Retail demand forecasting in 2023 – How to get it right?

As witnessed in the last few years, retail demand forecasting is vital for big box stores and small players, especially for survival and growth. Inaccurate, decades-old approaches to forecasting demand are no longer applicable in the face of a constantly evolving consumer market. The same period witnessed a dynamic shift in population from urban city centers to other suburban areas, impairing retailers’ ability to position their inventory based on historical trends. From rapidly transposing demographics to population-based demand changes, decreasing brand loyalty to an exponential increase in online shopping – these are a few examples of how the market reacted to the pandemic. Even after the pandemic ended, retailers were confronted with a severe existential crisis induced by global recession, causing income disparities and driving businesses toward off-brand and discount retailers. These bottlenecks are unlikely to faze away anytime soon. Therefore, reliable demand forecasting methods leveraging new-age technologies are gaining momentum today. In the same light, the demand sensing software solution from TradeEdge can help retailers easily capture the early signals and stay market ready all the time.

Recession-induced market trends – How do they impact retail demand forecasting?

The recent alterations in market trends have been reshaping the recession-driven, post-COVID era retail environment.

From the business standpoint, a few changes, such as the conversion of stores into mini-fulfillment centers and pickup points and restructured supply chains for catering to specific household orders rather than to large-format stores, are currently witnessed.

What do these trends mean for retail business? Firstly, retailers must forge new partnerships for continuity and success. Secondly, they are vying for meaningful relations built on trust with their consumers and other stakeholders. And, finally, bringing structural changes in the industry to thrive in the new regular retail.

But given the rising market volatility, these measures might not be enough to weather the storm. Opposing traditional orthodoxies is probably the first step, but retailers need extra pairs of helping hands to balance demand with supply in the face of evolving customer preferences. Artificial Intelligence and its extended capabilities are being leveraged to improve retail demand forecasting methods, ensuring businesses are more future-ready than earlier.

Tracking demand forecasting challenges of the retail supply chain

COVID-19 left a deep gash in the system, causing a paradigm shift in how businesses used to operate earlier. The challenges mentioned above are just the tip of the iceberg. The effect of the global health crisis and its subsequent recession has been felt far and deep in the retail supply chain. Difficulties in accurate demand forecasting are their aftermath.

Since the anticipated recession is likely to compound the myriad issues already plaguing the retail industry and its supply chains, the following are a few examples to highlight the intensity of the crisis.

A slowing economy

High inflation’s financial reality minimizes the impact on higher-income households. The pandemic-fueled savings continues to ignite their strong spending. However, their even shift towards more in-person services spending is hurting big retailers and is seen as one of the significant contributors to an economic meltdown. Goods price inflation is rising higher than service price inflation.

Poor spending or altered preferences?

Customer preferences have shifted, for example, from premium to private labels. Sales for specific categories of goods, like sporting goods, hobbies, books, and music, are losing steam. Likewise, demand for durable goods is taking a backstep, implying poor retail sales at consumer durable stores. On the other hand, inflation is denting a customer’s spending capability, with the share of users intending to delay purchases going up since mid-2021.

Softening demand

The anticipation of a recession has compelled many buyers to withdraw themselves from overspending. The purchase shift is more towards necessities, not so much for discretionary goods like clothing and electronics. Most businesses are witnessing a decline in demand for premium products in their portfolios; hence, the gradual demand shift will also affect supply chains. And when the inflation is running hot, with the inventory building into weaker demands, forecasting for the future will become a tough nut to crack.

Big inventory builds

The lingering pandemic issues resulting in multiple lockdowns in China last year compelled many companies to build their inventories. Companies fear not having enough to meet the sudden burst in demand for goods and end up ordering a lot. With inadequate forecasting estimations, big inventory builds might significantly challenge retailers.

Immobile stocks

Due to continuous product and material shortages, certain companies pivoted to bulk orders in the previous two years.  Unfortunately, consumer purchasing declined further, resulting in stockpiling, pausing, or canceling supplier orders. However, if and when demand Further, immobile inventory would impact cash flow, hampering their capacity to balance supply. Similar unstable scenarios can result in poor retail demand forecasting.

Labor shortages and an aging workforce

Demand and sales forecasting also depends upon the availability of labor and an adequate workforce. But, an expected economic meltdown could easily exacerbate the ongoing supply chain labor crisis. Labor shortages and an aging workforce are already impacting the logistics and manufacturing industries, compounded by the new rounds of layoffs during the recession. As many of these points are estimations of the growing uncertainties, retail demand forecasting methods will fail to paint a near-accurate picture.

Fragmented approach

A holistic understanding of customer demand across all product categories and channels benefits the entire end-to-end supply chain, supporting efficient demand forecasting for retail. But a fragmented approach and existing siloes prevent the timely availability of data from the supplier end, which can easily falsify the estimated demand figures or increase businesses’ inability to match.

Elevating demand forecasting for retail with AI

The world market looks decisively different today, but the worst is not over yet. The supply and demand shocks of the global economy shutdown unearthed vulnerabilities in the original production strategies and supply chains of firms everywhere. In addition, distribution issues worldwide and complexities of supply chains’ global scope hampered companies’ ability to forecast demand and determine the best ways to fulfill them. Furthermore, the previously indicated market for in-store sales was greatly impacted by the growth of online purchases. Therefore, retailers need to adopt a more omnichannel approach, meaning retailers should provide accurate inventory estimations online to induce more in-store visits.

But clinging to manual systems and antiquated software solutions only adds to the rising difficulties.

Therefore, investors are embracing next-generation technologies and software solutions incorporated with AI, ML, and advanced analytics to pave the way for autonomous planning. Demand forecasting for modern retail in the face of unprecedented challenges requires the assistance of granular data in real-time – data reflecting changing buying patterns, customer buying behaviors, supply chain, and logistic channels disruptions, macroeconomic factors, weather patterns, and so on. All these factors and their interrelationships significantly affect demand daily. AI technology can easily leverage such unstructured datasets to improve forecasts.

