The world for OEMs and many other companies has changed significantly. In today’s dynamic world, the paradigm of customer expectations has evolved like never before. Gone are the days when the quality of a product was the sole driver of consumer decisions. Modern customers, empowered by a wealth of information and options, demand more than just a well-working machine. They seek value not just in the product, but in the holistic experience encompassing price, delivery speed, sustainability, and customer service. These critical aspects are no longer byproducts of the R&D and manufacturing process; instead, they are deeply rooted in the efficiency and resilience of the supply chain.
As a result, supply chain optimization is no longer a nice-to-have. In fact, in today’s world it is a necessity for companies to survive. A dissatisfied customer, in today’s interconnected world, will quickly pivot to alternatives. Companies that understand this importance, however, will become successful and witness the power of scale that the 21st century with all its selling channels yields.
In this digital age, data and digitization is the cornerstone of supply chain excellence and therefore exceeding customer expectations. It’s the key to unlocking efficiencies that lead to faster deliveries, competitive pricing, and ultimately, skyrocketing revenues and margins.
However, the challenge remains: how do OEMs align their supply chain management strategy with these modern customer expectations? This blog delves into this question, exploring the expectations of contemporary customers from OEMs and how harnessing data is pivotal in meeting these demands. Stay tuned as we explore these crucial dynamics.
What do partners and customers expect from OEMs? The right stuff at the right time in the right quantity. Oh, and at the lowest possible price if possible.
Here’s what customers are expecting from OEMs today and in the future.
Customers want what they want, and they want it yesterday. Previously, it was superior products and quality that attracted and retained customers. Nowadays, it’s delivery time and availability, price, and customer services that have become more and more important to consumers.
Another key customer consideration is sustainability—customers want to know that the companies they work with engage in sustainable business practices. And they’re happy to put their money where their mouth is.
In the past year, 48% of companies have experienced increased pressure to adopt a more sustainable supply chain—and doing so makes sense for businesses. A whopping 73% of consumers are willing to pay more for sustainable products.
A sustainable supply chain is no longer just about keeping a clean conscience—it’s also about keeping your pockets full, too.
Competitive pricing has always been—and will continue to be—a crucial part of what attracts customers to your brand. If they can get the same quality elsewhere for less—can you blame them?
With more options and information than ever, customers are free to look around for their best option. Despite climbing costs, manufacturers need to prioritize competitive pricing to attract and keep customers.
Customers are demanding more and more from the customer experience when it comes to working with OEMs. 84% of customers say the experience provided by a company is just as necessary as its products and services.
That means even if you’re providing the best products at the most competitive prices, manufacturers that provide a better customer service could still beat you out when it comes to meeting customer expectations.
In an era where standing still equates to falling behind, businesses must evolve to meet these contemporary hurdles head-on. But how? What does that process look like?
Even though customers are more demanding than ever before, companies nowadays have one of the most powerful weapons at their disposal: data. When properly harnessed, data can not only help meet but also exceed customer expectations—providing a proactive customer offering and competitive edge.
Key to any kind of supply chain transformation harnessing unlocked potential is transparency. Something that the 45% of businesses with limited visibility over their supply chain desperately need. The ultimate goal? A supply chain that is so transparent that you know exactly what and how much your customers need and when.
Doing so can greatly benefit your customers’ experience and, subsequently, their perception of your brand. Take the UK grocery chain Morrison’s, for example. When Morrison’s deployed an AI tool to track and optimize inventory levels, the business was able to reduce in-store shelf gaps by 30%. This then allowed them to take the top spot for customer service in an independent survey—a big jump from their previous fourth-place position. Additionally, they increased their revenue significantly.
While implementing these changes may seem daunting initially, it’s less about a big bang shift changing every single company process from top to bottom, but more about a cultural shift toward agile data-driven decision-making.
It’s not R&D or higher production efficiency that’ll ensure businesses can meet demand. It’s a resilient, forward-looking supply chain. Creating such a supply chain requires end-to-end supply chain visibility and control—which comes about with adequate data.
So, transformation is about creating a data culture to anticipate shifts in demand and better meet customer needs—but how?
The flow of goods depends on the flow of data. The data-driven mindset needs to permeate every level of the organization—from interns to the boardroom. To stay competitive, businesses need to leverage customer signals to give leaders the ability to optimize forecasting, planning, and execution.
Since every supply chain starts and finishes with the end customer—and is a continuous cycle—companies must bridge the digital gap between them and their end customers. However, to see this impact, businesses need to analyze the barriers stopping the flow of data—such as inaccessible data silos across complex supplier, distribution or dealer networks. By consolidating even just a small chunk of regional data in the cloud with a powerful connectors system (@clearops), businesses can immediately put it to work to fuel more informed decisions and set global benchmarks.
