Supply chain modernization – what to consider in planning for the future

Previously we argued that, given the world’s current turbulence, many businesses have few alternatives but to modernize their supply chains (SC) in preparation for the future. However, what exactly does modernization entail? What are the common themes for SC modernization? How do businesses in different industries approach these themes? What cloud solutions are useful? This blog post provides an overview of what kind of SC challenges AWS customers wrestle with, what our customers have in mind when they talk about SC reinvention, what SC areas are most commonly affected, and what technologies are likely to play a role.

This includes the most common benefits of SC modernization, which include reduced operating expense (OPEX) and minimized operational losses using more efficient decision-making, improved customer service, agility to adapt to changing business conditions, cost reduction and business scalability due to cloud adoption, and faster roll-out of new analytics, such as machine learning (ML). Finally, we also provide our viewpoint on a journey towards digital SCs of the future.

Supply chain modernization – a web of interlinked objectives, focus areas, and technologies

AWS customers present us with a wide range of their current SC challenges, tactical objectives, and strategic aspirations. Recent economic and geopolitical events and growing energy prices exacerbated the need for SC resiliency, efficiency, and effectiveness. Often, our customers consolidate such requirements under the SC modernization umbrella, which sometimes manifests itself as reinvention, digitalization, transformation, innovation, optimization, and overhaul. While the language may differ and the list of synonyms is long, the underlying ambitions have certain commonalities. We are also starting to see interconnectivity between customer asks for modernization, the most prominent SC functions to optimize, and technologies our customers are seeing as the most promising for their objectives.

In order to better understand connections between modernization objectives our customers are trying to achieve (i.e., why?), what SC areas they are aiming to modernize (i.e., what?), and what technological solutions our customers have in mind (i.e., how?), we mapped customer requests from the past two years to these criteria. The objective of the analysis was to assess the gravitational pull between the three criteria; we created a circular infographic below to show the most critical interlinks between them. The most prominent interlinks are 7 percent of customers mentioning forecasting and predictions and, 4 percent considering AIML in demand planning and inventory optimization context, 6 percent of customers considering cloud migrations for logistics and transportation systems. When it comes to SC reinvention, 5 percent of customers associate it with predictions, demand planning, and inventory optimization, 3 percent associate it with logistics, and 2 percent for ML, data insights, and cloud migrations each.

In terms of high-level insights, emerging themes from customers embarking on their SC modernizations include deriving useful insights, data-driven practices, predictions, forecasting, and exploring cloud migrations for relevant systems. Resiliency, automation, and sustainability are less commonly associated with SC transformations, but we believe that these will play a larger role in the future. To provide our customers with food for thought across these objectives as well, we use examples from Amazon and other industries as interesting sources of inspiration.

As for functions that typically fall under SC modernization scope (i.e., what?), demand and supply planning and logistics are the most often addressed areas. Looking forward, we believe that end-to-end SC optimizations will play a bigger role as more narrow pockets of opportunity will become fewer; the focus is likely to become more on releasing cross-department synergies.

Finally, artificial intelligence and machine learning (AIML) is a technology or how?, which is commonly associated with SC modernization. This is expected as many businesses are starting to realize the potential and benefit from decision-making support using AIML. However, it is more surprising that solutions around digital twins and simulations are more nascent, especially when mentioned in the context of SC control towers. Simulation as a technology has existed for decades, but it is data-intense and often requires specific modeling skills, which potentially explains why this is still a commercially nascent field. We believe that cloud implementations of these technologies will play an important role in making SCs more agile and able to capture more dynamic opportunities. The following section breaks down this summary into more specific examples of SC modernizations and what cloud solutions we found useful for our customers’ journeys.

Customer examples of supply chain modernization and relevant AWS solutions

So where do our customers start when they raise the subject of SC modernization? In terms of the gravitational pull to other adjacent customer benefits or why?, SC modernization goes hand in hand with data-driven insights and decisions. For example, a healthcare company is embarking on a global transformation to harmonize its SC data to achieve end-to-end visibility and enable product tracking for more reliable customer notifications. A sports goods manufacturer would like to tighten their decision-making by removing silos between data sources, getting rid of third-party connectors to speed up analysis, and creating cross-BU insights to holistically improve SC performance.

Another transformational example is a food manufacturer who assesses how their SC complexity impacts costs and key operational metrics to rationalize their SKU portfolio. Another, and also analytical why? mentioned jointly with SC modernization, is forecasting and predictions. For example, a fashion company is undergoing a digital transformation using predictive analytics for retail and e-commerce volume forecasting, markdown optimization, e-commerce reviews, and other use cases. Migration into the cloud is the third largest why? associated with SC modernization. For example, a food delivery platform is looking to migrate its workloads into the cloud to achieve cost efficiencies, speed up solutions, and increase its reliability so that the technology stack can support an aggressive business growth strategy. Another example is a supermarket chain looking to migrate its on-premises SC solutions into the cloud to enable a scalable solution for end-to-end SC visibility, global and dynamic inventory views, and optimized decision-making. We’ve found such requests for dynamic and cloud-based SC platforms typical and created an industry-agnostic primer for quick platform development to enable fast-moving, event-driven, and coordinated decision-making.

As for SC modernization and affected SC areas or what?, we are seeing a strong relationship with demand planning and inventory optimization, which, in turn, unsurprisingly also tightly connects to forecasting and why? predictions. As a classic example, a furniture manufacturer is looking to optimize their SC by better forecasting demand to maintain the required reorder levels, reduce current losses in sales and stockouts at the store, and better anticipate changes in customer demand.

