End to End Supply Chain Management Analytics – Integration of People, Process, Technology and Location

Introduction

Supply chain analytics is the streamlining of the entire supply chain of the organization or business to create value to the customers and to gain a competitive advantage in the marketplace. Supply chain management refers to the management of the entire chain of supplies hence making them efficient and economical.

Why supply chain analytics is required?

In a highly competitive environment, organizations are striving to optimize the operational expenses to achieve high efficiency and manageability throughout the chain. Efficient supply chain analytics and tools help companies to achieve accurate and efficient forecasting, improved supplier network and improved warehouse management, increased visibility throughout the entire supply chain which can cater the dynamic end user demands.

The Challenges

The various challenges faced by supply chain executives are as follows:

• Supply Chain Visibility: Supply chain visibility is inhibited by a lack of capabilities and an unwillingness to collaborate.
• Cost Containment: Fighting integral costs as such might be futile, but being flexible can identify cost savings elsewhere.
• Risk Management: Process, data, & technology are identified as the roadblocks to good risk management, yet they are the key enablers.
• Increasing Customer Demand: Customers are getting increasingly demanding and have stringent quality and delivery requirements. This calls for more precise synchronization of supply and demand.
• Globalization: Lead times, delivery, and quality are top challenges, but overall globalization has been a positive boon for all.
• High inventory holding costs: There is high inventory at the destinations leading to high inventory holding costs and overhead costs.

End to End Supply Chain Management Analytics

End-to-End Supply Chain management is defined as a managed flow of products, cash, and information from the customer’s customer to the supplier’s supplier as defined by the business strategy. It requires the definition of an operating strategy to enable the business strategy.

Today, when companies talk end-to-end, they are usually advocating the automation of flows within their four walls. It has little to do with the customer and the market which I think is a missed opportunity. It is usually cross-functional enablement for the organization, but not an end-to-end journey.

Companies are not happy with what they have today. In surveys, we get three responses to every one response that describes their supply chains as traditional, and reactive. There is room for improvement. Supply chains respond. They do not sense. The flows are inside-out. The current processes do not allow them to be outside-in. As a result, the supply chain is slow and out of step with the market.

The path to building an end-to-end value network usually goes through five distinct phases: improving transactional efficiency, data sharing, formulation of policy, building relationships, and engaging in joint value creation.

Goals of End to End Supply Chain Management Analytics

End to end supply chain analytics acts as a single point of contact with all the functions of the supply chain (starting from supplier to the end customer) including customer supply chain management which manages orders, demand plans, demand forecasts and status. Starting from suppliers to the end customers, the goals of it are as follows:

Supplier management:

• Supplier performance reporting and dashboard
• Visibility through the control tower.

Transportation management:

• Location Planning &Origin Consolidation
• Load Consolidation
• Network Optimization
• Equipment Optimization
• Visibility through Control Tower
• Freight Bill and Audit
•  Spend Analytics

Port of arrival & customers:

• Visibility of the shipments through the control tower
• Freight Bill & Audit
• Spend Analytics

Warehousing Management:

• Implementation of Warehouse Management System
• Implementation of KPI dashboards to ensure the increase in productivity
• Inventory Optimization
• Performance Measurement: KPI Dashboard

Transportation Management:

• Network Optimization
• Location Planning for Consolidation
• Load and Equipment Optimization
• Visibility through Control Tower
• Performance Measurement: KPI Dashboards
• Freight Bill and Audit
• Spend Analytics

End Customers:

• Vehicle Route Optimization
• Visibility though Control Tower
• Demand Forecasting

Benefits of End to End Supply Chain Management Analytics

With clear end-to-end supply chain visibility, businesses can respond more quickly to demand changes, absorb disruption, and ultimately become better positioned to beat the competition. Performing end-to-end supply chain optimization enables the following:

• Truly breaking down silos in the supply by analyzing total costs.
• Better understanding of why optimizing on local activities such as production plants or transportation lanes may not benefit the entire supply chain.
• Tradeoff service and cost in a data driven way
• Determine the time to recovery and cost of risks
• Optimize decisions across the supply chain on an ongoing basis to improve performance and reduce costs.

