2023 was a year full of challenges for supply chain leaders. Recovery from COVID-19, avoiding supply chain disruptions, mitigating geo-political risk and climate changes, and understanding the implications of ESG on the supply chain were some of the headwinds leaders had to navigate. Amidst all these challenges and the corresponding need for a resilient supply chain, all the buzz around GenAI and ChatGPT added to the complexity of the supply chain technology roadmap for vendors and clients. 

As we enter 2024, the supply chain industry continues to grapple with most of these challenges driven by regulatory requirements and the need for supply chain visibility, and this year will see strong demand for sustainability services and technology enabling resilience in the areas of planning, distribution, network optimization, and track and trace.

Here are the top five supply chain trends that may reshape the industry and vendors' offerings in 2024 and beyond:

Conversational & GenAI to improve user experience and drive efficiency

2023 saw vendors proactively investing in GenAI and conversational AI and developing POCs and MVPs, while clients have been looking to embrace these technologies with successful use cases within the supply chain. The existing use of technology within the supply chain revolves around self-service reporting and advanced analytics alongside platform and SaaS solutions coupled with RPA, AI, and ML-based algorithms and proprietary tools from service providers.

Supply chain leaders have been working on the digital transformation of the supply chain and end-to-end technology roadmap. Still, adopting new technology within the supply chain varies significantly depending on the client’s supply chain and technology maturity. The use of technology in areas such as planning, forecasting, advanced analytics, scenario modeling, and network optimization has been one of the most common themes across vendors and clients in the last couple of years. The use of GenAI and conversational AI is still at a very early stage. NelsonHall estimates that ~50% of supply chain-driven organizations are investing in GenAI or looking to invest in 2024 to improve the accuracy of forecasting and demand planning, and virtual assistants to improve user experience, create and process documents across the supply chain, order tracking, invoice management, and risk management.

Digital twin to see increased demand and adoption

A supply chain digital twin uses real-time data and snapshots to create a detailed simulation model and help with scenario planning and analysis. A supply chain digital twin can be an excellent tool for understanding the supply chain’s behavior, predicting bottlenecks and anomalies, and troubleshooting.

With a focus on improving planning, forecasting, and driving user experience, NelsonHall expects two-thirds of large organizations to adopt digital twins in the supply chain within two years. This can be particularly useful in industries with a complex supply chain, such as manufacturing, energy and utilities, oil and gas, consumer goods and durables, defense, etc. Some of the tangible benefits organizations can leverage from the use of digital twins include:

• Long-term supply chain planning and forecasting
• Sensitivity analysis to avoid disruptions
• Inventory optimization
• Improving supply chain resilience and agility.

As organizations embrace digitalization of the supply chain, improving supply chain visibility, real-time tracking and tracing, advanced analytics, and control tower operations, the cost of adopting digital twins and barriers to adopting them will reduce significantly. However, from a vendor perspective, digital twins require a strong hardware capability or partnership and a robust engineering capability. Buyers of supply chain services need to weigh up their vendors’ end-to-end capability adequately. 

Increased demand for touchless supply chain operations

Supply chain disruptions have become more real post-COVID, and the recent events have added to the complexity. Supply chain disruption challenges, along with evolving customer preferences and regulations, require leaders to act swiftly, thus making them shift to a more autonomous and agile supply chain. Various areas within the supply chain where touchless operations have been  adding value are:

• Touchless planning
• Touchless order management
• Touchless analytics and reporting.

Vendors such as Capgemini, Infosys, IBM, and other major players have touchless planning services and claim to deliver tangible benefits such as:

• Up to 80% reduction in operations cost
• Up to 5% increase in revenue base.

In today’s world, when organizations are grappling with an overload of systems, platforms, tools, processes, and technology,  along with complex business organizations, a touchless supply chain can immensely improve user experience. A touchless supply chain will also ensure resources are utilized for more strategic work and free up to 60-70% of their time from mundane and routine work. At the core of touchless supply chain operations is the technology network, including RPAs, machine learning models, and AI, that ensures standard processes can be automated and performed independently with minimal or no human intervention.

