How to create a secure RPA implementation program?

Enterprises are steadily increasing their automation footprint and deriving more and more business value by implementing Robotic Process Automation (RPA). In order to survive in today’s competitive world, they are shifting toward combining traditional RPA with Artificial Intelligence and Machine Learning technologies, otherwise called Intelligent Automation, to enable enterprise-wide transformation.

Although the execution of RPA and new technologies has numerous advantages, it brings many security risks.   The significant risks linked with the RPA solution can be analyzed from two aspects. They are:

Compliance risks: Poor RPA governance is generated by various execution methods which evade recognized software development lifecycles and ideal practices that encounter data privacy, enterprise architecture, and network security.

Operational risks:  Operational risks comprise regulatory preparedness toward launching barriers. The dominant factor responsible for compliance and operation is the patchy tactic that countless firms apply when establishing many of these automation programs.

There are some security concerns revolving around the implementation of RPA. Therefore, the question arises, how secure is RPA implementation in business processes?

What should businesses do to check RPA security threats?

To curtail these risks, business and IT leaders should work in unison and choose the ideal RPA solution. Additionally, IT leaders should appropriately operate and design a Center of Excellence (CoE) that can support a standard where IT tackles regulatory concerns, networks, and data.

In contrast, business leaders must focus more on classifying areas and best use cases of RPA within the organization. Hence, RPA can successfully contribute to maintaining daily operations while developing programmers’ skills and maintaining implemented RPA bots.

Steps to create a secure RPA implementation program

Creating proper security measures is significant for executing a company’s Robotic Process Automation solution effectively. Hence, an apt security framework and governance are needed to nullify these security risks.

Below are some of the security practices which organizations can undertake to address these security lapses:

Conclusion

RPA automation security cannot be ignored. For a successful automation program to happen without any friction, a strong governance process should be in place, gearing up organizations to overcome security challenges and securing automation investments.

How RPA is changing the global manufacturing value chain

Robotics in the manufacturing industry is no longer restricted to physical robots performing repetitive tasks such as assembling parts and packaging goods on the factory floor. Instead, it has taken a new shape in the form of Robotic Process Automation bots. RPA in manufacturing refers to software bots working on automating recurring backend or frontend processes without disrupting the general workflows.

What is Robotic Process Automation, and how does it help the manufacturing sector?

Robotic Process Automation or RPA is a software technology that helps automate repetitive, rule-based, time- and labor-intensive workflows. However, unlike humans, these bots can handle such recurring tasks without downtime or errors. When employees are free of such mundane work, they can have more time to contribute to value-added services that require their skills and expertise, while the bots take care of lesser valued but mandatory workflows on their behalf.

Robotic Process Automation tools in the manufacturing sector cater to increasing operational efficiency and take it to the next level, thereby reducing human involvement in the processes as much as possible. RPA in manufacturing helps owners optimally utilize their resources, time, and budget while improving the company’s overall productivity without errors, delays, or downtime. It is seen as the key enabler for digital transformation as it automates and digitizes important operational areas, from invoice processing to accounts payable process automation and supply chain automation to improve agility, speed, and quality of all the processes.

RPA in the manufacturing industry: Impact and use cases

A few use cases of RPA in manufacturing include:

Logistics: RPA automates the entire logistics process, from assessing carriers, validating insurance options, and determining the optimal balance between transport time and cost to monitoring the transportation of raw materials and finished goods. Further, it offers real-time freight tracking and reporting.

Demand planning: Collating data from various sources, including market trends, internal sales forecasts, competitive analysis, and product catalog information, to accurately predict future demand and sales.

Bills of materials: The Bill of Materials, a critical document for manufacturers, is a time and labor-intensive task, but with the help of RPA bots, owners can easily and quickly determine what to purchase and where to make those purchases.

Onboarding new suppliers: Again, onboarding new suppliers categorically assesses each supplier. RPA automates collecting and consolidating this information to analyze proposals needed during negotiations.

