 56% of global manufacturers felt they had performed well/ really well. However, basis industry and experience, 21% of manufacturers described struggling during the pandemic’s first phase. What was driving this difference? Descriptive analytics were being employed across functional teams.

Phase 2. Cautious Optimism and Reconnecting. March 2021-August 2021. Inventories dwindled. Labour issues materialized. Shipping issues mushroomed. And supply chain teams started to see that the only new normal was disruption. Organisations invested in improving visibility capabilities to enhance functional efficiency of manufacturing, supply and logistics. A few companies leveraged new tech to improve visibility for cross-functional decision-making across source, make and deliver. Most companies continued to invest by sticking to their in-house standardisation compliance directives.

Phase 3. Immense Disruption. September 2021-March 2022. Lack of supplier visibility and sensing will catch the supply chain unawares. While staring at gigantic shortages in building supplies, technology, retail and automotive, companies will need to rework supply chains and take analytics more seriously. They will realise that the investments of the past decade are unequal to business requirements, and hence relax IT standardisation decrees.

Phase 4. Reconstruction. March 2022 onwards. As organisation after organisation reports shortfalls in earnings, supply chains take on even greater significance. Business leaders compel CFO and IT organisations to drive innovation so as to speed up business results. Investments in visibility and supplier collaboration shoot up, and procurement innovators are obliged to design successful direct material networks. Consultants will hasten to reskill to build schema on read applications in NoSQL and rules-based ontologies, to drive learning systems. As they face unavoidable variability, it will become clear that simply applying machine learning to improve optimisation in yesterday’s applications just will not work.

Going Forward By Forging Ahead

Only 4% of manufacturing companies are innovation-driven, and just 17% are early adopters. The adoption curve is distorted, with 81% of manufacturers following the leaders.

Innovators now need to invest in creating cognitive computing systems that will sense market data and drive bi-directional orchestration. Starting with demand processes, map trends from the customer’s customer to the suppliers’ supplier. Focusing on bi-directional orchestration based on unified decision models will drive better outcomes.

Software robots will need adult supervision through learning systems. We should begin by identifying the business problem and then harnessing new approaches to drive business value. Through this voyage, we must realise that it usually requires multiple analytic techniques and innovation unavailable from traditional sources.

Moving forward will also require the learning of a new language. The gaps in familiarity of terms and techniques are big. We could take time to educate employees on use cases, while sponsoring pilots to explore new areas of analytics.

As the world becomes more and more obscure, with escalation in variability of all types, analytics offer promise. However, benefits cannot be realised without education, exploration and leadership.


All the best with your analytics voyage. Discover how your organisation can best employ analytics to meet the new normal. Call +91 8861778187 or write to us at /

Leave a Reply

Your email address will not be published. Required fields are marked *