With adequate planning, the latest technology, and domain knowledge, vendors have successfully delivered touchless planning and other operations for their clients. NelsonHall expects touchless planning, order management, and analytics/reporting/forecasting to have higher demand in 2024 and widespread adoption to 70-80% in the next 18 months.

Enabling end-to-end supply chain visibility

2023 was a dynamic and challenging year, with the ongoing Russia-Ukraine war, Israel-Hamas war, Panama Canal crisis disrupting trade routes, and Houthi attacks in the Red Sea. Not much has changed as we enter 2024 with most of these crises ongoing. This certainly means a lot of planning, decision-making, increased cost pressure, transportation constraints, and other associated challenges that supply chain managers will have to work with. These geopolitical uncertainties have exponentially increased the complexity for supply chain managers, and the situation warrants quicker and faster decision-making.

In today’s complex global supply chains, decision-making without real-time, end-to-end visibility can be costly and counterproductive for businesses. Lack of visibility of real-time transactions, orders, shipment status, and inventory significantly impacts cost, customer satisfaction, and associated supply chain risk. Vendors have developed an ecosystem of solutions that integrates real-time data across systems, allowing business users to make faster decisions and access actionable insights and decisions. NelsonHall expects the demand for end-to-end supply chain visibility services to stay strong in the coming months and years, driven by the need for transformation, digitalization, and faster decision-making.

Avoiding supply chain disruptions in 2024

Given the current market situation, the possibility of a major supply chain disruption is not too far-fetched. Companies must assess their supply chain resilience and identify and react to gaps. This may be a longer-term plan; however, companies must respond to ever-evolving internal and external factors to address and adapt to business challenges.

This may require a proactive approach and an assessment of the current supply chain landscape. Creating supply chain visibility will be the first step towards understanding the strengths and weaknesses of the existing supply chain. Without adequate data and insights, organizations may be sitting on a ticking time bomb in the current market scenario. Visibility of the supply chain can help leadership with sufficient insights and data points to proactively identify challenges and plan accordingly. While 2024 may be challenging, using technology coupled with adequate monitoring, planning, and partnerships can help organizations avoid potential disruption.

From NelsonHall’s recent discussions with the major service providers, some of the themes for 2024 that emerged were that clients are showing maximum interest in improved forecasting and planning accuracy, leveraging digital tools and technology and AI for transformation, use of GenAI in the supply chain, and sustainability services.

NelsonHall recently had a briefing followed by a demo of Tech Mahindra’s latest offering in field service management: Yantr.ai, an AI and ML-based bolt-on solution that can sit on existing scheduling systems. Yantr.ai provides a control tower solution to field service management, focusing on delivering operational efficiency, enhanced productivity, and improved workflow control by leveraging advanced analytics, artificial intelligence, and machine learning models to enable real-time decision-making. With Yantr.ai at the center of all data points, it can provide end-to-end visibility of granular operations insights.

Launched in 2022, Yantr.ai is an offering under Tech Mahindra’s BPS business with BPaaS and SaaS options. Tech Mahindra has been focused on offering BPS solutions with a combination of platform enablement in niche areas as part of its growth strategy. NelsonHall expects to see further similar offerings from Tech Mahindra in the coming months.

Yantr.ai Overview

Yantr.ai focuses on industries such as telecom, utilities, retail, oil & gas, pharmaceutical, and other similar industries where there is a sizeable workforce in the field, and field service directly impacts the customer experience.

With today’s Amazon and Google-like experience, customer expectations evolve almost daily. However, while many innovation and technology interventions have happened, effective field service management remains a key challenge. Customers expect excellent product quality, effective communication, and seamless support; and field service management is vital to customer satisfaction.

Yantr.ai focuses on transforming field spend, improving CX, and improving field workforce productivity. It incorporates a digital twin and control tower for field services, and focuses on creating end-to-end visibility, data-driven decision-making, and scenario and capacity planning to provide actionable insights. Tech Mahindra claims to deliver benefits such as a 1-3% reduction in operational spend, up to 10% improvement in operational efficiency, and up to 15% improvement in customer experience.