How does RPA in manufacturing improve the value chain?

A manufacturing firm’s value chain comprises a network of activities designed to deliver value to end-users. As mentioned, it involves critical processes such as logistics, procurement, accounts receivables and payables, supply and demand planning, order management, inventory management, customer service, and regulatory compliance.

The benefits of RPA in manufacturing while automating these use cases are many, including:

RPA in manufacturing is changing how the industry used to function before technology solutions like AI, Automation, ML, and others penetrated the sector. Even though efforts are being made to imbibe the sector with the latest technological advancements, RPA has made the processes more efficient, streamlined, and fully optimized by automating recurring tasks and core activities.

Robotic Process Automation — Benefits and requirements for a successful implementation

With digital transformation growing at an unprecedented pace, automation has already been making inroads across all industries and domains. Against this backdrop, the demand for Robotic Process Automation (RPA) solutions has sky-rocked in recent times, with the companies compelled to implement it to gain an edge over the competition. With RPA getting a lot of consideration in recent times, it has taken a leap forward to transform the dynamics of business operations for its capabilities to lessen the cycle time, boost accuracy and productivity with better regulatory compliance. Hence, let’s understand the concept of RPA, its benefits, and the key points to a successful implementation in the future.

What is RPA?

Robotic Process Automation helps enterprises automate manual business procedures, so that the employees can invest more time in value addition tasks rather than tedious ones.

Key benefits of using RPA:

Requirements for organizations to successfully implement RPA

Strategic planning

For RPA realization in an establishment, it is vital to understand the goals to be fulfilled. Also, the procedures applied should be coherent. The RPA operation requires a proper plan for creating change management that will define the roles of the employees around RPA implementation. Hence, a thorough plan can help the company focus on issues before the RPA is executed.

Preparing the employees

Following a proper plan, the company should prepare the employees to endure the pressure of the introduction of automation solution. In line with this, educating and training them well may help the company and the employee achieve accurate estimated outcomes.

Create logical ROI expectations

Determining ROI and time consumed for its accomplishment is the most demanding thing to do for RPA application. Thus, this step would necessitate careful administration, understanding of omissions, and seasonality.

Identifying process that requires automation

This is one of the most important roadmaps for productive RPA implementation. In this stage, a business realizes the processes that can be automated. This is where Process Discovery steps in. This helps organizations successfully implement RPA and scale their automation journey.

Collaborating with IT and business teams

After the identification process, establishments must form relevant teams which should contain people from the group’s IT and business departments. The team members must work in accord to successfully implement and use RPA.

Conclusion

As there are many benefits of executing Robotic Process Automation such as AssistEdge RPA  in business processes, it is the ideal time for the organizations to adopt the RPA solution for a flawless, reliable, and swifter way out to not only reduce the cycle time but also to heighten productivity and thereby capture the attention of their targeted customers.

However, the operation of RPA can be tough and complex if it is used without proper planning and allotment of solutions and other reserves. Hence, considering the all-round advantages RPA powers, the application of a proper automated solution will surely lead to faster, more secure, and more dependable business services in the coming times.

Intelligent Automation reshaping end-to-end business processes

Many enterprises have leveraged Robotic Process Automation (RPA) to automate repetitive, rule-based business processes across various processes. This helps free the human workforce from mundane tasks and focus on higher-level activities that require more strategic work and thinking. However, RPA has certain shortcomings when implemented in more complex automated solutions.

With many technologies evolving and reshaping the end-to-end business processes across industries in recent times, enterprises are looking for ways to be more strategic with automation. It’s time enterprises go beyond RPA and automate more of the end-to-end processes.

This is where the role of Intelligent Automation (IA)comes into play.

Going beyond RPA — A journey towards Intelligent Automation

Intelligent Automation is increasingly becoming a core part of larger transformation programs. Bringing intelligence to an enterprise’s automation program enables end-to-end automation and opens new possibilities.