Despite the common myth that it takes years and enormous costs to rebuild and optimize your supply chain, it only takes five days of data collection to have an immediate impact.
It “only” requires a major shift in how data is managed and valued in an organization and a pivot to leveraging data in the board meeting as well as whenever a decision is taken. If the boardroom does not back up this shift, it won’t happen. Companies must be agile to be competitive, but agile doesn’t mean impulsive. Agility needs data for informed decisions.
With total buy-in from crucial company stakeholders, businesses need to turn their focus to the type of data they’re collecting—and pivot away from the traditional methods of using biased data to optimize supply chain operations.
Manufacturing businesses usually calculate their future demands solely based on their own past demand of their dealers and distributors, not based on their actual end-customer demand. Not based on the actual installed base out there. Not based on the actual market trends, market conditions or other relevant real-time factors. The powerful combination of data from every stakeholder throughout the supply chain – from supplier to OEM to dealer to consumer – yields a never seen range of optimization potential. And the best part about this is, all stakeholders in a supply chain benefit from centralized supply chain data collection.
79% of companies with strong supply chains achieve significantly higher revenue growth than average. A connected approach to supply chain management helps organizations anticipate shifts in demand and supply and enables them to better meet customer needs, regardless of extenuating factors.
So, ensuring company-wide buy-in and adopting new supply chain management strategy in a step-by-step approach helps companies transform customer experience. The final step is shifting from crawling and walking to running.
The crawl, walk, run continuum is how businesses get from descriptive information and analytics to a predictive supply chain.
Descriptive analytics summarizes and interprets historical data to gain insights into the events, patterns, and trends in a business. It involves examining data through various statistical techniques and visualizations to provide a clear understanding of what has happened. This data collection and analysis process is in place at many global companies, and it is what we are referring to when we talk about the crawl and walk phases of the continuum. The process has been very successful in helping companies cut down costs and waste in their supply chains.
However, it’s now time for businesses to take a step forward and embrace new data science solutions—and research shows this shift is already well underway. In fact, 74% of supply chain leaders planned to increase investment in technology and innovation in 2023.
If you’re not one of them, you’re getting left behind.
Crawling and walking no longer suffice, businesses need to run. This run phase of the continuum is where businesses switch from descriptive analytics to predictive analytics.
Predictive analytics consists of using data to predict future trends and events. It uses historical data to forecast potential scenarios that can help drive strategic decisions.
Companies are moving into the running phase of the continuum by layering analytic techniques and tools over their existing information architecture—the descriptive approach they’ve been using until now. These tools enable organizations to improve agility and responsiveness by sensing and shaping demand sustainably. And help identify unseen patterns that yield the actual top line benefits and efficiency gains.
So, optimized supply chains are what businesses want, and data is how they get there. But what exactly does that look like?
We’ve talked a lot about new technology helping businesses harness their data for predictive supply chain management, but what exactly does that look like?
Supply chain transformation starts with collecting data—and any good supply chain management strategy prioritizes data collection as the first step. At ClearOps, we enable seamless end-to-end data collection—from machine manufacturing companies to their dealers to the end-users and machines in the field. We do that by leveraging our unique data integration technology that seamlessly connects disconnected and disparate systems on one central layer of truth. Once we collect that data, it is time to unlock the hidden potential. At ClearOps, we use the power of data in order to predict future parts and service demand. And to orchestrate the network of thousands of stakeholders across the world: dealers, technicians, end-customers. The result? Machines that never ever stop running. And even if they do, we make sure the parts and technicians are where they are supposed to be — available to the customer.
The future is here, are you ready?
How do customer expectations like immediate delivery, sustainability, competitive pricing, and excellent experiences influence OEMs’ supply chain strategies, beyond product quality?
Customer expectations, like immediate delivery and sustainability, shape OEMs’ supply chain strategies, requiring efficient logistics, sustainability, cost competitiveness, and superior service.
What challenges to OEMs face in effectively using data to meet modern customer demands and enhance supply chain performance, despite the emphasis on data importance in the article?
OEMs face challenges in using data effectively for meeting customer demands and enhancing supply chain performance, including data silos, interoperability issues, data quality, and the need for advanced analytics.
What barriers or resistance might OEMs encounter when fostering a data-driven culture and agile decision-making processes, and how can they overcome these obstacles?
Implementing a data-driven culture and agile decision-making encounters barriers such as organizational inertia, lack of executive buy-in, resource constraints, and cultural resistance to change.
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