What is inventory management?

Inventory management is a difficult task, especially for businesses with multiple store locations and retailers who sell thousands of products each month. Order mix-ups, dead stock issues, deficient stock situations, and storehouse complaints are all common problems for retail and e-commerce business. For owners of small and medium-sized businesses, inventory management is a top priority. Reducing overstock and out-of-stock situations will be made easier with the help of a system that monitors inventory levels, orders, and transactions to perform predictive analysis and gather forecasted demand.

5 benefits of inventory management using machine learning

• Stock tracking using machine learning

The stock market is largely responsible for being volatile, dynamic, and nonlinear. Because of numerous (macro and micro) factors, including politics, international economic conditions, unforeseen events, a company’s financial performance, and others, accurately predicting stock prices is very difficult.

With machine learning, current data input is used to modify software-generated calculations and estimates. Using it to improve the precision of stock tracking, optimize inventory storage, and provide open communications throughout the supply chain is a way to improve the performance of tracking technology in inventory management by providing more accurate data to facilitate future planning.

• Inventory management optimization

Inventory optimization is the process of keeping the right amount of inventory on hand to meet demand while keeping logistics costs low and avoiding common inventory issues such as stockouts, overstocking, and backorders. Algorithms can be built to fit specialized limitations that work for your business with the help of artificial intelligence

and machine learning. Particularly in companies with numerous distribution centers, this can be used to improve inventory optimization. It proves to be a more efficient way of managing stock.

• Minimize forecasting errors

Machine learning can be used to cut transportation and warehousing costs by keeping inventory at a lean but comfortable level, and it can forecast demand in the near future, allowing stock to be purchased in time for transactions. This improves client delivery times and, as a result, client satisfaction.

• Limiting idle stock

The concern about stock degrees is one of the major factors influencing inventory management. Forecasts for how much stock to carry are frequently inconsistent when based solely on outdated tracking models. Extra and idle stock essentially represents tied-up money that could be put to better use.

If you stock too much, you risk increasing your costs, but if you stock too little, you risk running out of a product entirely. Finding the perfect balance is a difficult task. Reduce stock levels and avoid stockouts by mastering your lead times, automating tasks with inventory management software, calculating reorder points, and using accurate demand forecasting.

• Enhancing customer experience

The success of small and medium-sized businesses depends on maintaining customer satisfaction. A customer who finds the ordering process difficult, cannot get the stock they require, or consistently receives product late is likely to be dissatisfied and look for a new supplier. Let’s have a look at how to improve customer satisfaction:

· Avoid understocking.

· Have an estimate of seasonal demand

· Boost order fulfillment

· Reduce lead times

· Set sustainable pricing

Wrapping up

Technology updates will saturate the world of inventory in the future. To boost sales and draw customers, this industry is constantly evolving, using technologies like virtual reality, artificial intelligence, digital signage, and even stores with no inventory. And it will only get bigger from here. With the advancement of real-time inventory management systems, retailers now have access to more information about consumer demographics, spending patterns, and other factors. Retailers should try to improve their accuracy with their inventory and continue to appeal to their customers with this constant increase in inventory visibility.

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From Industry 1.0 to Industry 4.0, intending to reduce cost and to improve efficiency, organizations have always tried to automate business processes with whatever technology or machinery is available to them. With the widespread adoption of digital technologies, automation has evolved quickly during the last few years. Though traditional automation has been around for quite some time, the introduction & adoption of Robotic process automation (RPA) has taken the automation industry by storm. RPA streamlines & automates business processes that are repetitive and manual-labor intensive – it has transformed human lives, the business ecosystem, and the global economy in an unprecedented way. 

What is Intelligent Automation? 

With advancements in digital technologies such as Artificial Intelligence and Machine Learning, a smarter version of RPA that analyzes the vast amount of data generated, then decodes and learns the business process itself with context, rather than perform a series of rule-based tasks, has paved the way for Intelligent Automation. 

IA combines artificial intelligence and automation to create smart business solutions that can analyze, learn, and adapt to improve the efficiency and productivity of any organization. It leverages natural language processing, computer vision, machine learning, and machine vision to understand and solve complex business challenges.  Intelligent Automation automates any business processes that leads to reduced operational costs, improved efficiency, and enhanced customer experience. 

Let’s explore how Intelligent Automation Solutions are disrupting different industries. 