According to Gartner Research, calculating the ROI of the implementation of end-to-end supply chain visibility takes just one year and the quantitative benefits are phenomenal:

• Inventory savings of 20% of value
• Increased forecast accuracy in the region of 25%
• Improved service level agreements to consistent levels of 98%
• Freight charges reduced from 5% to 3.5% of volume
• Decreased inventory on stock from just over 10 days to fewer than 7 days
• Reduction in workforce by 10%

Enabling End to End Supply Chain Management Visibility

Automation, the Internet of Things and cloud computing are breaking down silos and empowering greater internal and external collaboration. However, investing in such technologies alone, will not result in end-to-end supply chain visibility – only solid execution can. By linking multiple business units and organizations to drive improvement, organizations can achieve higher customer satisfaction, streamline processes and maximize ROI.

Conclusion

End to end Supply chain management has a lot of connotations to it, and a lot of people don’t truly understand what it means. However, smart businesses keep their minds open to the best ways to streamline their operations for maximum efficiency which includes both time and energy savings for themselves and their employees. With end-to-end supply chain visibility, businesses will be able to respond more quickly to demand changes, absorb disruption, and ultimately better positioned to beat the competition.

©3SCSOLUTION

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Supply Chain Management is Strategy

A study just published in one of the world’s top research journals confirmed what supply chain managers already knew: the most successful companies manage and orient their entire supply chain network to serve the end consumer best, and they do this by tearing down departmental silos and integrating across key suppliers and customers. This trend has accelerated in the face of COVID disruptions.

In other words, supply chain management is the best strategy for creating value. True professionals know that the WHAT and the HOW of executing one’s operations determine their capabilities. Supply chain management is the what and the how: it encompasses the planning, sourcing, making, delivering, and returning of products and services. We challenge you to think of any struggling company. Now look to see if it has a “strategy” that is divorced from the reality of their supply chain management capabilities, or if the supply chain capabilities match the operating environment. Either way, competent supply chain management that aligns with the ultimate end user is key to success.

This has powerful implications for the wave of technology investments starting to take place. The biggest mistake that companies make is the failure to understand how technology should enhance capabilities. The baseline capability for all other outcomes is supply chain transparency. There is no point in investing in advanced optimization, forecasting, and collaborative capabilities until a company can see what is going on in a timely fashion.

Considering the importance of technology to supply chain management, what is the biggest obstacle to its adoption? Research is clear that performance takes the most significant hit when technology is significantly upgraded without concurrent improvements to the people and processes. It turns out that faster, more efficient technology is amazing when you get it right. However, it also is faster and more efficient at propagating bottlenecks, errors, and problems when the rest of the organization does not properly support it.

A recent example we have seen is a company that automated its distribution centre at the cost of tens of millions of dollars—yet to save money, it cut its training and talent development budget. It focused on big partners (customers and suppliers) first. This is a quite common approach, and the outcome is also far too common: throughput increased, making automation’s proponents happy, yet the problems have increased even more! The workers are frustrated by the technology they do not understand, bottlenecks are appearing that nobody knows how to resolve. Many processes are slower, and relationships with carriers, suppliers, and customers are suffering as the result of the snarled flows of goods and failure to work (or even communicate) with a large number of smaller partner companies to redesign processes.

Some companies are applying technology to better leverage and manage their organizational processes and talent management. A recent conversation with Blake Pinard at Snagajob, America’s #1 hourly work marketplace, revealed that the pandemic challenged them with a 546% increase in supply chain-related jobs. The company is developing numerous technology tools to document worker skills and experience and match them to where they contribute the most value. Badges for worker skills and tools for companies to monitor performance are among the insights offered by Snagajob’s specialized talent management systems. Years of research show that companies like Snagajob and Microsoft and others that invest in workers outperform their peers.