It is probably not surprising that we see demand predictions and optimized inventory levels as common starting points for SC modernization since these directly improve the bottom line of our customers with often impressive results. For example, we have seen 10 percent improvements for retail customer in-stock rates, waste reduction by up to 30 percent, and gross profit growth up to 25 percent. As another industry example, an IT hardware provider is optimizing their demand planning and inventory levels for spare parts to reduce their obsolescence.

Furthermore, SC modernization often includes logistics as an area of focus. For example, a convenience store chain is looking to modernize its SC workflows and support micro-fulfillment as part of the omnichannel strategy by enabling real-time inventory and item tracking during shipments or transfers. Another example is an energy service company that is looking for logistics and fulfillment synergies with Amazon to improve its service levels and focus on core activities. And an online retailer is looking for inbound SC optimization opportunities by assessing the total distance the products travel from vendors and looking for levers to reduce traveled mileage, costs, and CO2 emissions.

Finally, considering SC modernization with lenses of how? or a commonly thought-of technology, AIML is the most frequently mentioned solution that our customers are penciling in for SC improvements. This is in line with the previous point of forecasting and predictions as another closely related why? and demand planning and inventory as a most common what. These create a prominent triangle that closely relates to SC modernization.

An interesting example is a technology company seeking an MLOps solution to accelerate artificial intelligence innovation in SC and speed up ML delivery into production to reduce waste, improve production quality, and optimize SC operations. Another example is an apparel company that is looking to enable end-to-end ML lifecycle capabilities through Amazon SageMaker and migrate 500+ users to the platform to accelerate and standardize delivery of strategic initiatives by productizing AIML use cases, including SC optimization.

Some customers do not mention SC modernization by name but are looking for SC insights and data-driven decision-making, which, as discussed previously, is a common element of modernization for other customers and can be considered an integral part of SC visions. AIML is also increasingly finding its applications in logistics. For example, we are seeing a strong AIML use case to predict shipment estimated time of arrival (ETA), which Amazon SageMaker Canvas executes with no-code machine learning.

Six traits of successful supply chain modernizations

Understanding how our customers see and approach SC modernization, coupled with our broader operational experience from Amazon, allows us to summarize common traits of successful SC transformations and their ultimate outcomes. Many of our customers are starting their SC transformations by breaking data silos, aiming for end-to-end SC transparency, and looking to apply modern analytics such as AIML to SC areas, where these may be useful to improve the quality of decision-making, take out costs, and create operational efficiencies. These are natural starting points, and previously we also discussed the concept of thinking big, starting small, and scaling fast.

However, in terms of thinking big and deriving a future SC vision, we found the analysis above also useful in terms of what our customers are only starting to ponder about, i.e., emerging links between why, what, and how. In our opinion, some of this emerging thinking should already be an integral part of the SC strategy development today. At AWS, we defined six traits or features of successful SC modernizations with example questions that are worth answering to maximize the opportunity capture in the future:

1. Truly integrated business planning. What does the process of integrated planning look like to support end-to-end trade-off assessments of costs, speed, CO2, etc., to arrive at the best decisions? How will it work organizationally? What kind of solutions will be required for trade-off assessments and contingency simulations, given supply and demand uncertainties?
2. Control, visibility, and tracking. Do we have end-to-end visibility not only across internal but also our extended SC? Is the data reliable, and will we be able to create robust algorithms for automated decisions? What additional data could be useful, e.g., weather, traffic, social networks, Internet of Things (IoT), etc.?
3. Data-driven everything. How many of our SC decisions are truly data-driven or rather based on experience and intuition? Are we using prescriptive and predictive analytics to their full potential? What are our self-learning applications for the AIML stack? Does it fully leverage our collected data, and what areas do we consider underserved?
4. Responsive decision automation. How will our decision-making progress towards automation create automated self-adjusting and course-correcting SC? What decisions do we tackle first, how do we assess their efficiency, and what contingencies do we put in place? How do we automatically capture decision quality, learn from them and improve their quality in the future?
5. Collaboration with external parties. How will end-to-end business integration expand beyond four walls of the enterprise? For example, collaboration with suppliers, partners, customers, etc.? How do we work with them to ensure our integration efforts benefit both parties? What automation opportunities arise from tighter integration?
6. Sustainability as a decision criterion. How accurately do we capture our carbon footprint? Is it currently a factor for our decision-making alongside costs, speed, etc.? How do we analyze opportunities for CO2 reductions, and what levers are we pulling to minimize our impact on the environment?

This list helps to derive a long-term SC strategy and create a solid foundation for a truly digital operation of the future. Many of our customers are revising SC foundations today, and it is our objective to help them address not only current challenges but also enable them for the next natural step of SC evolution and get ready for future challenges and capitalization on new opportunities. If there is anything we learned in the past few years, it is to expect the unexpected, and we believe that cloud technology is a fit-for-purpose technological answer due to its reliability, scalability, and flexibility to perform in a wide range of circumstances.

Conclusion

With rising SC costs and increasing uncertainty in the modern world, it is only natural that our customers are looking for SC modernizations to capture opportunities, release benefits, and quickly adapt to ever-changing realities. Although there is no universal recipe or blueprint, we summarized common guiding and informative themes for the SC transformations. For example, to start thinking more holistically about data, analytics, and decision-making to maximize operational efficiencies, reduce costs, achieve the next level of agility, develop automation, and lead the industry rather than play catch-up.

Furthermore, we discussed additional traits of successful SC transformations that enrich currently common thinking, and these need to contribute to the currently laid foundations of future SCs. In this vein, we encourage our customers to think about their longer-term SC vision and business and technology roadmaps to implement it. If you would like to discuss what SC modernization could look like for your business, please reach out to your account manager to set up a discovery workshop with the AWS Supply Chain, Transportation, and Logistics business unit.