Healthcare   

 With the implementation of Intelligent Automation solutions, healthcare providers can devote more human resources towards patient care as the mundane & time-consuming tasks are taken care of by IA. The Healthcare industry struggled with the processing of unstructured data involving text, videos, and images – intelligent solutions took over the unstructured data processing and automated tasks such as collecting and interpreting diagnostic results, pharmacy & laboratory operations, etc., in a short time frame with increased efficiency.  Digitization of health records, inventory management, automating unstructured data processing, automating contact center operations, patient journey management, etc., are some of the ways automation has made an impact in healthcare. 

With virtual caregivers, patient management robots powered with speech & facial recognition, and AR/VR technologies, the healthcare industry can offer personalized patient care, ensuring superior patient and employee satisfaction. 

Retail 

Artificial intelligence infused into Robotic Process Automation (RPA) has enabled retailers to offer personalized offers to each customer. Intelligent Automation solutions analyze the massive amount of data generated via various customer touchpoints to create a seamless and error-free shopping experience for their customers. Assistive body measurement solutions by leveraging cutting-edge Artificial Intelligence & computer vision technologies to automate and fast-track body measurement and apparel-size recommendations for their customers accurately and efficiently is one of the applications of IA in retail industry.  

Pricing analytics, a sub-discipline of Intelligent Automation, helps retailers adjust prices based on customer’s intent and real-time market conditions. It can also gather customer sentiments from various social media platforms and allows retailers to make informed decisions. IA significantly improves the retailer’s operational efficiency, reduces costs, and ensures superior customer experience. 

FinTech 

FinTech organizations embraced Intelligent Automation solutions to automate almost every aspect of their business process. Combining technologies such as Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision, etc., Intelligent Automation enables FinTech providers to offer cognitive agents such as virtual assistants and chatbots. These cognitive agents are capable of learning complex structured and unstructured data to make logical decisions and have disrupted the way customers engage with financial institutions. 

Intelligent Automation solutions help wealth management organizations analyze the vast amount of available data to understand stocks’ historical performance, make more accurate predictions in the future, and help gauge market movements in near real-time. These solutions can also help FinTech providers to meet regulatory compliances and standards as they reduce the risk of human errors significantly.  

Transportation & Logistics 

In recent times, automation in transportation industry has made significant advancements. Intelligent Automation solutions enabled the highly fragmented and complex Transportation & Logistics industry to become more efficient and transparent with fewer resources – autonomous trucks, driverless cars, and drone taxis are some of the most exciting intelligent automation applications within the transportation & logistics industry. 

With the widespread adoption of digitization, the customer’s logistics & transportation expectations have changed enormously. Intelligent Automation solution providers help organizations streamline & optimize their business processes such as transportation planning, route planning, warehouse network management, and inventory management to meet the rising and fluctuating market demands. Driven by insights and predictions, IA solutions help organizations be more efficient, meet customer demands, and make better faster decisions.  

In Conclusion 

Intelligent Automation solutions are designed to address complex business problems, simplify business processes, reduce costs and improve efficiency. With increased market demand and dynamic customer expectations, if businesses don’t automate everything they can, they may not survive.

At Black Rock IT Solutions, we understand the intelligent automation ecosystem and can help build customized solutions for organizations across industries. To know more about Experion’s automation capabilities and how we can support your journey towards automation, contact us at sales@blackrockdxb.com   

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It is common for us to look back into our past and then form corrective actions to adopt in the present. But that usually isn’t enough to satiate the human mind – concerns about the future usually leave us in a state of constant worry and pressure. And so, throughout history, people have relied on various methods for predicting the future, trying to be as prepared for the future as they can be.  Corporates are no different. Regardless of the business functions (Strategy, Marketing & Sales, Operations, or HR), predictability of the future is one of the most sought out capabilities. Such predictions of ‘probable outcomes/situations in future’ help businesses to be proactive and devise strategic plans to mitigate risks, prevent undesirable outcomes, realize greater sales, improved profits, and deliver greater customer delight. Of course, these predictions need to be highly objective and quantifiable, as they often have immediate implications in the way a business operates in the present.  

So, the obvious question now is ‘How do we make predictions that are objective, with a quantifiable trustworthiness attached to it?’. Predictive Analytics can help businesses with future-state predictions based on past, historic data related to their business use cases.

