Insights on the Blind Spots in the Demand-driven Supply Chain

Lack of Real-time Data and Visible Latency

• An ideal demand-driven supply chain model works on real-time information distribution of the current demand and inventory levels among all participants in the supply chain. This enables the participants to react quickly by changing their production or distribution activities whenever unexpected changes occur in the system. The below infographic explains the differences between a traditional supply chain and the demand-driven supply chain.
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• However, industry experts agree that real-time data analytics is still at the nascent stages of development in many companies and the process will take time.
• A survey conducted by American Shipper found that visible latency arising from a lack of real-time data is one of the biggest blind spots facing the shipping industry. In the same survey, only 6% of the respondents claimed having real-time end-to-end visibility in their processes.
• As per a study conducted by the Harvard Business Review Analytic Services (HBRAS) in which they interviewed procurement officers across industries, it was found that 65% of the respondents stated that they did not have visibility beyond their immediate suppliers.
• The lack of real-time end-to-end visibility in the demand-driven supply chain is because of a lack of electronic connectivity to real-time data, no access to analytic tools, and insufficient expertise in implementing the visibility systems.

Difficulty in Aligning Metrics Among Manufacturers, Suppliers, and Retailers

• To succeed in the demand-driven supply chain, supply chain managers need to align their metrics among different entities like manufacturers, suppliers and retailers to benchmark their supply chain performance and reduce inefficiencies and gaps in the system.
• However, many supply chain managers face difficulty in aligning the metrics among manufacturers, suppliers, and retailers because all three operate and evaluate processes differently, like manufacturers considering fill rates as the best indicator for stock availability.
• The challenge for the supply chain managers arising out of different entities considering different metrics is effective coordination. This creates a blind spot that they must remove to get the different elements of the supply chain to interpret signals in the same way.

Deeper Analysis of Blind Spots Faced by Companies in Ecommerce and Retail

Not Engaging Directly With Customers

• For years, traditional consumer packaged goods (CPG) companies have relied on their relationship with retailers along with great products and smart brand placement to drive success in the ecommerce and retail industry.
• Most CPG brands have built their businesses keeping retail distribution in mind. Hence, they are heavily dependent on their retailers for a constant stream of revenue and fear that direct customer engagement will be counter-productive to their relationship with their retailers.
• However, shifting consumer preferences, low barrier to entry in the online retail market, and decreasing sales of brick and mortar retailers have made it imperative for CPG companies to engage directly with their customers to build a sustainable business. CPG companies need to consider taking into account consumer trends and shopping behavior patterns as part of their direct customer engagement.
• With the rise of ecommerce, many retailers have moved from brick-and-mortar stores to online platforms like Amazon and eBay that have effective supply chain and distribution networks.
• An effective way for CPG companies to engage directly with their customers and maintain good relationship with their retailers at the same time is to opt for the omni-channel approach. This approach combines the best features of the marketplace, physical retail stores, social media marketing, email marketing, and online websites to provide a fully-integrated shopping experience to the customers.
• As per a 2017 consumer behavior study conducted by Harvard Business Review in which they interviewed 46,000 shoppers in the US, it was found that 7% of the respondents were online-only shoppers, 20% of the respondents were store-only shoppers, while the rest 73% of the respondents utilized multiple channels for their shopping experiences.

Not Using Smart Packaging

• The modern consumers not only care about the products that they receive from CPG companies, they also care about the external packaging.
•  Easy to use, convenience, environmental sustainability, recyclability, safety from health issues, detailed information on the packaging and active brand engagement are some features that consumers want in their product packaging.
• With conventional packaging unable to address the above customer requirements, many CPG companies face the risk of missing out on significant business opportunities and facing potential disruption from competitors that utilize smart packaging.
• CPG companies using smart packaging utilize technologies like sensors, near field communication (NFC) chips, indicators, Augmented Reality (AR), radio frequency identification (RFID) tags, and QR codes to enable real-time tracking and tracing of the products in the production, distribution and sales cycles. This also generates enhanced customer satisfaction and association with the brands.
• As per a report by Deloitte, the use of smart packaging is at the initial growth stage in the pharmaceutical and FMCG industries, while it is at the introductory stage for the food, cosmetics, and industrial products industries.
• As per a report by Mordor Intelligence, the size of the global smart packaging market was $35.33 billion in 2018. It is expected to become a $44.39 billion market by 2024, growing at a CAGR of 4.19%.

Implementing Obsolete Solutions in Tackling Counterfeit Goods

• Many CPG companies are facing competitors that do not abide by industry regulations and produce excellent fakes of their products.
• As per the Global Brand Counterfeiting Report 2018, the size of the global counterfeit market is expected to become $1.82 trillion in 2020. Also, as per a survey of the global consumer behavior conducted by the Edelman Trust Barometer, only 52% of the respondents stated that they trust businesses.
• The above statistics show that with the rise in counterfeit goods both online and offline, many consumers are unwittingly ending up buying fakes, which is causing a lack of faith on these CPG companies. Brand reputations are being tarnished by the counterfeiters monetizing on behalf of the CPG companies.
• Even though most CPG companies are aware of the counterfeit goods, they are unwilling to implement new solutions or move over from their current obsolete ones, since brand security is less important to them than revenue generation.
• Most of the current product authentication technologies implemented by the CPG companies are offline-based, like holograms and special inks. However, since most of today's purchase is done online, customers do not readily have special devices for product authentication and verification. Also, most optical technologies cannot be differentiated with naked eyes, with the result that customers end up buying fake, counterfeit products.
• Newer technologies like image recognition, encrypted QR code, NFC, AR, blockchain, and AI allow product data capturing, interlocking security authentication, and enhanced customer engagement. However, CPG companies should be willing to implement these new technologies rather than sit back until they are badly affected by the counterfeit goods.