With its extended machine learning capability, AI-enabled demand forecasting methods can quickly learn from the past and deliver better, more accurate predictions every time. Daily forecasts further enhance stakeholders’ ability to respond promptly to sudden changes in demand patterns.

AI in retail demand forecasting – Three real-world examples

#1. A Swiss food and drink processing giant struggled while handling massive volumes of sales and inventory information for over 2000 brands from siloed and disparate internal systems, multiple channel partners, e-commerce, and syndicated data providers. This resulted in sub-optimal sales planning, supply chain inefficiencies, and sluggish business growth. However, with the help of TradeEdge Demand Sensing solution, the client was able to unlock the value of data and identify $200M in potential cross-sell and up-sell opportunities, thereby winning an edge over competitors.

#2. The consumer goods industry comprises hyper-competitive markets, where high customer expectations, a wide range of products, and complex supply chain networks are primary challenges faced by CPG companies. Therefore, the latter needs efficient demand planning to ensure the availability of products at the right place and time to retain their hold over consumer markets. Unfortunately, timely placed data, critical in demand planning, must be solved owing to their multiple, disparate sources. Further, companies also needed more consistency and questionable data quality. This significant issue troubled a multi-billion-dollar global consumer goods company. Data inconsistency impacted the client’s sales efficiency, partner relationships, and productivity. However, with the help of TradeEdge Demand Sensing, the client overcame data challenges by streamlining data acquisition, improving data quality and time to insights. As a result, the client achieved 5X growth in five years than previously witnessed.

#3. The sports merchandise market comprises a network of intermediaries, wholesalers, distributors, and retailers. Each follows its point-of-sale (POS) system that affects real-time visibility into sales performance across regions and products. Without real-time harmonized data, end-to-end supply chain visibility across the ecosystem remains a persistent challenge affecting adequate demand planning for companies. Our client, a multinational corporation with a global footprint in the sports merchandising and apparel business, wanted to redesign its Point of Sale (POS) Business Intelligence system to gain insights into their retailers’ POS and inventory position. As a result, they could increase market share, collaboration, and effectiveness with better demand visibility. Also, near real-time, high-trust data could enable them to make predictive business decisions. With TradeEdge’s support, the client was able to increase demand visibility by 60%  and gain access to near real-time harmonized data.

AI in retail demand forecasting – Business benefits

Today, the global AI adoption in the retail market is growing at a CAGR of 28%, with an estimated future value of above US$ 127.09 billion by 2033. Driven by the growing popularity of online shopping, tech-savvy, and mobile-friendly Gen Z population, retailers are investing more in AI technology to improve areas such as demand forecasting, inventory management, and customer experiences.

AI for retail demand forecasting offers the following benefits:

Common pitfalls of retail demand forecasting

There are some common pitfalls associated with legacy retail demand forecasting methods.

Factoring historical sales data: Using historical data would eventually lead to repeating past mistakes for demand predictions. The global health crisis and current economic volatility have devalued historical figures. It condemned retailers to consider new datasets reflecting factors like cyclicity, seasonality, inflation, geo-political scenarios, price changes, promotions, holidays, and weather patterns.

Lacking granularity in demand decisions: Traditional forecasts aggregate past data to give a surface-level view of demand. But daily patterns, trends, and variations in customer behavior remain untapped. The legacy approach enables decision-making on product categories but not at SKU levels. Hence, decisions about replenishing seasonal stocks stay out of the drill.

A top-down approach to demand planning: Demand predictions are made at the highest level of the supply chain while changes in demand at the granular levels remain unaccounted for. Unfortunately, inventory operations will never improve unless predictions at granular levels are inaccurate.

Bloated safety stocks:  Safety stock in the buffer improves availability and service levels and eliminates chances of out-of-stock situations. Since most companies operate in a system where suppliers’ lead time is longer than buyers’ tolerance time, the buffer stock is a safety valve to provide uninterrupted services. Unfortunately, this condition can lead to locked-up working capital and bloated demand numbers.

No scope for strategic building: Yet another pitfall for traditional retail demand forecasting is the lack of crafting stellar strategies to improve market share and beat the competition. Low levels of data transparency and forecast accuracy further hinder retailers’ ability to identify plausible risks and mitigate them or create a buffer plan to meet a range of outcomes.

How to get started with retail demand forecasting

By utilizing predictive analytics, AI, and ML capabilities, demand forecasting methods for retail have improved manifold, driving the accuracy and reliability of outcomes. Here are a few tips to get you started.

Large-scale data processing: Machine Learning can easily automate planner work and processing of large datasets. Evaluating each data set potentially impacting demand can elevate forecasting accuracy.

Predicting the effects of decisions: Commercial choices like promotions can affect sales volumes immensely. Machine Learning algorithms are leveraged to process large volumes of data demonstrating the impact of promotion and marketing activities, price elasticity and visibility of brands, and impacts of price changes on other products within the same category.

Considering price changes and their impact: Cannibalization or demand shifts occur when product prices are reduced, impacting the demand for goods in the said category. This means demand shifts immediately to cheaper products. To prevent over-ordering of low-priced goods, retailers should adjust forecasts for non-promoted goods and adjust figures in their replenishment plan.

Collaborating with vendors/partners: With a regular data-sharing routine developed between partners and suppliers, retailers can gain an end-to-end overview of how their network operates. Data sharing improves collaboration and provides enough bandwidth to arrange for risk mitigation when a range of issues arise to impact demand signals.

Accounting for new product introductions: New product introductions can challenge sales forecasts because there is no historical data to refer to. But, often, reference products with historical data can serve as a blueprint. AI-enabled demand forecasting tools can automatically select reference data and quickly update SKU forecasts when actual sales patterns emerge.

