On Supply Chain Disruption and How to Respond. Q&A with Mark Holmes

The pandemic accelerated digital transformation across industries. Digital initiatives that were previously projected to be five, even ten years out, were implemented in months in response to the drastic shift to a remote environment. The fast pivot to accelerated digital transformation opened the door to new opportunities and revenue streams for businesses – but ongoing supply chain volatility remains a major challenge.

1) The Coronavirus disease (COVID-19) pandemic has caused dramatic shifts in demand patterns as well as impacting the ability for manufacturers to meet that demand.  What are the main reasons why it is increasingly difficult for retailers to provide customers with reliable services?

We’re at a time of major transition for the U.S. economy. Now that the worst of the pandemic has passed, businesses have added jobs at a rate of 540,000 per month since January. Many consumers are making large purchases with savings accumulated during the pandemic, sending new home sales to their highest level in 14 years and auto sales to their highest level in 15 years. While this is good news for businesses and workers alike, it also creates challenges. 

The pandemic accelerated digital transformation across industries. Digital initiatives that were previously projected to be five, even ten years out, were implemented in months in response to the drastic shift to a remote environment. The fast pivot to accelerated digital transformation opened the door to new opportunities and revenue streams for businesses – but ongoing supply chain volatility remains a major challenge. With demand patterns fluctuating daily and manufacturers struggling to meet that demand, it has become increasingly difficult for retailers to provide exceptional customer experiences from the “first mile” of supply chains, right through to the “last mile”. Without complete visibility into the supply chain, businesses can only react to disruptions as they arise. 

2) In your opinion, what are the tools needed to develop an integrated network that allows supply chains to proactively adjust to disruptions?

These rising levels of supply chain volatility require a more proactive and predictive approach for businesses and retailers alike. This requires the use of a data platform that facilitates seamless, real-time communication and data integration between manufacturing, logistics providers, suppliers, and the range of different and disparate systems in use.

Take the example of SPAR Austria, a member of SPAR, one of the world’s largest food retailer consortiums. The company saw that stores ran short of goods because they had to rely on inaccurate data calculated by the central office. SPAR Austria recognized that accurate data would be key to overcoming this disruption, but didn’t know where to start. After carefully researching the options, the company realized that it was lacking an end-to-end enterprise resource planning (ERP) and point-of-sale (POS) system. With this embedded technology, managers of local stores would be able to control their inventory at shelf level with accurate data to improve OSA (On-Shelf-Availability). 

SPAR Austria ultimately decided to implement IMAge (Integrated Management Application for Grocery Enterprises), a solution based on the InterSystems IRIS data platform. Originally developed by SPAR Austria to adapt to the fast-growing Eastern European markets, the solution provides a browser-based interface that seamlessly combines information and functionality from local and centralized sources. The web interface gives store managers a unified, end-to-end view of their sales, inventory, orders, and deliveries. Faced with the disruption of the pandemic, managers are able to control the movement of goods, the inventory in their stores, and pending deliveries. 

3) Can you give us some examples how to include the ability to proactively foresee when disruptions in global supply will cause delays or shortages during the first mile?

Moving away from linear supply chains to digitally integrated supply chains or integrated networks will help to optimize the triggering of those actionable insights. This type of real-time communication and data integration, also known as a control tower, continuously feeds data into and out of the myriad of applications and ERP systems within an enterprise and its ecosystem. This provides businesses with risk insights that highlight the constraints that could negatively impact production and sourcing, customer order management, and capacity management. These constraints will trigger the necessary actions to be taken for processes including, inventory balancing, forecast and demand adjustments, manufacturing plans, transportation, and replenishment adjustments to name just a few.

4) Why is so important to focus on the fist mile of the supply chain?

The “first mile” is typically defined as the first 120 days of the supply chain, where importers have the ability to affect the success of their product and inventory. The “last mile” refers to the transportation of goods from a warehouse or distribution center to their final destination — typically, the customer’s doorstep. The first mile is where supply chain experts can optimize the efficiency, accuracy, and delivery of the last mile. Thus, if you can’t manage the first mile then the last mile will feel the ramifications. 

5) Once you were able to predict when there will be disruption in the first mile,  what are actions that need to be taken?

Leveraging innovative solutions that will predict when there will be disruption in the “first mile” allows companies to optimize the “last mile”. From there, retailers and manufacturers can shift sourcing requirements, enable inventory rebalancing in the network, or optimize product allocation in real-time for the “last mile” whether it be into manufacturing, Point of Sale or most optimal distribution point.  It is all about creating prescriptive analysis to enable actionable insights to provide the business users with more accurate faster “time to decision” or optimizing your existing applications.

