May 17, 2020

How Business Intelligence and Analytics can Optimise Supply Chain

Supply Chain
business intelligence
managment reporting
business objectives
4 min
How Business Intelligence and Analytics can Optimise Supply Chain
Business intelligence (BI) has come a long way from its management reporting roots. Analytical decision support is embedded within todays supply chain p...

Business intelligence (BI) has come a long way from its management reporting roots. Analytical decision support is embedded within today’s supply chain planning, manufacturing and logistics solutions. Users gain insights based on real-time data rather than yesterday’s batch roll-ups. They can manage by exception, get automated recommendations and evaluate trade-offs before they enter transactions—whether they’re transferring inventory, releasing work orders or arranging shipments.

These recent technical advances in BI are impressive. But their translation into a more intelligent supply chain that sustainably optimises business performance remains elusive. There are three primary challenges:

Challenge No. 1: Balancing Competing Business Objectives

To optimize long-term performance, organizations must balance multiple business objectives, such as satisfying customers and controlling costs. No company can be the best at everything, but with proper focus, design and management, companies can leverage analytics to reveal when customer and cost objectives complement or conflict with one another. They can then make better supply chain decisions.

For example, many businesses can calculate their perfect order percentage in a BI solution, but few can exploit supply chain analytics to drive their perfect order performance to the next level. That means going beyond on time, shipped complete to the underlying factors that drive customer behaviour. What is the customer profile for each business segment? How about his or her preferred product mix, primary delivery channel and degree of engagement? Broader awareness is the first step to improving customer loyalty, brand reputation and forecasting ability, but it requires dramatically more discipline and data.

Assessing and controlling landed costs may seem simple on the surface. However, direct material, production and logistics costs tell only part of the story. Many companies also struggle to allocate overhead and transfer costs in a way that aligns with their strategic objectives. The ultimate goal, though, should not be cost accounting for its own sake, but enabling opportunity cost decisions: where to position inventory in the supply network, how much material to purchase based on consumption or which production runs to outsource.

Challenge No. 2: Sourcing and Curating Contextual Data

Decades of investment in data warehousing are making enterprise data more accessible, timely and consistent. However, a profusion of legacy systems with diverging product masters, organizational schemas and batch integration programs can thwart even sophisticated master data management solutions. When migration is possible, tightly integrated cloud applications offer a better foundation for holistic supply chain BI, as well as reduced time lag as the business’s analytical needs evolve.

Challenge No. 3: Visibility to External Data

Tapping new external data sources is the next BI opportunity. Internet of Things (IoT) sensors on devices, materials and assets generate trillions of real-time updates. Social media chatter is voluminous and unstructured, but a single remark can impact demand for a product—or even a whole brand. Shopper data collected from e-commerce systems provides leading indicators of interest, promotional response and competitive threats.

This Big Data is transient, noisy and often peripheral to the business, but it also offers a way to anticipate issues and respond faster, with deeper understanding. Advances in stream processing, data science and the cost of storage are producing BI solutions for smart metrics, Big Data projects and social data mining. But to move beyond marketing applications into the supply chain, organizations need to combine external data insights with supply chain metadata and analytics. End-to-end visibility across external and internal data can help translate the impact of an incident in one part of the business into others, so you can improve service, reduce costs and identify future risks.

The Future’s Intelligent Supply Chain Is Sooner than You Think

The BI technology, data and organizational models to achieve an intelligent supply chain already exist. If you aren’t already harnessing your data, it’s time to start. And if you are already measuring and acting on that data, it’s critical for your business to take a more holistic approach to your BI as data velocity and variety continue to accelerate. Better customer service doesn’t always come at a lower cost, but the greatest competitive advantage goes to those who can first identify when it does.

Jon Chorley is the vice president of supply chain management strategy at Oracle.

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Jun 11, 2021

NTT DATA Services, Remodelling Supply Chains for Resilience

6 min
Joey Dean, Managing Director of healthcare consulting at NTT DATA Services, shares remodelling strategies for more resilient supply chains

Joey Dean, the man with the coolest name ever and Managing Director in the healthcare consulting practice for NTT DATA and is focused on delivering workplace transformation and enabling the future workforce for healthcare providers. Dean also leads client innovation programs to enhance service delivery and business outcomes for clients.

The pandemic has shifted priorities and created opportunities to do things differently, and companies are now looking to build more resilient supply chains, none needed more urgently than those within the healthcare system. Dean shares with us how he feels they can get there.

A Multi-Vendor Sourcing Approach

“Healthcare systems cannot afford delays in the supply chain when there are lives at stake. Healthcare procurement teams are looking at multi-vendor sourcing strategies, stockpiling more inventory, and ways to use data and AI to have a predictive view into the future and drive greater efficiency.

“The priority should be to shore up procurement channels and re-evaluate inventory management norms, i.e. stockpiling for assurance. Health systems should take the opportunity to renegotiate with their current vendors and broaden the supplier channel. Through those efforts, work with suppliers that have greater geographic diversity and transparency around manufacturing data, process, and continuity plans,” says Dean.

But here ensues the never-ending battle of domestic vs global supply chains. As I see it, domestic sourcing limits the high-risk exposure related to offshore sourcing— Canada’s issue with importing the vaccine is a good example of that. So, of course, I had to ask, for lifesaving products, is building domestic capabilities an option that is being considered?