Evaluating external factors: External factors include those not under anybody’s control. These factors change the direction of demand for goods/services, including weather forecasts, local events, or competitors’ business decisions. Machine Learning can easily automate extracting such factors and intergrade them into your forecast. This reduces forecast errors on the product, group, and location levels.

Key takeaways

Accurate demand forecasting for retail helps reduce risks and supports efficient decision-making for higher profit margins, seamless cash flow, proper allocation of inventories, and scale opportunities for expansion. This way, retailers can reduce lost sales and ensure customer returns while lowering safety stock to avoid spoilage. Further, strategic and operational plans are formulated around accurate forecasting. With the increasing use of new-age technologies, predicting future demand has become more streamlined, data-driven, and accurate, enabling retailers to easily keep up with unprecedented changes. It helps them catch the early demand signals accurately and stay prepared aforehand.

Task Mining vs. Process Mining – How do they differ

Process mining and task mining have been crucial in driving organizations’ digital transformation initiatives to enhance their operations and customer experiences for quite some time now.  By providing a detailed understanding of processes and identifying areas for optimization, these automation solutions enable businesses to make data-driven decisions. As more organizations adopt these technologies, the data mining market will likely set a growth standard for other automated solutions to follow in the industry.

A recent report from Grand View Research suggests that the global process mining software market will exceed USD 7.95 billion by 2028.

Additionally, a report by Gartner says that the majority of companies (by 2024) that may have implemented automation technologies will most likely integrate artificial intelligence (AI), encompassing machine learning (ML), natural language processing (NLP) algorithms, process mining and task mining, and intelligent document processing functionalities. Specifically, around 65% of such organizations will introduce these advanced AI capabilities into their systems.

So, approximately one-fourth (25%) of the world’s businesses will incorporate process mining into their operations to advance toward autonomous business processes.

The rising adoption of task mining and process mining

The increasing adoption of digital transformation and the need to analyze and comprehend business processes has become a significant driving force for the growth of task mining and process mining technologies. Furthermore, the emergence of technologies such as AI and RPA further fuels this trend. In addition, the COVID-19 pandemic has also played a vital role in accelerating the demand for digital and process transformation. Moreover, the integration of process analytics and ERP applications, as well as the surge in the deployment of task-level automation, are contributing significantly to the expansion of the task and process mining market.

Understanding task mining vs. process mining

Task mining and process mining are two different, but complementary technologies used to analyze business processes.

Task mining focuses on identifying and understanding the tasks and activities that comprise a process. It provides a detailed view of the steps involved in a process, including how they are performed, and the tools and applications used. Task mining identifies the areas for automation, improving user experience, and enhancing productivity. It is the practice of recording and analyzing desktop-level event data, including employees’ screen recordings, keystrokes, mouse clicks, and data entries. The captured data is then processed using advanced NLP and optical character recognition (OCR) tools to derive valuable insights and improve business processes.

However, process mining is a data-driven methodology that involves the analysis of event logs to gain insights into how business processes perform. It helps organizations to identify inefficiencies, bottlenecks, and variations in workflows, providing a fact-based approach to process improvement. Thus, process mining provides an overview of the entire process, while task mining focuses on specific tasks. However, when used together, both technologies gain a deeper understanding of business processes and optimize them for better efficiency and productivity.

Six main differences between task mining and process mining

Category Process Mining Task Mining
Focus Entire business processes Specific tasks
Scope Broad analysis of end-to-end processes Narrow analysis of specific tasks
Data Sources Event logs, ERP systems, databases, etc. User interactions, mouse clicks, keyboard strokes, etc.
Granularity High-level process maps with many steps Detailed task flows with specific user interactions
Analysis Identifies bottlenecks, inefficiencies, and compliance issues in end-to-end processes Identifies automation opportunities and suggests improvements in specific tasks
Use Cases Operational improvement, risk management, compliance Process automation, user productivity
Implementation Data preparation and cleansing, process discovery and mapping, analysis, and visualization Software installation and configuration, user data collection, analysis, and visualization

Benefits of task mining and process mining

Task Mining and Process Mining are powerful techniques that provide companies with valuable insights into their business processes. Here are the top five benefits of each solution:

Task Mining Process Mining
Identify inefficiencies Visualize processes
Streamline processes Identify bottlenecks
Improve employee productivity Improve process efficiency
Enhance customer experience Monitor compliance
Increase compliance Make data-driven decisions

Benefits of task mining

Identify inefficiencies: Task mining can help identify inefficiencies in the business process and identify areas where processes can be improved to increase efficiency by analyzing how employees perform their tasks.

Streamline processes: Task mining can help streamline processes by identifying bottlenecks and areas of redundancy, which allows organizations to optimize their operations and remove any unnecessary steps.

Improve employee productivity: By understanding how employees perform their tasks, organizations can identify areas where employees may be struggling and provide them with the necessary support to improve their productivity.

Enhance customer experience: Task mining can help organizations understand how customers interact with their products and services. This allows organizations to improve their offerings and provide a better customer experience.

Increase compliance: By finding out how employees perform their tasks, organizations can ensure that they follow established procedures and comply with regulatory requirements.

Benefits of process mining

Visualize processes: Process mining, by leveraging the process discovery approach, enables organizations to visualize their business processes, making it easier to identify inefficiencies and areas for improvement.

Identify bottlenecks: Process mining can help identify bottlenecks in the business process, allowing organizations to focus on optimizing those areas.

Improve process efficiency: By analyzing business processes, companies can detect areas where processes can be optimized to increase efficiency and reduce costs.

Monitor compliance: Process mining can help organizations monitor compliance with regulatory requirements and ensure that established procedures are followed accordingly.

Make data-driven decisions: By evaluating process data, organizations can make data-driven decisions to improve their business processes, leading to better business outcomes.

How to identify which technique is the best for your business?