6) Can you give us some tips on how do move away from linear supply chains to digitally integrated supply chains or integrated networks?

The use of modern data platforms with a data fabric architecture with integrated AI and ML will play a crucial role in helping to make this a reality for manufacturers and CPG/retailers. Leveraging a data fabric architecture is a new approach to connecting disparate data and sensing disruption in real-time to support various applications, analytics and provide intelligent insights. Data fabric provides the connectivity and integration needed for full supply chain visibility with actionable insights that are connected, real-time and accurate. As we move to a digital core to synchronize the functions of the supply chain, it is imperative a modern data platform is used to be the “connective tissue” with disparate data sources and systems. See below a diagram that illustrates the “digital core” I’m referencing here:

7) What are the main challenges in real-time communication and data integration between manufacturing, logistics providers, suppliers and the range of different and disparate systems in use?

Most organizations don’t trust the data they are seeing and lack the technology to extract the intelligence they need. The business needs the basics, such as visibility into inventory levels, sales, and production output. The visibility into individual sources and items adds an additional level of complexity and opportunity. Unfortunately, this has also yielded more data than any human being (and many existing systems) can manage. With all of this data siloed in disparate systems, sources, and locations, it can be difficult for companies to truly get the most out of these assets.

Lack of visibility also correlates with a company’s lack of agility. According to a recent IDC report, 70% of companies are focusing on improving supply chain visibility and 80% are looking for ways to be more agile. It is very important to see what is happening in real time, having the capabilities to quickly assess what is happening, and then do something about it. However, if the supply chain lacks agility, no amount of forewarning will help if you are not able to respond to what you see; conversely, agility will be much less useful if you lack the ability to know where and how to react.

Thus, the concept of a “control tower” has emerged – a way to gain visibility across platforms and databases and that provides an integrated, accurate, and real-time view into the entire enterprise, as well as partners’ data and systems. Without an overarching and accurate view of the business, it’s difficult to plan for growth and practically impossible to respond to a supply chain disruption like a pandemic.

8) Can so called control towers be further optimized by integrating them with the Internet of Things (IoT)? How?

Control towers can be further optimized by integrating the Internet of Things (IoT). Through data-capturing devices (including IoT), businesses gain access to real-time data at the edge that provides valuable details regarding orders, shipments, location, and more. Knowing when product integrity has exceeded customer-driven thresholds for temperature, humidity, or vibration at any point in the supply chain, including within manufacturing, can have a significant impact on the customer experience and affect yield and sustainability.

9) How are technologies such as artificial intelligence (AI) and machine learning (ML) playing a role here?

In addition to incorporating reliable and timely data into integrated business processes, industry leaders are looking at advances in artificial intelligence (AI) and machine learning (ML) technologies to aid in decision making. In some cases, this means incorporating analytics into automated processes to prescriptively drive the business, and other times it means gaining valuable diagnostic and predictive insights for strategic planning.  Either way, the business becomes smarter and guided by data, not just gut feel, and evolves from reporting what happened to predicting what is likely to happen and proactively performing intelligent data driven actions based on the analyses.

Demand management is one area of the supply chain where companies have been focusing artificial intelligence (AI) and ML efforts to better predict and model demand. While some organizations focus on aggregated demand, leaders have started to break down planning into more specific levels, from region to store cluster and even down to individual stores and SKUs. More detailed and accurate forecasting processes can yield impactful improvements to overall performance and profitability.

Going back to the SPAR Austria example, by automating optimized replenishment in real-time through AI and Machine Learning, the company could sense demand shifts for 800 promotions per store (1,500 stores) per week and improve on-shelf availability. This significantly improved demand forecasting in light of the pandemic.

By leveraging advanced analytics technologies like ML, companies can automate predictable and repeatable situations, taking the burden off of the user. In this way, the smart decision is embedded into the process and the system takes care of exceptions without human intervention, leaving users free to manage more pressing issues. Sometimes these are tactical issues like credit card fraud, and other times, it may involve more complicated routing for logistics providers.


Mark Holmes, Senior Advisor for Supply Chain, InterSystems.

Mark Holmes joined InterSystems in 2021 as its Senior Advisor for Supply Chain to broaden InterSystems global market in supply chain. He brings more than 25 years of experience in consulting, manufacturing operations, and software development from such organizations as Dow Chemical, GS1 (Brussels), Aspen Technology, and GSI. He specializes in working with manufacturers and retailers/CPG to solve their most difficult supply chain issues through digital transformation with a modern data fabric architecture. He earned a BS degree in business administration from Indiana University in Bloomington, Indiana, and an MBA from Bentley University in Waltham, Massachusetts.

Sponsored by InterSystems

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