Rethinking the supply chain to win back customer confidence

  • Posted on November 13, 2020
  • Estimated reading time 4 minutes
rethinking the supply chain

Whether it’s paper products and grocery items or ventilators and masks, the supply chain is experiencing a lack of customer confidence due to recent market disruptions. I see the disappointment when I go to the grocery store and the shelves are empty. And companies know customers are frustrated when the supplies they ordered are delayed. As an engineer, I like solving problems, and winning back consumer confidence in how businesses manage their supply chain is a big one.

Many executives recognize there’s a problem. As many as two-thirds (68%) of decision-makers we surveyed report that their organization could improve when it comes to their data supply chain. Businesses need digital technologies and advanced analytics that give them the ability to visualize a product in every stage of its lifecycle in real time, from raw materials through delivery to end customers.

Adaptive businesses are using predictive and intelligent technologies to seize this time to innovate and put the focus where it needs to be, on the customer. The value of modern cloud-based solutions is that they’re architected from the ground up to be highly configurable. Users can facilitate on-demand changes in business rules, track products across supply chains and enable rich reporting and data visualization.

Avoid the big bang approach
Nearly 94% of organizations using or planning to use AI to retool the supply chain report that the data supply chain and analytics are critical when it comes to scaling the value of AI, according to Avanade AI Maturity research. However, businesses often try to take on too much change all at once, which can overwhelm the people they need most to manage and run the supply chain.

For example, if a consumer goods company is used to relying on existing retailers or other distributors, it can use its data to validate the business opportunity and potentially sell products direct to consumers or expand into new, more profitable partnerships. This may seem simple enough. However, many clients often try the ”big bang” approach, which starts at the highest enterprise level without putting the right data platform and analytics tools in place.

Instead of taking on too much, we help clients design a proof of concept tied to business objectives. Learning along the way, we identify and design an approach that minimizes risk and is also repeatable. I’ve seen too many clients reinventing the same processes without gaining incremental value.

Out with the old and in with the new to achieve higher ROI
Most clients deal with legacy on-premises platforms not meant to unify information or provide quick access. They have multiple customer relationship management systems, applications and products, and data is stored in many forms, including spreadsheets, emails, CRM software and third-party data.

Without a single source of truth, organizations can waste so much time trying to get to information. And it’s labor intensive sorting through multiple applications and software versions. Flexible, modern platforms and AI tools help companies pivot nimbly to scale AI, quickly access data and minimize supply chain disruption. In fact, companies already investing in AI are expecting a two to five times return on investment.

Take control of your data for greater insight and increased value
For one client, we took all the data and essentially allowed the Microsoft Azure ecosystem to digest disparate sets of data, load it into a model to create reports and design visualizations. With the data, we were able to capture, interpret and analyze phrases and sentiment, positive and negative. From this data, we ended up with a semantic data model to capture the meaning from the information and then surfaced the results into visuals in Power BI dashboards.

With this information, the client was able to identify key metrics so it could rate its relationships with sellers and accurately calculate the rate of return. The company developed follow-up strategies to address performance gaps with pinpoint accuracy and put in place a repeatable process it could scale throughout the organization.

Customer-first approach requires governance and security
I think it’s very important to adopt a customer-first approach in everything we do. With this approach, it’s essential to put data governance and security in place with a strong commitment throughout the enterprise.

The principle of privacy requires companies to structurally build data protection into their core processes, technology and analytics, right from the start. By default, the processing (storage and use) of any personal data is to be protected and undertaken only on a lawful basis. This implies that businesses must have data management, lineage and transparency capabilities in place.

When data integrity measures are implemented, companies can dramatically improve customer engagement and lower risks by classifying data and adopting policies that help ensure the correct path, and by conducting audits to ensure regulatory compliance.

Win back customer confidence, increase supply chain resilience
The types of changes required to improve supply chain resiliency may be very different from the capabilities that most businesses have invested in today. It may require moving data from on-premises to modern data platforms. It may require organizing data and deploying advanced analytics to connect with consumers where they live, shop and do business.

Organizations struggling with legacy technology that inhibits their agility to respond to customer demands need to avoid the big bang approach and replace it with specific use cases that will deliver the highest business value.

Finally, it’s important to enact governance and security measures to win over customer confidence. With the right controls, businesses can assure customers that steps are in place to protect access and help ensure data integrity for a stronger, more resilient supply chain built for the future.

Register for the data supply chain report to learn more.

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