The COVID pandemic has brought supply chain management to the forefront of national consciousness. Empty supermarket shelves and toilet paper shortages have turned out to be the strongest advocates for the supply chain’s importance to our society. Supply chain managers have done an amazing job of pivoting to deal with the disruption in record time, reinventing entire supply chains and changing the global economy in one quarter of a year. The most successful companies in this new normal will be the frontrunners when it comes to technology and worker relations.

© Supply Chain Management Review

How to Build a Trillion Dollar Supply Chain

Amazon became a trillion-dollar business in February 2020 – a month before COVID-19 shut down the United States. That’s not an opinion, it’s a fact. They became the fourth trillion-dollar company in history, and the reason they were able to reach that milestone is because early on they bet on technology and highly qualified resources that disrupted the supply chain market, and subsequently solved how to meet demand and make life more convenient.

And nothing has stopped them from doing it.

As of mid-June 2020 – 90 days after the global shutdown, with roughly 30 million people unemployed – rather than having slowed down at all, the company’s market cap reached $1.3 trillion.

While other businesses were being shuttered, many organizations with cloud-enabled supply chains have found themselves thriving. Analysts and media have been awestruck at how quickly Amazon and other companies have responded to changes in the marketplace. But the truth is, these companies made smart investments in building resilient supply chain networks using technology that is readily available to everyone in today’s market.

Software resilience gives organizations the ability to pivot in the face of market pressures and evolve to meet changes in customer trends, and disruptions to individual markets or the global economy.

There is no reason why other companies can’t do the same. The one thing Amazon has made clear is that investing in advanced technology is the antivirus for today’s global supply chain issues. The tech giant isn’t doing magic tricks! All they are doing is betting on a clear, long-term strategy driven by cloud and advanced technologies.

While other companies continue investing in cheap labor, taking shortcuts, and worrying about process improvement, visionary organizations have proved that none of that will help build resilience. Those are Band-Aids, half measures, and possibly only busy work. What is truly effective and should be the foundation of a modern business is the reimagining and rebuilding of supply chain infrastructure. Anything else is playing catch up.

However, the rest of the market will catch up. Competition will intensify as more organizations move supply chain processes to the cloud and remove business silos to create a better view of the customer and deliver on the promise of online customer experiences.

So, what else needs to happen in the world before other businesses decide to do the same and invest in infrastructure that gives them elasticity and visibility across their supply chains? Exactly what disruption does the market still need to see to believe that cloud and advanced technologies are the foundation of modern business?

For the past 10 years, it has been established that businesses need to digitise their supply chain infrastructures and make themselves resilient to a marketplace that was fundamentally different – and changing fast – from what it was in 1983 or 1996.

So, the future we been talking about has finally arrived, and if your supply chain network is straining under the weight, sorry to say , but there is no more wiggle room. There is no more cushion. Band-Aids won’t help you. It is time to change.

Consumer behaviors are fundamentally different from what they were five months ago. Consider this: since the pandemic began, retailers have experienced a spike in online orders that has taken online sales to sustained levels that we previously only experienced during Black Fridays or Cyber Mondays. Supply chains have been fulfilling the same level of activity as those “events” every day since mid-March.

In a recent article in Supermarket News, it was reported that online grocery sales are expected to grow 40% in 2020. What do numbers such as this this tell us as supply chain professionals? What it tells me is that there’s no way back to “normal,” and customers seem to have shifted what “normal” even means. As professionals, we need to make our supply chain more resilient and migrate them toward the future.

What most businesses without a cloud strategy have been doing for the last five months is stretching their infrastructure to the limit. Today, organizations should have clear roadmaps of how they will migrate to newer infrastructures and advanced technologies that can support their supply chains.

Kudos to all the companies who saw the trends, invested in technology, and met the supply chain challenges of the last few months. They have proven that a vibrant marketplace can exist and needs can be met.