To determine whether task or process mining is the best fit for your business, you should consider your specific needs and goals first. This is because task mining focuses on understanding the individual tasks that employees perform and identifying areas for improvement in terms of efficiency, accuracy, and compliance. As a result, it is well-suited for businesses with highly manual, repetitive tasks that can benefit from automation or optimization.

On the other hand, process mining is focused on analyzing the end-to-end processes that drive business outcomes. It can help identify bottlenecks, inefficiencies, and areas for optimization in complex, cross-functional business processes. It is helpful for businesses that are looking to optimize and streamline their operations, improve customer satisfaction, or reduce costs.

Use cases of task mining and process mining

Key task mining use cases include:

Data entry: Task mining automates data entry tasks such as typing in information from physical documents, emails, or other sources into a digital system. By recording and analyzing the steps involved in the data entry process, task mining can identify opportunities for automation, such as using optical character recognition (OCR) or machine learning algorithms to extract and input data automatically.

Invoicing: Task mining can process invoicing by identifying and analyzing the steps in creating and sending invoices. By identifying repetitive tasks such as data entry or formatting, task mining can help to streamline the invoicing process and reduce errors.

Reporting: Task mining automates the reporting process by identifying and analyzing the steps in generating reports. Therefore, by automating recurring tasks such as data collection, formatting, and analysis, task mining can help to speed up the reporting process and improve accuracy.

Reconciliation: Task mining reconciles the process by identifying and analyzing the steps involved in comparing and matching data from different sources. By automating cyclic and tiresome tasks such as data entry and matching, task mining can help to speed up the reconciliation process and reduce errors.

Customer service: Task mining improves customer service by identifying and analyzing the steps involved in responding to customer inquiries or resolving issues. By systematizing repetitive tasks such as data entry or searching for information, task mining can help customer service agents to emphasize more complex or high-value tasks, improving overall efficiency and customer satisfaction.

Process mining purposes

Auditing and Compliance: Process mining analyzes and audits business processes for compliance with regulations, industry standards, and internal policies. By analyzing event logs, process mining can help identify potential compliance violations and prevent them from happening.

Order Management: Process mining can analyze the order management process, from order entry to fulfillment, to identify inefficiencies, bottlenecks, and potential delays. This can help organizations optimize their order management process and improve customer satisfaction.

Purchase-to-Pay: Process mining examines the purchase-to-pay process, from procuring goods or services to payment. By identifying bottlenecks, inefficiencies, and errors in the process, process mining can help organizations streamline the process and reduce costs.

Order-to-Cash: Process mining can analyze the order-to-cash process, from order entry to cash receipt. Process mining can help organizations optimize their order-to-cash process, reduce order fulfillment times, and improve cash flow by identifying potential bottlenecks, inefficiencies, and delays.

Lead-to-Order: Process mining can explore the lead-to-order process, from initial customer contact to order entry. Process mining can help organizations optimize their lead-to-order process and improve customer satisfaction by identifying bottlenecks, inefficiencies, and potential delays.

Logistics: Process mining examines the logistics process, from order fulfillment to delivery. Process mining can help organizations optimize their logistics process and improve delivery times by identifying bottlenecks, inefficiencies, and potential delays.

How can AssistEdge Discover help enterprises?

AssistEdge Discover, a process and task mining platform by EdgeVerve, can help organizations automate their business processes by analyzing employee work patterns. The solution captures employee activity data from various sources, such as desktops, servers, and mobile devices, and uses machine learning algorithms to identify employees’ tasks, applications, and websites.

Following this, the data is then analyzed to identify patterns, inefficiencies, and opportunities for automation. By automating repetitive and time-consuming tasks, AssistEdge Discover helps organizations improve productivity, reduce errors, and free up time for employees to focus on more strategic tasks.

Key takeaways of using the AssistEdge Discover

One perfect example of how AssistEdge Discover  has improved productivity is demonstrated through a case study that highlights the solution’s positive impact on the performance of a significant US-based telecom company. By leveraging AssistEdge, the company was able to gain insights into employee workflow, identify inefficiencies, and optimize its workforce. This led to a 20% improvement in agent productivity, allowing them to handle more customer interactions and increase efficiency.

Download the case study to learn more about how AssistEdge Discover helped a US-based telecom company.

Final thoughts

To recap, task mining and process mining are two robust business process management solutions with distinct approaches and objectives. Task mining captures user interactions with software systems to identify patterns and inefficiencies in task performance. In contrast, process mining focuses on analyzing data from IT systems to gain insights into how business processes get executed.

Adopting task mining and process mining can provide countless benefits for companies looking to optimize their business processes. Task mining and process mining can help companies identify inefficiencies, streamline operations, and make better decisions. As a result, companies can achieve better business outcomes by adopting these techniques, giving them a competitive edge in today’s digital age. Hence, it is highly recommended that companies adopt such solutions as AssistEdge Discover to stay ahead in the market.

Driving success with automated supply chain management process: An all-inclusive overview

The subsequent disruptions, triggered by the COVID-19 pandemic, have created a host of challenges and, significantly increased the complexity of the supply chain management process., including labor and equipment shortages, poor IT infrastructure, climate unpredictability, economic downturn, and geopolitical crises. Additionally, customer buying behavior has changed, with an increased demand for sustainable purchases.

Hence, automated solutions have become necessary to effectively manage the value network amidst these challenges. This requires a connected, agile, and resilient framework that can efficiently manage all activities, from procuring raw materials to delivering finished goods or services to end-users. By implementing such solutions, businesses can easily overcome challenges and maintain a competitive edge in an ever-changing marketplace. This is where, an agile supply chain network that’s more responsive to market disruptions is the need of the hour.

Importance of an efficient supply chain management process

An efficient supply chain management process lays a solid foundation for the economic growth of a region or country. It bridges the gap between material processors, manufacturers, vendors, warehouses, transportation companies, distribution centers, and retailers. To ensure the proper functioning of each of these segments, a reliable information system is needed to provide appropriate management of supply chains via scheduling, sourcing, supplier management, and data analytics.