Yantr.ai’s core modules are:

• Capacity planning: forecasts the demand trend by incorporating external factors such as weather and seasonality to empower planners to remain prepared and responsive
• Strategic planning: focuses on monthly and long-term (up to two years) demand optimization, identifying and addressing challenges such as unmet demands, skill shortages, demand reshaping, and permanent relocation
• Operational planning: focuses on demand optimization for the next 30 days, with daily demand, capacity planning, optimizations, and SLA visibility
• Jeopardy planning: offers real-time triage, jeopardy, and unscheduled work order management based on multiple dynamic factors and business rules
• Route planning and optimization: ensures all vehicles/trucks take optimized routes to save time and money, thus improving service efficiency
• Scenario modelling: simulation of distinct scenarios based on various economic and weather factors like wildfires, tornados, floods, etc.

Forecasting is supported by machine learning models covering ARIMA, exponential smoothing, Naïve, and neural network time series forecasting. Based on the data input and forecasting accuracy, Yantr.ai will dynamically select the best model for forecasting and plot different visualizations for actual demand, forecast demand, and variance between actual and forecast demand.

Client Cases

One example of the use of Yantr.ai for field service is for a major Australian telecom company where the benefits delivered by Tech Mahindra are:

• $15m to $20m savings to field operations spend
• ~4 % SLA improvement
• ~10 % to 15 % work being saved from external suppliers
• Truck roll reduced by 10%
• 40% FTE reduction.

The client’s business challenges were managing extensive field service operations, optimizing inefficient technician assignments, reducing response time, and improving customer satisfaction. The client also struggled with no real-time insights or dynamic scheduling capabilities. Tech Mahindra deployed Yantr.ai to optimize technician assignments, provide real-time job insights, and improve resource utilization. Dynamic scheduling capabilities were used to help the client adapt more quickly to unforeseen challenges.

Beyond field service, Yantr.ai has also been used by a major Australian media company to address route planning. In this case, the company’s challenges were improving customer satisfaction by meeting SLAs for newspaper/magazine distribution to retailers and end customers, and optimizing routing to help in vendor negotiation and cost savings. Tech Mahindra implemented Yantr.ai to build an optimal routing model to reduce cost and improve delivery SLAs. Key benefits delivered to the client were:

• 15%+ higher accuracy in route planning
• Improved SLAs and customer experience, along with reduced travel cost
• Ability to do scenario modeling/analysis, including demand changes
• Profitability analysis of routes and optimization of remote delivery points
• “Right location of depot” suggestions.

Yantr.ai also offers data ingestion through standard connectors/APIs and a custom data ingestion pipeline for ingesting from different formats, including CSV flat files. The tool has an orchestration system to manage complex workflows, which can be modified based on the client systems and processes’ requirements or updated as the project progresses and requirements evolve.

Tech Mahindra claims it is the first company to build a planning engine and control tower for enterprises in field services. The tool offers intelligent DSR (decision support recommendation), on-the-day risk mitigation, routing optimization, and other demand and capacity planning capabilities.

Implementation, Pricing & Key Benefits

Tech Mahindra offers modular functionality deployment options for Yantr.ai. The pricing mechanism is a one-time implementation cost and ongoing license costs based on the number of workflows, infrastructure deployed, and consulting support. In the BPaaS service, Tech Mahindra also provides managed end-to-end field service management support.

The typical time required to implement Yantr.ai is estimated to be 6 to 9 months for a mid-size organization.

Tech Mahindra categorizes the key benefits clients can get by deploying Yantr.ai with the 3Ps (people, planet, and profits):

• “People” benefits include an increase in workforce productivity, improvement in CSAT and NPS, and an increase in technician reskilling
• “Planet” benefits include reduced fuel consumption and carbon footprint and improved decision-making and situation planning during natural calamities or other emergencies.
• “Profits” include savings in operational spend, reduced field technician expenses, truck rolls, and workflow management-related expenses.

Tech Mahindra continues to invest in Yantr.ai, focusing on partnerships with ServiceNow, IFS, isMobile, and Celonis.

NelsonHall expects Tech Mahindra to leverage the technology and architecture behind Yantr.ai to increasingly deliver solutions beyond field services in areas such as logistics, fleet management, and demand planning. Tech Mahindra will likely focus on the telecom, utilities, and oil & gas sectors.