Intelligent Automation combines AI and Automation to perfect high-functioning responsibilities that demand reasoning, judgment, decision-making, and analysis. It gives employees more time to invest in value addition tasks like having conversations and making connections with customers. Intelligent Automation, also known as hyper-automation, is increasingly redesigning the functional dynamics of organizational digitization programs.

The need for Intelligent Automation — Reshaping end-to-end business processes

As enterprises move along the automation maturity curve, many barriers keep them from adopting automation at scale, from failure to identify the right processes to automate to lack of IT readiness. With IA and comprehensive models, it’s not surprising that about 50% of enterprises see the value of end-to-end automation tools to achieve meaningful process optimization, according to a survey report by EdgeVerve and SSON.

Enterprises that remained focused on a single department and deployed tactical automation in finite use cases are now keen to implement IA as a strategic enabler of end-to-end automation.

Here are a few examples:

Automotive

By applying IA, the automotive industry can forecast and adjust production as per the changes in supply and demand. Intelligent Automation helps customize the workflows to enhance efficacy and minimize the errors in procurement, production, support, and other domains without the intervention of human force.

Pharma/Life Sciences

Drug production needs precise calibration of equipment and measurement of product a tremendous amount of data collection, collation, processing, and analysis. Drug trials are not considered productive without any analysis and tested results. As a manual approach is likely to make calculation blunders, it is advisable to implement Intelligent Automation to boost speed, quality, and production.

Healthcare

The healthcare industry is another example of using Intelligent Automation with natural language processing (NLP) to support a consistent approach for data collection, analysis, diagnosis, and medication. The application of chatbots in remote healthcare appointments necessitates less human intervention and often a shorter time for analysis.

Insurance

Intelligent Automation in the insurance industry can virtually wipe out the requirement for labor-intensive rate calculations and can streamline the paperwork processing, such as claims and appraisals. Additionally, IA facilitates insurance firms to adhere to compliance regulations effortlessly by guaranteeing that requirements are met.

Employee onboarding and offboarding

Onboarding and offboarding require constant hours of employee labor. While the paperwork, payment processing, training, and obtaining resignation letters are all simple tasks, they can be tiresome and time-consuming. Nevertheless, these processes can be sleeked and completed in a timely manner with Intelligent Automation.

Future of Intelligent Automation

With its ability to automate complex processes, Intelligent Automation solutions will lead businesses toward more adaptive processes that assist enterprises in unearthing bigger ROI. Hence, modern enterprises must exploit this disruptive technology to its fullest potential with an eye on more complex use cases.

10 reasons why you need a Distributor Management System

Due to various operational distribution complexities in an extensive supply chain market, it is a huge task to monitor and manage thousands of distributors and retailers. Handling all these operational intricacies manually requires humongous efforts. Even though the distribution workflow seems to be an easy one, it needs some effective planning for a seamless flow of supply chain operations. Therefore, an all-inclusive and comprehensive distribution management system is the need of the hour for the organizations involved in the distribution supply chain.

What is a Distributor Management System (DMS)?

Distributor Management System (DMS) helps enterprises manage the supply chain and distribution network efficiently – from streamlining inventory to providing complete visibility of secondary sales data.

Here are some reasons why you need a Distributor Management System for your business:

  1. Optimum utilization of resources: The most significant advantage of an efficient distributor management system is that it optimizes the use of the existing resources. In addition, DMS, a time-efficient solution, also enables a swift and seamless flow of operations. Therefore, it is fair to acknowledge that efficient order management, delivery, return process, and payment collection system assisted by powerful reporting feature plays a significant role in achieving the targets and planned growth.
  2. Accurate inventory management:  With a DMS and access to real-time data, enterprises can bod goodbye to ‘out-of-stock’ scenarios and inventory challenges.
  3. Effective order management: Helps streamline and simplify order management on the buy-side and the sell-side, providing real-time ordering information across the supply chain. Well-aligned
  4. Promotions and pricing: With real-time information at hand, enterprises can better manage promotions and set up custom promotions and pricing. Besides this, they can set attainable targets to encourage better results from dealers and sales teams.
  5. Provides real-time visibility of secondary sales data: A robust DMS helps enterprises obtain data of all distribution channels in real-time that can be converted into actionable insights to handle stock-out scenarios, better plan promotions, and drive sales growth.
  6. Seamless delivery and returns management: Delivery is the most crucial stage of the supply chain process because there must be constant communication between the distributors, warehouse, retailers, and the sales team. Hence, an efficient Distribution Management System is required.
  7. Common communication platform for all parties involved: For streamlined communication, all the parties involved in the sales and the supply teams can coordinate without hiccups on a common platform.
  8. Well-ordered payment collection: Undeniably, a streamlined dealer Distributor Management System will certainly benefit a business that will lead to profitable revenue generation. A Distributor Management System creates due alerts, invoice generation, payment collection, and simple and coordinated reminders.
  9. Effortless management of distributor claims: By implementing an all-inclusive Distribution Management System, organizations can manage claims easily and enhance the connection with distributors.
  10. Improved targets & effective reporting: By executing an efficient distribution and supply chain management system, an organization can clearly understand the sales team’s performance and set realistic yet challenging targets to increase revenues.

Hence, a Distributor Management System (DMS) plays a key role in managing the supply chain, providing real-time access to data, and enabling enterprises to stay ahead of the competition.

The six different ways to improve forecast accuracy using demand sensing

The global supply chain network has become increasingly complex, and so also the rapidly changing consumer behavior. These factors have compounded to render traditional demand planning techniques ineffective and inadequate. Demand planners need more visibility into their demand-supply value chain and quality demand signals in order to achieve a perfect balance between surplus and adequate inventories. Moreover, keeping a close tab on external factors like global emergencies, weather trends, and economic trends is equally important to understand the change in market demand for goods/services. Hence, an intelligent demand-sensing solution has become the need of the hour for demand planners and others in the value chain to drive business growth and customer engagement.

What is demand sensing, and how does it differ from traditional techniques?

Demand sensing leverages AI/ML technologies to gather real-time data that help predict future sales in a shorter period. It helps pick short-term market trends to estimate what consumers will want, when, and where immediately.

For example, traditional demand planning techniques could have effectively planned long-term demand and sales by considering historical data. But, if companies had used demand sensing technology, they would have immediately captured the silent hints of upcoming disruptions months before the COVID virus took the shape of the global pandemic. This unpreparedness, lack of foresightedness, absence of the right tech-based tools, and obvious siloes existing in the global supply chain were responsible for companies remaining misinformed about what would happen until the worst happened. Most organizations struggled to keep the shelves of their retail and distribution centers stocked up amidst the pandemic-fueled panic-buying that went on in full blaze just before the complete lockdown norms were officially made known to the general public.

And the aftershocks of the pandemic are still being felt deep down in the supply chain networks. Demand sensing in the supply chain breaks those existing siloes and improves visibility into the network by facilitating a seamless supply of real-time insights so actionable decisions can be made effectively regarding predicting future demand and sales. There are reasons why demand sensing as a forecasting method is gathering more preferences than its predecessor. Let’s elucidate the differences between these methods in brief.

Demand sensing: Improving forecast accuracy with data

There are different ways to improve forecast accuracy with demand sensing; a few of which are elucidated below:

Use PoS data

Point of sale data at retail stores is ideal for providing real-time information like the quantity of products sold, date and time, region, and so on. PoS inputs are consolidated and compared with short-term forecasts to identify deviation and used for predicting product demand for the immediate future.

Analyze order history

While PoS inputs capture demand signals at the consumer end of the supply chain, historical data like sales history, open order history, and currently available orders are used to estimate manufacturers’ product demand. This information is valuable when planning for inventory buffers.