A tech-enabled data-driven platform for the supply chain can easily break the existing siloes, enabling easy accessibility of real-time data that improves decision-making throughout the network. Data and analytics also help build a resilient and responsive network, benefitting stakeholders in the following ways:

Given its far-reaching impact, businesses today have started prioritizing an efficient supply chain management process as it enables companies to have a resilient network where all the functions are fully optimized, driving revenue and growth and fostering loyalty and competitive advantage for owners. Furthermore, a well-executed supply chain can help align demand with adequate supply, reducing unnecessary waste.

Automated supply chain management – A brief overview

An automated solution can help address supply chains’ current challenges and elevate their responsiveness to market changes. Supply chain automation, therefore, refers to using technology solutions to streamline supply chain processes and executing workflows with minimal human involvement.

It leverages extended capabilities of Artificial Intelligence, like Machine Learning and other digital solutions that automate and scale up each process, ensuring timely and efficient deliveries. It optimizes the whole network for higher efficiency and competitiveness. A few examples of automation in the supply chain management process would be:

Key automation benefits for supply chain management

Automation of manual tasks: Processing invoices, logistics bills, and other documents is manually time-consuming and causes process bottlenecks. With the power of automation, manual tasks are eliminated, saving time and costs for the owners while ensuring a smooth flow of resources in the supply chain. In addition, this allows employees to focus more on productive tasks like prospecting and building customer relationships.

Improvement in network visibility: Visibility and transparency of operations allow employees across different departments and locations to remain on the same page. It prevents chances of missed orders or delivery errors. With shipping automation, customers can easily track the progress of their orders with updated Information.

Enhancement of data accuracy: Automating the supply chain management process helps eliminate data silos and ensures quick access to reliable and accurate data. Data availability in real-time prompts informed decision-making about demand planning and allows the generation of dynamic and shareable reports for future strategy-building.

Betterment of customer service: Automation ensures faster delivery service with competitive fees and updated order information shared with end users. Since customers today are more demanding than earlier, exceeding their expectations has become the key to thriving in the market.

Elimination of human errors: While manually executing tasks, errors caused can ultimately prove costly for the organization, impair the brand’s reputation and remove loyal customers from the business. Thus, automation in the supply chain eliminates human dependency and manual work, thereby arresting plausible risks and averting costly mistakes like order duplication, incorrect entry from inventory or order details, inaccurate entry of customer information, picking or packing wrong items for customer orders, and so on are vital areas for automation in supply chain management.

Many CPG companies are turning to demand planning, which involves forecasting customer demand for a product or service over a period. For illustration, this case study discusses how Consumer Packaged Goods (CPG) companies have successfully implemented demand planning to forecast customer demand, manage costs, and maximize profits. It highlights examples of their strategies, challenges, and results.

Download case study on CPG companies successfully implementing demand planning.

Critical components of automated supply chain management

The supply chain management process comprises five segments: planning, sourcing, inventory, logistics, and returning. And the segments together execute a list of primary goals s mentioned below:

Primary technologies used in supply chain automation

Like the consumer market, the technology market is constantly evolving, with new cutting-edge solutions presenting intelligent ways of fulfilling buyers’ daily and extravagant needs. Automation, Artificial Intelligence, Machine Learning, and IoT are a few such examples that have extensively contributed to how companies conduct their businesses in the digital world. There is a list of others being implemented to revamp the supply chain management process:

Automated sorting and retrieval system: This technology is implemented in warehouses to traditional methods of sorting items around and retrieving them automatically. It automates the warehouse management system to replace the conventional approach with a more streamlined and linear process.

Procurement intelligence: A software solution for sourcing and supplier management enables the availability of the right components in the correct quantity to ensure the continuity of the production and delivery process. Procurement intelligence, including updated market insights, pricing, lead times, and regional and raw material trends, allows owners to make informed decisions pertaining to demand planning, sourcing, storage, and delivery of finished items.

IoT for tracking items: IoT-based tracking devices, as mentioned, are increasingly used to provide a real-time view of items moving along the supply chain network. This improves the visibility of the value network, helps identify bottlenecks in the system, and provides remedies for improvement.

Digital twins: A supply chain digital twin is simply a replication model of the value network created digitally to determine the impact of any physical change of the current reality and predict the outcomes in the real world. This simulation helps businesses to estimate market demand as nearly as possible. In addition, it optimizes supply chain planning and can be tested for impact by evaluating multiple supply chain scenarios.

Automated supply chain management – Challenges and remedies

Now, any new invention is not devoid of limitations and presents numerous challenges for end-users. Automation in a supply chain management process has its fair share.

Automation in the supply chain – Best practices for implementation

Automation in the supply chain management process can drive tremendous value for organizations, their stakeholders, and customers. Market experts believe automation initiatives will eventually lead to supply chain autonomy in 10+ years. Unfortunately, many initial automation projects have failed to keep up with user expectations. Poor planning and implementation strategy can be cited as one of the major reasons for such outcomes. Therefore, adopting a few best practices like the ones mentioned below could help improve the overall performance of a supply chain management process and benefit all connected to the same network.

Better collaboration with stakeholders

A collaborative value network bridges the communication gap between components and enables easy accessibility of crucial market insights in real-time. Information accessibility safeguards stakeholders from sudden market disruptions and assists them in preparing aforehand for an unforeseeable future.

Develop a robust implementation strategy

Simply automating key management processes without thorough network analysis might give birth to further challenges and bottlenecks. Hence, a proper implementation strategy should be in place to enjoy the full potential of a modern software solution and quickly scale it across the network as and when needed.

Analyze critical processes

A thorough analysis of existing processes within the supply chain network should be an integral part of automation implementation strategies. It is impossible to decide on the right automation solution without identifying areas of concern or how each method works. Since a one-size-fits-all approach doesn’t work for every industry, an automation program should align with respective business objectives and network requirements. For example, an automation program for warehouse management in the CPG sector would differ from the apparel industry.