The pandemic stress-tested many supply chains beyond previous expectations, identifying and magnifying any process shortfalls. The current economic downturn and additional disruption of supply chains by geopolitical factors have further exacerbated the difficulties enterprises face in their day-to-day supply chain management.

Accordingly, enterprises are typically seeking increased digitalization and efficiency in their supply chains, greater supply chain agility, and much, much better supply chain visibility.

However, organizations are typically not self-sufficient in driving supply chain transformation and look for partners to assist them in this journey.

So, what should organizations look for in a supply chain transformation partner?

Ability to Identify End-to-End Digital Supply Chain Operating Model

Larger organizations will be looking not just for a service provider but for a trusted advisor who can proactively deliver innovation and best practices. This typically entails finding a supply chain vendor to work with them using design thinking to identify their new target operating model that can achieve the key business outcomes sought. Such vendors typically have innovation labs where they bring domain consultants and technology experts to co-create with client executives. As well as innovation expertise, the vendor should bring its own benchmarks to measure the client’s current performance and assist in establishing target KPIs within the to-be operating model and understand the best practice processes that are key to achieving these desired KPIs and business outcomes. This level of process knowledge typically comes from operational process expertise obtained through many years of running and enhancing client supply chain processes. Hence, it is important to choose a vendor with both consulting and operational supply chain expertise to reimagine and deliver supply chain transformation.

End-to-end supply chain expertise is also becoming increasingly important. Supply chain transformation requires an ability to break down existing silos and integrate data and automation across silos, so vendors need to be able to design and implement an end-to-end operating model reaching from demand identification and planning & supply planning through order fulfillment and transport planning & optimization, to warranty & returns operations. Indeed, manufacturing optimization could even be included as part of the end-to-end supply chain view for a more enterprise-wide perspective of the supply chain. This holistic perspective is necessary to eliminate friction between departments and automate transactional activities to the extent possible.

Ability to Establish the Data Framework

Data is an immensely important part of this perspective. The existence of supply chain silos within organizations has often resulted in multiple data sources that are fragmented and potentially inconsistent and out-of-date. In the new supply chain operating model, the vendor must incorporate real-time data quality and consistency into the design to provide a solid data analytics and visualization framework. Accordingly, the new operating model should have strong master data management holding a single version of the truth. This will typically require the vendor to undertake ERP standardization and optimization, including improving existing ERPs' controls and implementing a data lake. It will probably also involve improving the level of integration downstream with distributors and customers, upstream with raw material and component suppliers, and within the supply chain itself, e.g., with logistics firms.

It may even be appropriate to undertake cloud migration of existing ERPs.

Ability to Deploy Pre-Built Plug-and-Play Components

The vendor should provide a comprehensive integration platform, control towers, and dashboards within the new supply chain operating model. This integration platform will typically access a wide range of pre-developed plug-and-play models. These plug-and-play models will be based on both proprietary tools and platforms and have the ability to seamlessly integrate specialist point platforms for areas such as transportation planning and inventory management.

While they are likely to have preferred partners for many of the point solutions required, the vendor should have a broad alliance ecosystem with pre-built APIs and supply chain integration and build in longevity to the digital supply chain operating model by providing the ability to switch platforms in and out within its integration framework as new platforms and opportunities become available. These platforms, whether proprietary or third-party, will predominantly be cloud-based to provide greater supply chain scalability and resilience.

Ability to Apply Machine Learning in Support of Supply Chain Simulations

Enhanced demand forecasting is currently a key component of supply chain transformation. Depending on the type of business, the vendor should be able to integrate customer data from point-of-sale, social platforms, and third-party data sources, ingest data from the organization's systems and apply this data within pre-built machine learning models. Machine learning models should be used to improve forecasting accuracy and to run simulations, for example, to indicate the impact of marketing campaigns and price adjustments.

However, machine learning and analytics have a much wider role in underpinning simulations across the supply chain, including in supply forecasting and logistics optimization. In addition, digital twins are starting to be deployed to test and refine transformational approaches before their adoption, while process mining should be used to check process conformance and further opportunities for process automation.