What is Predictive Analytics?

Predictive Analytics is the application of advanced analytics techniques to predict the outcomes of future events. Data scientists usually build these models by bringing together relevant data engineering practices, statistical models, artificial intelligence, and machine learning including deep learning techniques as required. This model is then run against all available historical data, which forms the foundation for the predictive model. The model then identifies hidden patterns and relationships between attributes in the data, learning to predict future outcomes based on the data. The variability of identified patterns in historic data would help the model to quantify the ‘prediction accuracy’ level. This accuracy level specified by the model could then be used as the benchmark for trustworthiness – but do remember that accuracy could also be an indication of data quality or data insufficiency. 

Predictive Analytics is finding an increased usage among businesses world-over, to utilize ‘big data’ irrespective of it being ‘structured’ or ‘unstructured’, numeric or text, and real-time or non-real-time.  The beauty of the approach is that predictive analytics models are also capable of performing ‘continuous learning’ and improving prediction accuracy by themselves, by making use of incoming, incremental data across time. 

Predictive Analytics – Opportunities to be Proactive

Predictive Analytics can be applied in various industries, across multiple functions of the business, and in numerous use cases. All these applications are opportunities for business teams to be proactive and act in advance to capitalize on opportunities & avoid any undesirable events. 

Here are some of the use cases for predictive analytics: –

1. Predictive Maintenance – One of the ways to improve profits, other than an increase in sales, is by controlling costs efficiently. For organizations that have a large number of machinery operated all-around the year, or are generally heavy on their usage of assets, it becomes highly important to track the efficiency of these assets and prevent equipment downtimes. Using Predictive analytics models, we can now predict the ‘need’ for maintenance, any deviant behavior, part replacement, etc. for every machine across the whole outlay. 

2. Customer Relationship Management – Customer Relationship Management is a critical function for any business, helping with customer retention and customer loyalty, thus making a direct impact on sales, revenue, and profits.  Predictive Analytics has always been finding an ever-increasing adoption in a variety of use cases in CRM, such as targeted marketing, churn prediction, lifetime value analysis, etc.

3. Quality Management  – Quality Management always has the risk of being viewed as a cost, but if you look closely it is a great enabler for better business outcomes. Effective quality management can decrease unnecessary expenses and also increase sales, revenue & profits. Both manufacturing & services sectors would benefit from getting insights on any potential quality issues in advance. Using Predictive Analytics, we can get insights into any threats to quality and thus be more efficient, prevent potential damage to brand value, etc.

4. Fraud Prevention & Risk Management – In Insurance & Banking, fraud in various forms is a concern that has been growing at an alarming pace. On top of this, they face a constant challenge of staying on top of risks related to customer behavior, competition, natural and man-made catastrophes, etc., which has only increased manifold especially in the wake of increased digital adoption, developing markets, and socio-environmental challenges. For all these various scenarios, Predictive Analytics helps to develop a better understanding of potentially hidden threats and opportunities ahead of time.

Of course, none of these are new approaches to data and analytics. Most big brands in the world have been doing this for many years – even for as long as a decade! What is important to realize is that with the advances in analytics and cloud computing, even the ‘kick-starters’ of the world can make use of the opportunities that predictive analytics presents.  

Analytics & Cloud Computing – A Perfect Partnership

Cloud Computing is already more than just a buzzword – most industries have already started reaping the benefits of cloud adoption and today, the technology world sees more and more products & services being built on the foundations of the cloud. Greater processing power and storage capability that can be scaled up and down on a need-basis in the cloud, in contrast with on-premise infrastructure is much more business-friendly. Cloud computing helps industries to work with larger volumes of data in real-time, thus setting the foundation for increased usage of data analytics, especially big data and predictive analytics. 

As per Gartner estimates in June 2020, by 2022 public cloud services will be essential for 90% of data and analytics innovation. Cloud computing can help to overcome constraints related to data volume, speed, etc., and hence can empower industries to reap huge benefits out of their predictive analytics & big data analytics models. Using these cloud computing platforms, data science teams not only develop & deploy custom analytics models in the cloud but also choose from a wide variety of “off-the-shelf” SaaS analytics offerings to suit their business needs.  

The Future

Digital adoption has surpassed the inflection point in a hockey stick growth curve and is growing at an ever-increasing pace. Well for one, if any businesses were hesitant to get their feet wet, COVID-19 has given them the extra boost to jump head-first! 