Upskill in-house resources

It is imperative to maintain a team of experts who are well-adept with the new software solution and ensure its seamless performance during task execution. However, hiring software experts is not a cost-effective approach. Instead, owners can focus on training sessions to upskill their existing resources and help them become familiar with the fundamentals.

Create a continuous maintenance framework

Any machine, robot, or software solution needs a continuous monitoring and maintenance framework to ensure smooth functioning. Investments in supply chain management automation do not go easy on the budget. Hence, proper monitoring of the platform and its timely maintenance should ensure higher returns from the investment.

Future of automated supply chain management

New-age technology solutions are considered the primary source of competitive advantage by 61% of supply chain leaders and are means to address their digital transformation needs. Tech-enabled platforms that focus on elevating human decisions, managing assets, updating legacy systems, and unifying the value network are most desired to improve the supply chain management process. Therefore, emerging technologies like automation, AI, deep learning, and IoT have become critical investment areas. Market experts predict that in the next three years, the world will witness an increase in the adoption of digital supply chain technologies like intralogistics intelligent robots in warehouse operations, embedded advanced analytics, and data science in commercial supply chain management. Again, as per predictions, by 2025, nearly 25% of supply chain decisions will be made using an intelligent edge ecosystem.

The final takeaway

Automation in a supply chain management process has assisted businesses in improving their supply chain efficiency through streamlined operations and connected networks. This has reduced unnecessary labor, warehousing, and other overhead costs for organizations and their stakeholders. In addition, automation, AI, and their extended capabilities are the means for their survival in the face of rising competition and increasing market disruptions.

How is AI shaping the future of CPG e-commerce?

Despite its significant presence in the market, the consumer-packaged goods (CPG) industry has strived hard to keep up with the requirements and inclinations of customers due to its reliance on outdated product development and distribution methods. Consequently, this has led to major challenges such as reduced customer loyalty, missed growth opportunities, and increased distribution and marketing expenses in today’s rapidly changing marketplace.

Hence, to address these challenges, the industry must embrace advanced technologies like Artificial Intelligence (AI), which can help personalize customer experiences, respond quickly to market shifts, and optimize resource allocation swiftly. Moreover, businesses can stay competitive and better meet the demands of their customers by applying AI, thereby paving the way for future business growth. Iin such demanding situations, it becomes imperative for companies to adopt the ‘CPG e-commerce’ model urgently by leveraging AI to stay relevant in the competitive business game.

The role of AI in CPG e-commerce

The advent of AI has brought significant transformations to the e-commerce landscape for CPG businesses. Through the utilization of AI, CPG companies can improve their operational efficiency, elevate the satisfaction levels of their customers, and generate greater profits. Backing this, a report by Gartner suggests that by 2023 most organizations using AI for e-commerce will achieve at least a 25% improvement in customer satisfaction, revenue, or cost reduction.

One of the essential advantages of AI in CPG is its ability to provide personalized experiences to customers. AI systems can analyze customer data and behavior to offer personalized product recommendations, promotions, and shopping experiences. This facilitates increased customer satisfaction and loyalty, leading to continued business. Personalization also allows CPG companies to distinguish themselves from their opponent market players and create a loyal customer base.

Moreover, AI is increasingly used in inventory management, which is critical for CPG companies. AI-powered demand forecasting and inventory optimization solutions can help CPG businesses maintain optimal inventory levels, reducing out-of-stock situations and avoiding overstocking. This leads to more efficient supply chain management, reducing costs and boosting profitability. In addition, by optimizing inventory, businesses can also ensure they have the right products in stock, improving customer satisfaction and loyalty. AI is a game-changer in inventory management, enabling enterprises to make data-driven decisions and adapt to changing market conditions.

Why is understanding AI important in shaping the future of CPG e-commerce?

As ‘AI applications in CPG’ continues to transform the industry, the companies that fail to adopt AI-powered solutions risk lagging behind their competitors. Thus, using AI, CPG companies can optimize their supply chain operations, improve customer reaches, and increase sales and value.

In addition to driving innovation and improving business outcomes, understanding AI is also important from an ethical standpoint. As AI becomes more prevalent in the e-commerce industry, there is a growing need for transparency, accountability, and responsible use of these technologies. CPG industries that understand the ethical implications of AI and prioritize the associated practices in their e-commerce operations can build consumer trust and shield their reputation.

Finally, understanding AI is essential for fostering collaboration and innovation in the CPG e-commerce industry. By understanding how AI works and can be applied to e-commerce operations, CPG companies can collaborate with AI developers and other stakeholders to drive innovation and increase the overall customer experience. In addition, by working together to develop and implement AI solutions, CPG firms can stay ahead of the curve and continue to meet their customers’ evolving needs and preferences.

Impact of AI on different modules of CPG e-commerce

The key modules of CPG e-commerce are:

AI is revolutionizing the way CPG businesses operate in e-commerce. By evaluating customer data and behavior, an AI ‘set of rules can provide AI-powered automated customer service tools for instant support, lower response time, and enhanced customer fulfillment.

Regarding supply chain management, AI-powered solutions can predict demand, optimize inventory levels, and reduce waste, leading to more cost-effective supply chain management and increased profitability. In addition, AI-powered product recommendation engines can analyze customer data to deliver personalized product recommendations, boosting the business.

AI-enabled advancements in CPG E-commerce

Chatbots for customer service: Artificial Intelligence has transformed CPG e-commerce by introducing chatbots for customer service, voice-activated ordering, visual search, and personalized social media marketing. These chatbots can provide 24/7 customer support, reduce the need for human agents, and improve customer building and satisfaction.

Voice-activated ordering: Voice-activated ordering has made purchasing more convenient and seamless, leading to increased sales. Moreover, visual search technology makes the search process more intuitive and user-friendly, enhancing customer comfort and growing sales. Finally, these tools can deliver personalized and targeted social media ads, increasing brand awareness and driving more transactions.