Summary

In summary, leading supply chain vendors will possess:

• A combination of consulting, technology, and operations expertise in supply chain management supported by design thinking labs
• Best-practice supply chain solutions based on integrated combinations of process models, industry platforms, and automation technologies
• A supply chain integration platform and a pre-built portfolio of supply chain plug-and-play models for process automation
• A strong alliance ecosystem for access to best-in-class supply chain tools and platforms
• The ability to think end-to-end, breaking down supply chain silos and automating transactional activities across the supply chain
• Dedicated supply chain talent with specialized skills and capabilities
• Predictive and cognitive supply chain capabilities, including strong forecasting and digital twin capability.

Read Part 1 here.

Q&A Part 2

JW: What are the main supply chain flows that supply chain executives should look to address?

JJ: Traditionally, there are three main supply chain flows that benefit from automation:

• Physical flow (flow of goods from, e.g., from a DC to a retailer, the most visible and tangible flow) – some more obvious than others, such as parcels delivered to your door or raw materials arriving at a plant. To address these issues, the industry is getting ready (or is ready) to adopt drones, automated trucking, and automated guided vehicles (AGV). But to achieve true end-to-end physical delivery, major infrastructure and regulatory changes are yet to happen to fully unleash the potential of physical automation in this field. In the short-term, however, let’s not forget the critical paper flow associated with these flows of goods, such as a courier sending Bills of Lading to a given port on time for customs clearance and vessel departure, a procedure that often leads to unexpected delays
• Financial flow (flow of money) – here the industry is adopting new technologies to palliate common issues, e.g., interbanking communication in support of letters of credit
• Information flow (flow of information connecting systems and stakeholders alike and ensuring that relevant data is shared, ideally in real-time, between, e.g., a supplier, a manufacturer, and its end customers) – this is the information you share via email/spreadsheets or through a platform connecting you with your ecosystem partners. This flow is also a perfect candidate for automation, starting with a platform to break silos or for smaller transformation with tactical RPA deployments. More ambitious firms will also want to look into blockchain solutions to, for instance, transparently access information about their suppliers and ensure that they are compliant (directly connecting to the blockchain containing information provided by the certification institution such as ISO). While the need for drones and automated trucking/shipping is largely contingent on infrastructure changes, regulations, and incremental discoveries, the financial and information flows have reached a degree of maturity at scale that has already been generating significant quantifiable benefits for years.

JW: Can you give me examples of where Capgemini has deployed elements of an autonomous supply chain?

JJ: Capgemini has developed capabilities to help our clients not only design but also run their services following best-practice methodologies blending optimal competencies, location mix, and processes powered by intelligent automation, analytics, and world-renowned platforms. We have helped clients transform their processes, and we have run them from our centers of excellence/delivery centers to maximize productivity.

Two examples spring to mind:

Touchless planning for an international FMCG company:

Our client had maxed out their forecasting capabilities using standard ERP embedded forecasting modules. Capgemini leveraged our Demand Planning framework powered by intelligent automation and combined it with best-in-class machine learning platforms to increase the client’s forecasting accuracy and lower planning costs by over 25%, and this company is now moving to a touchless planning function.

Automated order validation and delivery note for an international chemical manufacturing company:

Our client was running fulfillment operations internally at a high operating cost and low productivity. Capgemini transformed the client’s operations and created a lean team in a cost-effective nearshore location. On top of this, we leveraged intelligent automation to create a touchless purchase/sales order to delivery note creation flow, checking that all required information is correct, and either raising exceptions or passing on the data further down the process to trigger the delivery of required goods.

JW: What are the key success factors for enterprises starting the journey to autonomous supply chains?

JJ: Moving to an autonomous supply chain is a major business and digital transformation, not a standalone technology play, and so corporate culture is highly important in terms of the enterprise being prepared to embrace significant change and disruption and to operate in an agile and dynamic manner.

To ensure business value, you also need a consistent and holistic methodology such as Capgemini’s Digital Global Enterprise Model, which combines Six Sigma-based optimization approaches with a five senses-driven automation model, a framework for the deployment of intelligent automation and analytics technology.

Also, a lot depends on the quality of the supply chain data. Enterprises need to get the data right and master their supply chain data because you can’t drive autonomy if the data is not readily available, up-to-date in real-time, consistent, and complete. Supply chain and logistics is not so much about moving physical goods; it's been about moving information for decades. A bit of automation here and there will not make your supply chain touchless and autonomous. It requires integration and consolidation first before you can aim for autonomy.