But nothing they have done in terms of technology is radical. The technologies they have used have been in the market for a while, waiting to replace or integrate with whatever systems already existed. Businesses just have to make the decision to get themselves in the game.

In the retail industry, there is a 1,000 pound gorilla currently dominating the field, but we have witnessed company digital transformations that have enabled other retail organisations to start playing on the same field at the same level, and switch from a defensive strategy to finally playing offence again.

The time is now. A recent Fortune study of F500 CFOs showed that the COVID-19 pandemic has added further urgency and accelerated digital transformation plans. There may never be a driver as powerful as the current crisis. Organizations that are still being held back by legacy supply chain and logistics technology need to begin moving towards a cloud-enabled supply chain – or risk being left behind.

 

© Supply Chain Management Review

NextGen Supply Chain: On-demand warehousing

Recently, I was talking to a friend who had interned with a company who are leaders in the shipping industry. His project was to create the roadmap for their ‘on-demand warehousing’ business, a new domain the company is looking to enter. This was a new term and when I looked into it, what I got to know was something I feel is going to be the next game-changer in the supply chain.

Uber and Airbnb started the online matchmaking of demand with supply in the space of underutilized transportation and lodgings, on-demand warehousing has extended it to the storing of inventories. Dynamic on-demand warehousing is emerging as a viable way of purchasing warehousing services on demand – paying only for what is used.

From Melbourne to Mumbai to Manhattan, it seems the entire supply chain is taking a crack at on-demand warehousing. Actually, the concept is not as new as the name. After all, who doesn’t know that fairly successful e-commerce company that has been practicing on-demand warehousing for some time. You know it as Fulfillment by Amazon (FBA). Companies that sell products on Amazon can use FBA to fulfill those orders. Or as FBA says – “You send your products to Amazon fulfillment centers, and we pick, pack, and ship them and provide customer service.”

Just as interesting, companies as diverse as KFC and Exploding Kittens have used on-demand warehousing. In the case of Exploding Kittens, it is the only way it was ever going to be distributed initially. The company thought it could assemble, pack and ship its first orders at a launch party. Except a million orders flooded in almost all at once. Forget the launch party, enter on-demand warehousing.

So, how does on-demand warehousing work?

There are three different models already. In one, the company providing the warehouse space also fulfills orders just like FBA. Or, the owner of the inventory might prefer to have its own people on-site filling orders. A third variation is a consortium of companies that efficiently stage inventory and fulfill orders across their private network.

On the operations side, on-demand warehousing is not so tough. Slotting inventory into empty space in a warehouse is manageable. And it’s not impossible to work out the details of who and how that inventory will be managed. The tough part is matching up warehouses with space and companies with the inventory.

Warehousing Tries to Keep Up with e-Commerce

Before the advent of e-commerce, many retailers used warehouses as intermediate storage points (distribution centers or DCs) to supply their stores; it is still a common way to manage distribution.

A retailer could use a network consisting of a few Distribution Centers (DCs), each serving a “region” comprising many states, with replenishment lead times of a few days.

With the development of e-commerce, however, the traditional warehousing model began to fall short.

E-commerce creates significant challenges in terms of customer expectations, compared to traditional retail. In general, outbound shipping with e-commerce features very small quantities sent directly to individual customers.

Shipping time is absolutely critical: Many customers now expect their items to arrive inside two days, and more and more retailers are offering same-day delivery. Furthermore, e-commerce demand can be highly variable, influenced by social media and faster news cycles in the Internet media.

Such factors are forcing significant changes on the warehousing industry. The changes are not simply because e-retailers keep more inventory in warehouses because, by definition, they have no brick-and-mortar stores.

Overall, demand for warehousing space is growing, as is the need for an efficient warehousing and distribution strategy.