These AI-enabled advancements in CPG e-commerce have revolutionized the industry by transforming the functional dynamics of the industry. Hence, CPG companies that adopt these technologies will stay competitive in the market and provide their customers with a seamless, personalized shopping experience. With AI applications in CPG constantly evolving, they will play a crucial role in the CPG e-commerce landscape.

Benefits of AI in CPG e-commerce

A report by Statista says that revenue in the eCommerce market is estimated to reach US$4.11 trillion in 2023 and is expected to reach US$6.35 trillion by 2027.

Here are some of the key benefits:

Limitations of AI in CPG e-commerce

Although the use of AI in the CPG industry garners several benefits, it also comes up with several challenges that require urgent attention from business leaders.

Thus, addressing these challenges will improve the effectiveness and success of AI in CPG e-commerce businesses.

For more ideas, here’s a white paper on how to win in e-commerce channels. This comprehensive information outlines the strategies and tactics that businesses can leverage to succeed in the highly competitive e-commerce world.

Download White Paper on how you can win in e-commerce channels.

Future prospects of AI in CPG e-commerce

Continued growth and development of AI: AI is constantly evolving and improving, and this trend is expected to continue in the CPG e-commerce industry. With the help of AI, CPG companies can optimize their supply chain, personalize customer experiences, and enhance their overall efficiency.

Increased integration of AI in CPG E-commerce: As AI becomes more advanced, we can expect to witness an augmented integration of AI in various aspects of CPG e-commerce, including inventory management, customer service, marketing, and sales. AI-powered chatbots, for instance, can provide personalized product recommendations and customer service, while AI-powered tools can help companies optimize their pricing strategies.

The emergence of new AI applications: With the continued growth and development of AI, we can also expect to see the rise of new AI applications in the CPG e-commerce industry. For example, AI-powered visual search technology can enable customers to find products based on images, while AI-powered voice assistants can provide a hands-free shopping experience.

Need for collaboration and regulation: With the increasing integration of AI in the CPG industry, there is a need for cooperation between CPG companies, e-commerce platforms, and AI developers to ensure that the technology is used ethically and responsibly. Additionally, there is a need for regulation to ensure that AI is not used in a discriminatory or harmful manner. Data privacy, transparency, and accountability regulations are essential for building consumer trust in AI-powered systems.

How can TradeEdge Network help CPG e-commerce firms?

TradeEdge is a cloud-based software solution that helps CPG companies to leverage AI and machine learning to acquire insights into customer behavior, product performance, and supply chain operations. As a result, it enables them to improve their sales and supply chain operations in e-commerce. It offers advanced analytics, data visualization, and decision support capabilities for optimizing sales strategies and supply chain operations, including real-time visibility into inventory levels, demand, shipment tracking, order and inventory management, and promotions management.

Overall, TradeEdge provides CPG companies with a powerful platform for leveraging AI and machine learning to optimize their e-commerce operations. TradeEdge can help CPG companies stay competitive and succeed in the dynamic e-commerce landscape by providing advanced analytics, personalization, inventory optimization, and demand forecasting capabilities.

Final thoughts

The emergence of AI is transforming the e-commerce landscape, enabling companies to access a suite of robust solutions that can streamline business operations, enhance customer satisfaction, and boost profitability.

By leveraging AI-driven solutions, CPG companies can analyze vast amounts of consumer data to gain valuable insights into buying patterns, personalize marketing efforts, and develop products that align more closely with individual customer requirements.

Also, AI-powered supply chain and logistics systems can optimize operations and reduce costs, while virtual assistants and chatbots can improve customer communication and support. As more CPG companies adopt AI, it is becoming increasingly evident that it will play a substantial role in shaping the future of e-commerce in this sector.

Process Mining in healthcare – 5 winning initiatives to enhance patients’ experiences

The healthcare sector has witnessed an exponential surge in the implementation of automation, driven by advanced technologies and software. Consequently, the automation of various tasks, including appointment scheduling, medical record management, and diagnostic testing, has led to digital transformation of the healthcare industry. Thus, automation has proved to be a game-changer, enhancing accuracy and precision in surgical procedures and aiding clinical decision-making.

A report by Vantage Market Research suggests that the worldwide healthcare analytics market is anticipated to increase at a CAGR of USD 89 billion by 2028. However, integrating and interoperability of different systems and technologies, along with concerns associated with the impact of automation on improving healthcare services. This is where the role of process mining in healthcare comes into play.

Challenges faced by employees in the healthcare industry

The healthcare industry confronts several challenges that affect the employees. Out of the many concerns, the shortage of healthcare professionals is perhaps the most significant hiccup which is bothering the industry for a long time. Consequently, this dearth is frequently leading to burnout issues for the employees, thus affecting the quality of services offered to patients. The exhaustion can also result in high turnover rates (employees leaving the organization), which can be costly for businesses. Adding to the woes are the increased workloads and stress levels which lead to medical errors and severely impact the patient’s health and well-being.

Importance of improving healthcare process through advanced technologies

Improving healthcare processes through powerful technologies is crucial for delivering high-quality care, improving patient outcomes, and reducing costs. Machine Learning (ML), Artificial intelligence (AI), and robotics are increasingly used to improve healthcare processes, from diagnosis and treatment to patient monitoring and follow-up care.

One of the most significant benefits of powerful healthcare technologies is their ability to streamline processes and reduce administrative burdens. For example, digital record-keeping and automated billing systems can help reduce paperwork and reduce errors. At the same time, AI algorithms can analyze patients’ data to diagnose more accurately and recommend personalized treatment plans.

Advanced healthcare technologies also help to enhance patients’ safety by minimizing the risk of medical errors., while AI-powered systems can detect potential safety risks and provide real-time alerts to healthcare providers.