JW: And how should enterprises start to undertake the journey to autonomous supply chains?

JJ: The first step is to build the right level of skill and expertise within the supply chain personnel. Scaling too fast without considering the human factor will result in a massive mess and a dip in supply chain performance. Also, it is important to set a culture of continuous improvement and constant innovation, for example, by leveraging a digitally augmented workforce.

Secondly, the right approach is to make elements of the supply chain touchless. Autonomy will happen as a staged approach, not as a big bang. It’s a journey. Focus on high-impact areas first, enable quick wins, and start with prototyping. So, supply chain executives should identify those pockets of excellence that are close to being ready, or which can be made ready, to be made touchless, and where you can drive supply chain autonomy.

One approach to identifying the most appropriate initiatives is to plot them against two axes: the y-axis being the effort to get there and the x-axis being the impact that can be achieved. This will help identify pockets of value that can be addressed relatively quickly, harvesting some quick wins first. As you progress down this journey, further technologies may mature that allow you to address the last pieces of the puzzle and get to an extensively autonomous supply chain.

JW: Which technologies should supply chain executives be considering to underpin their autonomous supply chains in the future?

JJ: Beyond fundamental technologies such as RPA, machine learning has considerable potential to help, for example, in demand planning to increase accuracy, and in fulfillment to connect interaction and decision-making.

Technologies now exist that can, for example, both recognize and interpret the text in an email and automatically respond and send all the information required; for example, for order processing, populating orders automatically, with the order validated against inventory and with delivery prioritized according to corporate rules – and all this without human intervention. This can potentially be extended further with automated carrier bookings against rules. Of course, this largely applies to the “happy flows” at the moment, but there are also proven practices to increase the proportion of “happy orders”.

The level of autonomy in supply chain fulfillment can also be increased by using analytics to monitor supply chain fulfillment and predict potential exceptions and problems, then either automating mitigation or proposing next-best actions to supply chain decision-makers.

This is only the beginning, as AI and blockchain still have a long way to go to reach their potential. Companies that harness their power now and are prepared to scale will be the ones coming out on top.

JW: Thank you, Joerg. I’m sure our readers will find considerable food for thought here as they plan and undertake their journeys to autonomous supply chains.

Read Part 1 here.

Introduction

Supply chain management is an area currently facing considerable pressure and is a key target for transformation. NelsonHall research shows that less than a third of supply chain executives in major enterprises are highly satisfied with, for example, their demand forecasting accuracy and their logistics planning and optimization, and that the majority perceive there to be considerable scope to reduce the levels of manual touchpoints and hand-offs within their supply chain processes as they look to move to more autonomous supply chains.

Accordingly, NelsonHall research shows that 86% of supply chain executives consider the transformation of their supply chains over the next two years to be highly important. This typically involves a redesign of the supply chain to maximize available data sources to deliver more efficient workflow and goods handling, improving connectivity within the supply chain to enable more real-time decision-making, and improving the competitive edge with better decision-making tools, analytics, and data sources supporting optimized storage and transport services.

Key supply chain transformation characteristics critical for driving supply chain autonomy that are sought by the majority of supply chain executives include supply chain standardization, end-to-end visibility of supply chain performance, ability to predict, sense, and adjust in real-time, and closed-loop adaptive planning across functions.

At the KPI level, there are particularly high expectations of high demand forecasting accuracy, improved logistics planning and optimization, leading to higher levels of fulfillment reliability; and enhanced risk identification leading to operational cost and working capital reduction.

So, overall, supply chain executives are typically seeking a reduction in supply chain costs, more effective supply chain processes and organization, and improved service levels.

Q&A Part 1

JW: Joerg, to what extent do you see existing supply chains under pressure?

JJ: From a manufacturer looking for increased supply chain resilience and lower costs to a B2C end consumer obsessed with speed, visibility, and aftersales services, supply chains are now under great pressure to transform and adapt themselves to remain competitive in an increasingly demanding and volatile environment.