For e-retailers, shipment options have been challenging indeed. Traditionally, their options have been these:

• Startup–drop ship. If the retailer owns its own manufacturing/assembly facility, initially it may ship directly from that facility. Many small e-commerce retailers start this way.
• Self-owned network. If the retailer operates its own warehouses, it is unlikely to have the scale and financial resources to build an extensive network. As a result, the average distance to the customer is high, resulting in high shipping costs and longer delivery times.
• Network outsourced to 3PL. Although this offers a little more flexibility compared to a self-owned network, many 3PLs demand commitments of one year to three years. This effectively locks the retailer into a fixed structure for several years.
• Distribution outsourced completely. Again, “Fulfillment by Amazon” (FBA) wherein it distributes other retailers’ products through its network is an example of this. Although this can provide speedy service to customers, the costs can be high and many retailers are wary of handing over a core part of the business to a top competitor.

A Role for On-Demand Warehousing
On-demand warehousing, quickly matching those needing space with facilities that have space available, is emerging to help facilitate the pace and scope of e-commerce’s logistics needs. Several new companies have launched recently to provide the on-demand service.

It is “dynamic” in the sense that the retailer can change the configuration frequently: based on demand conditions, warehouse space could be deployed at different locations, for different volumes, in a dynamic fashion. Its order management system and warehouse management system can link the retailer’s systems with those of the warehouse provider.

The idea is that the shipper has access to a large network of warehouses, and can activate services “on the fly,” ranging from bulk pallet handling to fulfillment, in small to large volumes and for relatively short times.

For example, a small e-commerce retailer may decide to create half a dozen different distribution points, with as few as 50 pallets at each warehouse and little to no fixed time commitment. A tried and true approach here is third-party logistics. It’s a big business. But 3PLs usually want multi-year deals and big space requirements. They typically have no intentions of letting anyone but their own people manage the inventory.

On-demand warehousing is not a new twist on 3PLs. Flexibility and short-term storage are the hallmarks of on-demand warehousing.

At the same time, 3PLs have quite a different business model. As an expert in warehousing has to say, “[T]here is a great void between the requirements of 3PLs and companies that might only need 300 pallet positions for three months. This notion of flexible space just doesn’t exist.”

In on-demand warehousing, the warehouse provider would use its own labor and equipment to perform standard and optional services such as receiving, shipping, case pick, item pick and packing, and would charge the retailer on a per-unit basis.

In such a system, the retailer incurs no upfront fixed costs, and gains significant flexibility. Of course, the unit cost charged by the warehouse provider may be higher or lower than what would be incurred by the retailer if it operated its own high-volume, high-utilization warehouse. But this is the benefit of dynamic on-demand warehousing: The retailer gains flexibility and avoids capital expense, even if sometimes the per-unit cost is higher.

Many companies are now getting into this field now. The new flexible warehousing concept is a good example of what has been called “platform capitalism” – one of the fastest growing and most significant trends in the business-to-business (B2B) world, based on reports in publications such as The Guardian.

Over the last decade, electronic marketplaces have proliferated, providing an ever-wider range of services and business activities.

For example Warehouse Exchange, which is in beta, is what WeWork is for office space. Both offer space and some supplemental services including security. All of the matchmaking is done electronically. But the challenge these companies are facing is the limited supply, while the demand is off the charts. The claim that demand is strong seems to be confirmed by UPS’ jump into the fray last month. UPS’ Ware2Go platform is targeted at small- and medium-size companies. UPS says its service can handle the entire transaction starting with finding space and ending with fulfilling orders with guaranteed two-day order-to-delivery.

Value for Warehouse Operators
On-demand warehousing is of real value for warehouse owners as well. Building a warehouse can be expensive; any unused space has an opportunity cost.

Even if a warehouse owner/operator has long-term contracts with retailers or 3PLs for much of its space, any remaining space can be turned into a revenue-generating asset by “registering” it on a dynamic on-demand warehousing marketplace.

Depending on its operating costs, opportunity costs and market dynamics, a warehouse owner can choose a price that may be more, or less, than the rates it charges its existing clients.