Hence, these solutions can easily transform the way healthcare is delivered, making it more efficient, effective, and patient-centric. As the healthcare industry progresses, embracing and integrating these technologies to improve patient processes and outcomes will be increasingly important.

Scaling automation opportunities in healthcare

There are numerous healthcare automation opportunities, including the administrative responsibilities from billing and coding to clinical decision-making and even surgery. To scale automation in healthcare, it is crucial to identify areas where automation can add the most value and develop a clear strategy for implementing and integrating automation technologies.

This involves ensuring interoperability and data sharing between different systems, investing in training and education for healthcare employees, and addressing any ethical and regulatory concerns related to automation.

Moreover, the collaboration between healthcare providers, technology companies, and regulatory bodies is essential to develop standards and protocols for scaling automation in healthcare.

For instance, a renowned hospital chain in the UK collaborated with EdgeVerve and leveraged the power of AssistEdge RPA to optimize one of its primary healthcare operations in real-time by automating the process and eliminating the manual efforts involved.

Check the leading hospital chain case study for more information.

What is process mining, and why is it important in healthcare?

Process mining is a data analysis technique to optimize business processes by identifying inefficiencies, bottlenecks, and variations. It provides several benefits, including improving patient care, reducing costs, and enhancing outcomes by analyzing patient data and clinical workflows. Process mining can also be utilized to detect areas for improvement in care delivery and improve patient flow.

Moreover, process mining enables healthcare organizations to gain insights into their processes and identify opportunities for improvement. As a result, healthcare providers can optimize patient care, reduce costs, and improve outcomes by analyzing process data such as patient records and clinical workflows. It can also be used to comply with regulatory requirements and maintain the quality and safety of patient care.

How does process mining in healthcare work?

An application of process mining in healthcare analyzes process data such as patient records, clinical workflows, and resource utilization to gain insights into how healthcare processes are performed. The process involves the following steps:

Hence, process mining in healthcare provides a data-driven approach to optimize healthcare processes, reduce costs, and enhance patient care.

Five winning initiatives for process mining in healthcare – Key opportunity areas

Patient flow optimization: Process mining can help healthcare organizations optimize the flow of patients through various care settings such as emergency departments, operating rooms, and outpatient clinics. The evaluation of patient data through process mining can aid in the identification of areas of patient flow that suffer from inefficiencies, roadblocks, and potential for enhancement.

Clinical pathway analysis: Clinical pathways are sequences of care that outline the best practices for managing a particular condition or disease. Process mining can help healthcare organizations analyze clinical pathways to identify areas for improvement, such as reducing the length of stay, decreasing readmission rates, and improving patient outcomes.

Supply chain optimization: Process mining can help healthcare organizations optimize their supply chain processes, such as inventory management, procurement, and distribution. Process mining can help identify inefficiencies and bottlenecks by analyzing supply chain data, reducing waste, and improving patient safety.

Billing and coding optimization: Process mining can help healthcare organizations optimize their billing and coding processes, increasing revenue, and reducing compliance risks. Process mining can help identify errors and inefficiencies by analyzing billing and coding data, improving accuracy, and lowering the risk of fraud and abuse.

Quality and safety improvement: Process mining can help healthcare organizations improve the quality and safety of their care processes. Thus, it is capable of detecting regularities and tendencies, pinpointing root causes, and developing targeted interventions to improve patient safety and reduce harm by analyzing data on adverse events, near misses, and other safety incidents.

Process mining offers healthcare organizations a wide range of opportunities to improve their processes, reduce costs, and enhance patient outcomes. By leveraging process mining tools and techniques, healthcare companies can gain deeper insights into their operations, make data-driven decisions, and drive continuous improvement.

Benefits of process mining for healthcare employees

Improved workflow efficiency: By analyzing process data, process mining can identify bottlenecks and inefficiencies in workflows, helping healthcare employees optimize their work and improve the efficiency of their tasks.

Reduced workload: Automation opportunities identified through process mining can help healthcare employees reduce their workload, enabling them to focus on more critical and value-added tasks.

Enhanced patient safety: Process mining can help healthcare employees identify areas for improvement in patient safety, cutting down the medical errors and adverse events.

Improved quality of care: By optimizing healthcare processes, process mining can help healthcare employees provide higher quality care, improving patient outcomes.

Data-driven decision-making: Process mining provides healthcare employees with data-driven insights into their workflows, thus enabling them to make more informed decisions and proactive steps to improve their performance.

Increased productivity: By automating recurring and time-consuming tasks, healthcare employees can increase productivity and save time for more critical tasks.

Thus, process mining can benefit healthcare employees by giving them with insights into their workflows, allowing them to optimize their work and boost patient care’s safety, quality, and efficiency. Hence, it becomes imperative for organizations to opt for ‘best-in-breed’ process mining and mapping solutions such as AssistEdge Discover.

AssistEdge Discover – Driving process intelligence

AssistEdge Discover is a process and task mining platform that aids organizations in their automation efforts by providing critical insights into the process intricacies required to build an efficient automation pattern. This solution enhances the ability to extract the full potential of automation while also expediting the process discovery process by reducing human effort in tasks such as identification and mapping.

Additionally, AssistEdge Discover leverages AI integration to maximize the benefits of automation, ensuring optimal outcomes for business processes. The solution’s transparency eliminates human bias, creating an efficient automation blueprint that boosts the automation success rate. Additionally, more competent exception handling reduces the likelihood of potential errors and pitfalls, increasing predictability.

Process mining – An evolutionary leap toward automation

Process mining can help healthcare providers boost patients’ experiences and optimize healthcare processes to deliver high-quality care services. It offers a visual representation of processes that can help identify incompetence by allowing providers to simplify mapping processes and improve the patient experience. In addition, by analyzing clinical pathway data, healthcare providers can ensure patients receive the best possible treatments. Thus, it will be suitable to acknowledge that process mining provides deep insights that enable data-driven decision-making, leading to continuous improvement in the healthcare industry.