“Domestic supply chains are sparse or have a high dependence on overseas centres for parts and raw materials. There are measures being discussed from a legislative perspective to drive more domestic sourcing, and there will need to be a concerted effort by Western countries through a mix of investments and financial incentives,” Dean explains.

Wielding Big Tech for Better Outcomes

So, that’s a long way off. In the meantime, leveraging technology is another way to mitigate the risks that lie within global supply chains while decreasing costs and improving quality. Dean expands on the potential of blockchain and AI in the industry

“Blockchain is particularly interesting in creating more transparency and visibility across all supply chain activities. Organisations can create a decentralised record of all transactions to track assets from production to delivery or use by end-user. This increased supply chain transparency provides more visibility to both buyers and suppliers to resolve disputes and build more trusting relationships. Another benefit is that the validation of data is more efficient to prioritise time on the delivery of goods and services to reduce cost and improve quality. 

“Artificial Intelligence and Machine Learning (AI/ML) is another area where there’s incredible value in processing massive amounts of data to aggregate and normalise the data to produce proactive recommendations on actions to improve the speed and cost-efficiency of the supply chain.”

Evolving Procurement Models 

From asking more of suppliers to beefing up stocks, Dean believes procurement models should be remodelled to favour resilience, mitigate risk and ensure the needs of the customer are kept in view. 

“The bottom line is that healthcare systems are expecting more from their suppliers. While transactional approaches focused solely on price and transactions have been the norm, collaborative relationships, where the buyer and supplier establish mutual objectives and outcomes, drives a trusting and transparent relationship. Healthcare systems are also looking to multi-vendor strategies to mitigate risk, so it is imperative for suppliers to stand out and embrace evolving procurement models.

“Healthcare systems are looking at partners that can establish domestic centres for supplies to mitigate the risks of having ‘all of their eggs’ in overseas locations. Suppliers should look to perform a strategic evaluation review that includes a distribution network analysis and distribution footprint review to understand cost, service, flexibility, and risks. Included in that strategy should be a “voice of the customer” assessment to understand current pain points and needs of customers.”

“Healthcare supply chain leaders are re-evaluating the Just In Time (JIT) model with supplies delivered on a regular basis. The approach does not require an investment in infrastructure but leaves organisations open to risk of disruption. Having domestic centres and warehousing from suppliers gives healthcare systems the ability to have inventory on hand without having to invest in their own infrastructure. Also, in the spirit of transparency, having predictive views into inventory levels can help enable better decision making from both sides.”

But, again, I had to ask, what about the risks and associated costs that come with higher inventory levels, such as expired product if there isn’t fast enough turnover, tying up cash flow, warehousing and inventory management costs?

“In the current supply chain environment, it is advisable for buyers to carry an in-house inventory on a just-in-time basis, while suppliers take a just-in-case approach, preserving capacity for surges, retaining safety stock, and building rapid replenishment channels for restock. But the risk of expired product is very real. This could be curbed with better data intelligence and improved technology that could forecast surges and predictively automate future supply needs. In this way, ordering would be more data-driven and rationalised to align with anticipated surges. Further adoption of data and intelligence and will be crucial for modernised buying in the new normal.

The Challenges

These are tough tasks, so I asked Dean to speak to some of the challenges. Luckily, he’s a patient guy with a lot to say.

On managing stakeholders and ensuring alignment on priorities and objectives, Dean says, “In order for managing stakeholders to stay aligned on priorities, they’ll need more transparency and collaborative win-win business relationships in which both healthcare systems and medical device manufacturers are equally committed to each other’s success. On the healthcare side, they need to understand where parts and products are manufactured to perform more predictive data and analytics for forecasting and planning efforts. And the manufacturers should offer more data transparency which will result in better planning and forecasting to navigate the ebbs and flows and enable better decision-making by healthcare systems.

Due to the sensitive nature of the information being requested, the effort to increase visibility is typically met with a lot of reluctance and push back. Dean essentially puts the onus back on suppliers to get with the times. “Traditionally, the relationships between buyers and suppliers are transactional, based only on the transaction between the two parties: what is the supplier providing, at what cost, and for what length of time. The relationship begins and ends there. The tide is shifting, and buyers expect more from their suppliers, especially given what the pandemic exposed around the fragility of the supply chain. The suppliers that get ahead of this will not only reap the benefits of improved relationships, but they will be able to take action on insights derived from greater visibility to manage risks more effectively.”

He offers a final tip. “A first step in enabling a supply chain data exchange is to make sure partners and buyers are aware of the conditions throughout the supply chain based on real-time data to enable predictive views into delays and disruptions. With well understand data sets, both parties can respond more effectively and work together when disruptions occur.”

As for where supply chain is heading, Dean says, “Moving forward, we’ll continue to see a shift toward Robotic Process Automation (RPA), Artificial Intelligence (AI), and advanced analytics to optimise the supply chain. The pandemic, as it has done in many other industries, will accelerate the move to digital, with the benefits of improving efficiency, visibility, and error rate. AI can consume enormous amounts of data to drive real-time pattern detection and mitigate risk from global disruptive events.”


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