Track macroeconomic indicators

Macroeconomic indicators such as gross domestic product, the overall stock market, employment data, and housing sales data point to a change in consumer demand. Decisions are made based on such insights to re-adjust the short-term forecast to avoid excess inventories.

Consider competitor promotional offers

Demand sensing also tracks competitor movements to adjust short-term forecasts. Sudden last-minute promotional offers or discounts from competitors can change the game for your business, leaving no time to react or amend such situations. Marketers can predict the impact of such factors on sales and adjust forecasts accordingly. Hence, keeping a close watch over competitors is crucial for maintaining a competitive edge in the market.

Take advantage of competitor stock-outs

It is highly possible that your competitors can suddenly face stock-out situations due to the unavailability of raw materials, quality problems, or production breakdowns. Businesses can take advantage of the situation to capture the competitor’s market and re-position inventories to meet the unexpected demand by increasing production for a while. They often try to shift direction to alternative products available on the shelf.

Adjust forecasts as per weather patterns

Unlike traditional forecasting methods, demand sensing considers weather patterns, seasonal variations, and natural calamities to adjust short-term sales forecasts, raw material procurement, production plan, and distribution plans to meet end-user demand.

Conclusion

Demand sensing is currently the best bet for businesses to catch up with increasing market volatility, shorter product lifecycles, frequent changes in buying behaviors, and external factors influencing the demand for goods. With the help of AI, ML, and Automation, demand sensing solutions like

TradeEdge Demand Sensing provides actionable insights by aggregating real-time data about product sales and inventory across multiple channels. They provide granular visibility into network-wide sales and inventory, helping businesses know what is selling, where and at what speed.

How AssistEdge RPA 19.0 helped a logistics provider scale its automation program

Business processes, people, and data are the bedrock of how logistics companies operate and compete in the increasingly saturated market. In order for them to continue to do so, they need a competitive edge beyond what Robotic Process Automation bots can accommodate. Simple RPA bots deployment can only handle recurring tasks, but scaling automation and implementing it in other critical business areas needs something beyond what RPA bots can handle. To achieve end-to-end process automation, companies need to scale automation.

Hence, to open new horizons of transformation, logistics companies need to take their automation program to the next level and connect the very foundation of processes, data, and people.

This is where Connected Automation comes in. Connected Automation is the foundation for Intelligent Automation empowering enterprises to connect data, processes, and people,  on a single, unified platform.

Powering Connected Automation with AssistEdge RPA 19.0

For instance, a leading logistics provider worldwide, with operations in over 200 countries, handles millions of packages daily, from pick-up to delivery. The client wanted to orchestrate its end-to-end process automation, democratize automation with low code/no-code tools, and give their employees the power to automate everyday tasks. In order for the automation program to reach the next level, the company was ready to migrate 200+ live bots across 50 processes to AssistEdge 19.0 with minimal downtime and no adverse impact on business KPIs.

The flexible, API-first and open architecture of AssistEdge 19.0 offers advanced security and reliability and optimizes the total cost of ownership. AssistEdge 19.0 Migration Workbench’s easy 4-step migration process helped complete the migration process in record time, automating the routing process for domestic and international package returns, with the most negligible impact on business continuity.

Benefits delivered to the client:

Download the case study to read more.

AssistEdge, a cohesive automation platform by EdgeVerve, enables enterprises to scale in their automation journey. With a comprehensive suite of products, businesses can initiate process discovery, intelligent automation, and digital workforce orchestration. Connected automation helps enterprises unlock value through reduced service time, faster sales cycles, better resource allocation, accelerated revenue recognition, and improved efficiency.