With all this digital transformation, remember – more data is being generated at every digital touchpoint in every second across the world. As per a Forbes report in 2018, more than 90% of the world’s data has been generated in the last two years. Information is considered to be the most valuable asset and resource by economists. Analytics helps businesses get ahead of their competition, contribute to the growth of the economy and society in general. In short, one can even say Predictive Analytics will empower the world to be better prepared & geared up for forthcoming days. 

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With advancements in technologies, availability of real-time data and analytics, retail industry will be transformed to meet consumer preferences as they evolve. Unlike the old days, the consumer has access to the product or service information and different purchase options. Online purchases have elevated the customer experience with more personalization. According to IDC Research, by 2020, at least 55% of all organizations will become digital with new business models and digitally enabled products and services.

To remain competitive in this rapidly changing industry, retailers must leverage the latest digital technologies and transform themselves to meet the changing consumer behavior. They must leverage digital transformation solutions and create a new business model by placing the customer in the anchor position.

What is digital transformation?

According to SalesForce, Digital transformation is using digital technologies to create new — or modify existing — business processes, culture, and customer experiences to meet changing business and market requirements. This reimagining of business in the digital age is digital transformation.  In retail, it is nothing but moving from a product-centric approach to a customer-centric approach by extending hyper-personalized customer experience.

What are the challenges retailers face in their digital transformation journey?

Multi-channel buying experiences:

With the staggering growth in e-commerce, in-store purchases have come down. A good majority of consumers still prefer retail shops as they want to see, touch, feel, and try the product before they make a purchase. They may gather product information online and make an offline purchase or try the product and order online. This shows that consumers prefer a combination of both. Retailers who try to bridge the gap between online and offline and go ‘phygital’, stands a better chance to win the customer. Retailers must leverage the latest technologies to create an omnichannel customer experience, which enables them to extend personalized offers across channels.

Demand for seamless, superior customer experience:

A seamless customer experience allows both online and offline stores to complement each other rather than compete to keep customers coming back for more. Customers expect in-store retailers to keep track of their online purchases and treat them as loyal customers when they visit the store. Retailers must use advanced analytics for capturing customer data and produce meaningful insights to create an integrated customer experience.

Customer loyalty is driven by experience:

Customer experience fuels customer loyalty than just product features. Even if the retailers offer superior products, the customer is delighted only if they provide him with a superior experience. Though pricing offers and promotions are building blocks of customer experience, hyper-personalization is the critical factor. Retailers who can predict customer behavior, anticipate their needs, and extend personalized offers exclusively for them via their preferred channel creates a profoundly loyal customer.

Key elements of digital transformation strategies in the retail industry

In-store experience

Ensuring better customer engagement and rich in-store experiences enable retailers to connect with customers through all touch points and influence buying behavior. They can leverage beacons, IoT devices, and smart shelves to delight customers with in-store navigation, real-time product pricing, and offers.

Optimize consumer journey

Optimizing end-to-end customer experience across the customer lifecycle journey plays a crucial role in mitigating customer churn and ensuring customer loyalty. Retailers must leverage data analytics technologies to understand customer behavior and preferences to provide tailor-made personalized offers or product recommendations in each stage of the customer lifecycle journey.

Advanced payment solutions

Frictionless payments or digital payment terminals enable consumers to move from cashless to cardless, significantly reducing transaction times.

Customer service

Advancement in artificial intelligence and machine learning technologies has changed customer care interaction by automating routine services which don’t require human intervention. Using a digital bot powered by AI & ML technologies enable retailers to answer standard questions, handle returns, help customers with product specification queries, etc. efficiently and thus improving the customer experience.

Digital transformation is key to success for retailers, perform or perish

Hyper-personalized customer experience stands tall in this digital economy. To win the modern customer, retailers must create a superior shopping experience that sets them apart from the competition. They must establish an exceptional omnichannel experience irrespective of the channels to win retail customers.

blackrock partnered with one of the leading retail chains in Australia for transforming their legacy systems to match the changing business scenario. Our solution enabled them to reduce the legacy maintenance costs, and it leads to a 30% increase in customer response time. To know more about our Digital Transformation services for the retail industry, drop a mail to sales@blackrockdxb.com

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