Financial Impacts of Not Having Real-time Data in Organizations

Difficulty in Maintaining Competitive Advantage

• Real-time data helps companies maintain their competitive advantage in the industry. By analyzing critical parameters, companies can improve their level of foresight and position in the market.
• As an example, companies that notice a reduction in their sales can use real-time data to understand the cause. If they find that their opponents are running promotional offers to lure away their customers, they can design their own promotional offers or reduce the price of their products to pull back the lost customers. Companies that do not utilize real-time data lose their competitive advantage in the market, thus losing their sales and customers.
• Major companies like Amazon and Google have used the power of real-time data to maintain their competitive advantage in their respective industries.
• In a survey conducted by CIO Insights in which more than 150 CIOs, IT managers, developers, and IT professionals across industries participated, 19% of the respondents stated that real-time data helped improve their respective company's competitive advantage in the market.

Difficulty in Improving Customer Satisfaction

• Since customers have short attention spans and a variety of options, they resent being put on hold or re-directed several times when they call the customer service for any information. Customer service centers of companies that have real-time data of their customers can respond with greater accuracy and speed to mitigate the customer concerns.
• Customer service centers of companies can configure a real-time data dashboard to display customer information when a customer calls. This will eliminate the time taken to look up the customer data and history in their records, thus driving up customer retention and satisfaction. The dashboard can also identify bottlenecks occurring in the call queue.
• As per a survey conducted by Benchmark Portal, the average customer service call duration across all industries is 5.97 minutes. Hence, companies not having real-time data find difficulty in engaging customers during this time and end up with reduced customer satisfaction and eventually sales.
• An example of a company that utilized real-time data to retain their customers is the Dickey's Barbecue Pit chain. During one of their sales, they reported lower-than-average sales at an outlet but excess ribs at the same outlet. By using real-time data, the company texted a special sale of ribs to their nearby customers and ended up with increased sales and higher customer satisfaction.
• In the above survey by CIO Insights, 26% of the respondents said that real-time data helped improve customer experience.

Difficulty in Improving Operational Efficiency

• Real-time data helps companies understand their business performance and improve operational efficiency both from a micro and a macro perspective.
• Companies can track real-time data like location, project, department, customer, vendor, materials, and supply chain and evaluate the performance of each of these factors to improve operational efficiency. The real-time data can also help business managers to track their financial data like cash flow, revenue, assets, expenses, net revenue, profit, and others.
• As an example, real-time data can be utilized in the manufacturing sector to improve productivity and profitability. By using real-time data, a manufacturing company can track the shipment of its raw materials and ensure timely delivery so that production is not affected.
• In the above survey by CIO Insights, 23% of the respondents said that real-time data helped improve customer experience.

Insights on How Lack of Predictive Data Affects Companies

Facing Disruptions During Manmade or Natural Calamities

•  Natural disasters like earthquakes, hurricanes, floods, and wildfires, among others, cause sudden disruptions in the normal supply chain. Also, manmade incidents like wars, labor disputes, strikes, and trade wars cause similar disruptions.
• During such incidents, responding to the customers' immediate needs becomes the most important priority of companies. It requires companies to have a strategy and technology to quickly recover from the crisis and ensure that service disruptions are minimized.
• The way in which companies can recover from the calamities and ensure minimum disruptions in their operations is by using predictive analytics. Companies using predictive planning and forecasting get information on the number of resources needed to ensure minimum disruption to operations through historical and real-time data. By utilizing artificial intelligence (AI), machine learning, and predictive modeling, companies can forecast the trends and future outcomes to a sufficient extent.
• Companies adopting predictive analytics can prioritize their business operations, maintain forecasting and schedule optimizations. This increases their efficiency and effectiveness during both normal and emergency situations, besides improving customer satisfaction. Companies that do not adopt predictive analytics suffer severe disruptions to their operations during such calamities.

Difficulty in Detecting Frauds and Errors

• With increased cyber crimes and criminal activities, companies lose almost 5% of their revenue to frauds every year. Frauds not only cause financial loss to companies, they also decrease the companies' reputation.
• Using advanced predictive analytics that incorporate both numeric data (structured) and textual data (unstructured) like emails, call center notes, social media interactions, or agents’ reports, companies can detect frauds and errors faster, thus preventing vulnerabilities.
• An example of an industry that can greatly detect and prevent frauds using predictive analytics is the insurance industry. Insurance frauds (excluding health insurance frauds) total up to almost $40 billion every year in the US alone. Using predictive analytics algorithms that scrutinize claims through several processes and reveal insights into fraudulent claims, insurance companies can detect and eliminate fraudulent activities.
• By using the SPSS predictive analytics solutions developed by IBM, the Infinity Property and Casualty Corporation, based in Birmingham, Alabama, was able to record a 400% ROI within six months, besides adding $1 million in revenue and cutting the actionable time for fraudulent claims by 95%.

Research Strategy