How AI-driven demand sensing is the future of supply chain

The phrase building resilient supply chains has been a part of the business lexicon for decades. But the COVID pandemic brought the spotlight on the vulnerabilities of the global supply chain network and made it front-page news. Supply chains have become more complex, interconnected, and global than ever before. And traditional demand planning approaches have been proved inefficient in predicting sudden disruptions anywhere in the network. In order for businesses to build a resilient supply chain and stay future-ready always, they need to catch the demand signals as early as possible. AI-powered demand sensing is precisely what companies need today.

Importance of data in accessing real-time demand signals

AI-powered demand sensing thrives in the presence of data. Data is essential for real-time demand signals, minimizing out-of-stock situations, and preventing lost sales. However, due to persistent data challenges, businesses often struggle to meet the data needs for demand planning.

Four key challenges when it comes to demand planning data needs

There are four key challenges we see when it comes to demand planning data needs, such as: –

Demand sensing — A supply chain game-changer

According to a report by Reuters, nearly 37% of companies reported poor visibility due to internal siloes, a lack of data-driven information, and an over-reliance on their internal data. These factors impaired their ability to detect emerging threats deep down in their demand-supply network.

These numbers should not worry companies since they have all seen the worst side effect of faulty demand signal capturing measures. Hence, the question remains – how to capture the rapidly evolving demand-supply dynamics on time and how to do it right?

Intelligent demand sensing powered by AI technology is the only solution for all the problems mentioned earlier.

Hence, in order for an AI-powered demand sensing solution to work well, there should be unhindered accessibility of data. Since AI thrives in the presence of data, any silo existing can impair the actionable decision-making pertaining to demand predictions.

For instance, TradeEdge Demand Sensing aggregates real-time data about product sales and inventory across multiple channels and provides actionable insights. This provides granular visibility into network-wide sales and inventory, helping businesses understand what is selling, where and at what speed.

How is AI redefining the future of demand sensing?

Traditionally, predicting future sales was simply done by considering historical sales statistics. It is born out of the belief that history repeats itself continuously. But, the same approach was negated when the pandemic showed not everything follows the same pattern and that demand can change dramatically by unprecedented triggers.

AI demand sensing with capabilities like Deep Learning and Machine Learning helps improve forecast reliability and creates an agile and responsive supply chain network.

Benefits of demand sensing:

The bottom line is…

Demand sensing can be the best bet when markets are becoming more volatile than ever, and customer demands change at lightning speed. And, with more external factors impacting the global demand-supply value chain more frequently, creating a cascading effect downstream, it is better to catch those early signals to stay future-ready.

How demand sensing helps in better inventory management

Supply chain management is becoming tricky due to market variabilities that change the demand-supply equation, further leading to unnecessary inventory waste and stockpiling. Understanding the needs of the customer is essential in order to make informed decisions and optimize inventory across the value chain.

How can companies manage inventory and reduce stock-outs, inventory holding cost, and revenue losses?

This is where the role of an intelligent demand sensing solution comes into play.

Demand sensing – An approach beyond traditional demand planning

Demand sensing uses AI/ML technologies to help businesses forecast the immediate future wants of the customers and curtail the unnecessary loss of goods by predicting demand accurately. It can thus curb the shortage of products besides other correlated issues.

This approach to short-term forecasting trends instantly helps companies better guess what the consumers want, when, and where. The parties involved can forecast the latest sales data and make improvements to generate more profits.

There are two aspects of demand sensing which are:

Short-term forecasting: It controls an elevated level of data granularity to examine daily demand information for the end customer and instantaneously find out the changes in demand behavior.

Increasing supply chain visibility: This is done by identifying patterns in point-of-sale (PoS), promotion, social media, NPI, weather, IoT, internet search, and economic data to make near-term developments to your forecast and inventory positioning.

Both methods cut down demand insecurity and allow crucial adjustments to the estimation without anticipating the next forecast cycle.

Hence, it is fair to conclude that demand sensing comes to the rescue of businesses to organize, standardize, and generate a more helpful supply chain and inventory management landscape.