Supply chain pressure results from increasing levels of supply chain complexity, higher customer expectations, a more volatile environment (e.g., trade wars, Brexit), difficulty in managing costs, and lack of visibility. In particular, global trade has been in a constant state of exception since 2009, creating a need to increase supply chain resilience via increased agility and flexibility and, in sectors such as fast-moving consumer goods and even automotive, hyper-personalization can mean a lot size of one, starting from procurement all the way through production and fulfillment. At the same time, supply chains are no longer simple “chains” but have talent, financial, and physical flows all intertwined in a DNA-like spiral resulting in a (supply chain) ecosystem with high complexity. All this is often compounded by the low level of transparency caused by manual processes. In response, enterprises need to start the journey to autonomous supply chains. However, many supply chains are still not digitized, so there’s a lot of homework to be done before introducing digitalization and autonomous supply chains.

JW: What do you understand by the term “autonomous supply chain”?

JJ: The end game in an “autonomous supply chain” is a supply chain that operates without human intervention. Just imagine a parcel reaching your home, knowing it didn’t take any human intervention to fulfill your order? How much of this is fiction and how much reality?

Well, some of this certainly depends on major investments and changes to regulations in areas such as sending drones to deliver your parcels, flying over your neighborhood, or loading automated trucks crisscrossing the country with nobody behind the steering wheel; major steps in lowering costs and improving customer satisfaction can already be undertaken using current technologies. Recent surveys show that only a quarter of supply chain leaders perceive that they have reached a satisfactory automation level, leveraging the most innovative end-to-end solutions currently available.

JW: What benefits can companies expect from the implementation of an “autonomous supply chain”?

JJ: Our observations and experience link autonomous supply chains to:

• Lower costs – it is no surprise that supply chain automation already helps to lower costs (and will do even more so in the future), combining FTE savings and lower exception handling costs coupled with productivity and quality gains
• Improved customer satisfaction – as a customer you may ask, why should I care that the processes leading to the delivery of my products are “no touch”, that it required hardly any human intervention? Well, you will when your products are delivered faster, and that from order to delivery your experience was transparent and seamless, requiring no tedious phone calls to locate your product(s) or complains about delivery or invoicing errors!
• Increased revenue – as companies process more, faster, with fewer handling and processing errors along the way, they create added value for their customers and benefit from capacity gains that eventually affect their top line, particularly when operational savings are passed on to lower delivery/product prices, thus allowing for a healthy combination of margin and revenue increase.

We have seen that automation can do far more than simply cut costs and that there are many ways to implement automation at scale without relying on infrastructure/regulation changes (e.g., drones) – for example, by leveraging a digitally augmented workforce. Companies have been launching proofs of concept (POCs) but often struggle to reap the true benefits due to talent shortages, siloed processes, and a lack of a long-term holistic vision.

JW: What hurdles do organizations need to overcome to achieve an autonomous supply chain?

JJ: We have observed that companies often face the following hurdles when trying to create a more autonomous supply chain:

• Lack of visibility and transparency – due to 1) outdated process flows, and 2) siloed information systems often requiring email-based information exchange (back and forth non-standardized spreadsheets, flat files)
• Lack of agility (influencing/impacting the overall resilience of the supply chain) – the inability to execute on insights due to slow information velocity and stiffness in their processes, often focused on functions as opposed to value-added processes cutting across the organization
• Lack of the right talent – difficulty in finding talent in a very competitive industry with new technologies making typical supply chain profiles less relevant and new digital profiles often costly to train and hard to retain
• Lack of centralization and consolidation – leading to high costs, poor productivity, and disjointed technology landscapes, often unable to scale across the organization due to a lack of a holistic transformation approach and proper governance.

One thing that many companies have in common is a lack of ability to deploy automation solutions at scale, cost-effectively. Too often, these projects remain at a POC stage and are parked until a new POC (often technology-driven) comes along and yet again fails to scale properly due to high costs, lack of resources, and lack of strategic vision tied to business outcomes.

In Part 2 of the interview, Joerg Junghanns discusses the supply chain flows that benefit from automation, describes client case examples, and highlights the success factors, adoption approach, and key technologies behind autonomous supply chains.

SCM is one of Genpact’s “invest-to-grow” service lines, where the company is looking to make disproportionate investments and scale up the business: in this case, to become one of the top two global supply chain transformation services vendors. In its “invest-to-grow” businesses, Genpact is looking to achieve at least twice the level of revenue growth achieved by Genpact overall and to do this by investing in complementary competencies rather than scale.

Genpact identified Barkawi Management Consultants, part of the Barkawi Group, as a potential target by working alongside the company (from now on referred to as Barkawi) within in its client base. Discussions began in late 2017, with the deal expected to close this month, August 2018, once the regulatory processes are complete.

The acquisition of Barkawi provides a strong platform for Genpact to deepen its supply chain consulting practice, achieve a revenue balance in SCM between transformation consulting and managed services, strengthen its relationships and expertise in key supply chain technologies, and strengthen its presence in Europe.

Deepening Supply Chain Consulting Capability

In the area of SCM, Genpact had existing capability in planning & inventory optimization & demand analytics and a couple of large managed services contracts. However, the company had limited front-end consulting capability, with just 30 supply chain management consultants. Although Genpact was organically adding SCM consultants, this relative lack of front-end expertise was limiting its ability to handle a significant number of concurrent prospect conversations. The acquisition of Barkawi brings 180 SCM consultants to Genpact, enabling the company to have not only a greater number of simultaneous client and prospect interactions but also to have deeper and more end-to-end conversations across more SCM transformation dimensions (including operating model transformation, technology transformation, digital transformation, and customer-oriented transformation).

Prior to the acquisition, Barkawi had ~200 consultants, with the bulk of these (~180) in the U.S. (principally in a center in Atlanta) and Europe (principally in a center in Munich). These are the operations being acquired by Genpact. The remaining Barkawi personnel were based in the Middle-East and China, which are not markets where Genpact actively generates business, and these personnel will not be transferring to Genpact.

Barkawi principally employs two types of consultant:

• Management/process consultants active in supply chain and aftermarket services
• Digital/technology consultants where the larger part of the practice consisted of assessment/implementation/optimization projects around partner technologies such as Kinaxis and Anaplan.

The U.S. business was slightly larger than the European business and employed a majority of personnel active as technology consultants, while the European business employed a majority of its personnel in management/process consulting.

Achieving a Balance between Transformation Consulting & Managed Services

Barkawi will be combined with Genpact’s consultants into a single SCM consulting service line, giving a broadly balanced mix across management/process consulting and technology consulting. This global service line will be headed by Mike Landry, previously head of Barkawi Management Consultants’ U.S. entity, and will be organized into supply chain consulting, aftermarket consulting, and technology, with these horizontals matrixed against the following verticals: consumer products, life sciences, industrial machinery, and product manufacturing.

Genpact is aiming to achieve a rough balance between the Genpact specialisms of consumer products and life sciences and the Barkawi specialism in industrial manufacturing. Similarly, Genpact is aiming for a roughly equal revenue split between consulting and managed services, with the CPG sector having a higher proportion of managed services contracts.

Strengthening Supply Chain Technology Relationships

Another advantage of the Barkawi acquisition is that it brings Genpact strong existing relationships with, and expertise in, supply chain planning platform companies Kinaxis and Anaplan. Barkawi is one of the leading partners of Kinaxis, and the company’s partnership with Anaplan on supply chain complements that of Genpact's with Anaplan for EPM.

Strengthening European Presence

In terms of its client base, Genpact estimates that the majority of Barkawi’s clients in the U.S. (where it was typically selling ~$200K technology consulting projects), are prospects for a wider range of Genpact supply chain transformation services. In addition, Barkawi had a strong management/process consulting presence in major manufacturers in Germany, which Genpact will seek to build on.

In addition, while the bulk of Barkawi’s European personnel are in Germany, Genpact will look to extend this capability by growing its team in both Munich and across Europe to address supply chain consulting in the wider European market. Genpact perceives there to be major consulting opportunities within the leading manufacturing companies, assisting them in implementing and optimizing technology, working with data, and creating optimization models. This applies particularly to companies with a strong element of aftermarket services, where these companies need to optimize their aftermarket models and address aftermarket fulfilment, warranty management, and forecasting.

Overall, Genpact is still looking to grow the supply chain management consulting team further, will continue to recruit, to support these growth initiatives.