Both methods cut down demand insecurity and allow crucial adjustments to the estimation without anticipating the next forecast cycle.

Demand Sensing in inventory management

Inventory management is harmonizing the service-level desired goals and the financial limitations for a substantial distribution of goods with the picture of demand and supply unpredictability in mind.

The inventory requires optimization that has primarily two parts:

Safety stock is the immediate function of forecast inaccuracy and service level, while cycle inventory is optimized by linear programming.

Demand sensing thus leverages real-time data to enhance forecast accuracy, thus decreasing the safety stock quantity, working capital, higher inventory movement, and lower inventory carrying cost. However, for efficient functioning, demand sensing and inventory optimization approaches should be implemented simultaneously.

How can organizations optimize inventory with demand sensing?

Lower demand latency: Demand sensing helps businesses get the appropriate information quickly by repurposing the PoS (real-time information, product sold, quantity, date and time, region) data for better sales forecasting without distributors’ insights.

Ideal operation of current inventory stocks: Demand sensing helps businesses optimize the existing stocks and balance their supply by considering the new consumer demands.

Planning inventories for new products: With new products continuing to supersede the existing product demands, it is the perfect time to implement demand sensing tools so that the businesses can meet the necessity for new products with the proper amount of the previous developments in a stockpile.

Demand sensing – A powerful solution for the future

Additionally, for businesses to gain the upper hand over their competitors, supply chain professionals should be able to identify the latest trends in the market and anticipate potential consumers’ demands along with their fluctuating preferences. However, this is not always possible with the assistance of traditional demand planning methods as it has certain limitations in predicting the future supply-demand accurate insights. Demand sensing is an invaluable resource and way of managing the inventory, primarily replenishment, the supply chains.

Therefore, organizations must leverage this intelligent solution to reduce the lead times and move other inventory optimization into the next century.

TradeEdge Traceability Solution: Managing market traceability for Mars’ digital supply chain initiatives

Product recalls are a costly business reality. Added to that is the limited visibility into product storage and movement, legacy technology, and existing data siloes across the supply chain. These factors hinder recall efforts and can lead to reputational, legal, and regulatory issues. Product traceability can save businesses from the problems mentioned above, but companies need access to inventory movement in real-time to reduce the burden of costly recalls, fight counterfeits, control quality, and even save lives.

Besides data unavailability, there are other challenges impairing product traceability for businesses, such as:

What is product traceability?

Product traceability can easily track inventory movements effectively, right from where they are shipped or back through their operational and production stages. Traceability helps companies identify defective or unsafe goods in their offerings, timely locate and remove them before such items impact buyers negatively, and avoid product recalls damaging to a company’s reputation.

Product traceability solutions minimize costs arising out of rejected items, recalled contaminated goods, and compensatory costs for damage control.

For example, the TradeEdge Product Traceability solution helped Mars, a global manufacturer of confectionery, pet food, and food products, meet market traceability needs for the company’s digital supply chain initiative.

How does product traceability function?

TradeEdge Product Traceability is a cloud-based and globally scalable solution designed to enable enterprises to control the inventory movement in near-real-time (Hold & Release Process).

Taking the above example of Mars here, the traceability transformation program was successfully implemented across all business segments worldwide to leverage agile ways of working in a decentralized business landscape. This enabled Mars to meet commitments regardless of Covid pandemic restrictions. TradeEdge implementation helped the global F&B company absorb and harmonize data from multiple ERP (Enterprise Resource Planning) and warehouse management systems agnostic, thus providing a unified business consumption layer to business users.

With its help, clients like Mars achieved:

The bottom line

In order for businesses to fully optimize such product traceability programs, they need to follow specific ground rules, such as:

Product traceability solutions empower businesses with real-time insights into their product supply chain and manufacturing units to address any issue arising from harmful items that need timely recall, replacing, or placing on hold. Such timely availability of crucial insights immunes businesses from damaging losses and poor reputations.