Clearly, there are several options already in on-demand warehousing. The challenge is to find the one best suited to your business. In any case, rest assured that the next time someone asks you what KFC and Exploding Kittens have in common, you know the answer—on-demand warehousing.

 

References:

https://www.supplychain247.com/article/the_uberization_of_dynamic_ondemand_warehousing

https://www.scmr.com/article/on_demand_warehousing_ready_to_be_game_changer/third_party_logistics

https://www.theguardian.com/technology/2015/jun/07/facebook-uber-amazon-platform-economy

The Fuzz of Fuzzy Logic: An Overview

“Accha lo, bura lo, decision lo.” – Our professor of Operations Management used this line in one of his presentation. The purpose was to make the students understand the importance of decision making. As per him, the students should be firm and definite in their answers and decisions. To quote in his exact words – “It always has to be either YES or NO but never PO”. This statement, however propelled me to ponder on the question – Whether this statement really strikes a chord with the b-school world? (A world wherein each answer and every argument revolves around a two word phrase. We are so fond of this phrase that I am highly certain that most of us would surely start the answer for above question with the same phrase – “It depends”.)

Now coming to the actual question – how is the above discussion related to our today’s topic of JIT? The answer is quite simple, because today we would diving deep into the world of PO which is blurred and FUZZY. Unlike traditional logical systems, fuzzy logic is aimed at providing a model for modes of reasoning which are approximate rather than exact. In this perspective, the importance of fuzzy logic derives from the fact that almost all of human reasoning – and especially common sense reasoning – is approximate in nature.

In bivalent logic, truth is bivalent, implying that every proposition is either true or false, with no degrees of truth allowed. For most part of this era of technology, it was believed that anything and everything innovated in this era followed bivalent logic. This belief was corroborated further by the behaviour of all instruments, gadgets, equipment etc which worked in only two states i.e., either ON (giving maximum throughput) or OFF (giving minimum throughput). However, the problem with bivalent logic is that it is in fundamental conflict with reality – a reality in which almost everything is a matter of degree. To address such problems there is need for a logic for modes of reasoning which are approximate rather than exact. This is what fuzzy logic aims at.

The word fuzzy refers to things which are not clear or are vague. Any event, process, or function that is changing continuously cannot always be defined as either true or false, which means that we need to define such activities in a Fuzzy manner. Fuzzy Logic resembles the human decision-making methodology. It deals with vague and imprecise information. This is gross oversimplification of the real-world problems and based on degrees of truth rather than usual true/false or 1/0 like Boolean logic.

Take a look at the following diagram. It shows that in fuzzy systems, the values are indicated by a number in the range from 0 to 1. Here 1.0 represents absolute truth and 0.0 represents absolute falseness. The number which indicates the value in fuzzy systems is called the truth value.

In other words, we can say that fuzzy logic is not logic that is fuzzy, but logic that is used to describe fuzziness. There can be numerous other examples like this with the help of which we can understand the concept of fuzzy logic.

Fuzzy logic implies that everything is imprecise, partial and granular and the truth is perception based and a matter of degree. Fuzzy logic has been and still is, though to a lesser degree, an object of controversy. For the most part, the controversies are rooted in perceptions, especially a perception of the relation between fuzzy logic and probability theory. A source of confusion is that the label “fuzzy logic” is used in two different senses – a) narrow sense: fuzzy logic is a logical system b) wide sense: fuzzy logic is coextensive with fuzzy set theory.

Fuzzy logic in control system:

A control system is an arrangement of physical components designed to alter another physical system so that this system exhibits certain desired characteristics. Fuzzy logic is applied with great success in various control application. Almost all the consumer products have fuzzy control. Some of the examples include controlling your room temperature with the help of air-conditioner, anti-braking system used in vehicles, control on traffic lights, washing machines, large economic systems, etc.

Following are some reasons of using Fuzzy Logic in Control Systems −

• While applying traditional control, one needs to know about the model and the objective function formulated in precise terms. This makes it very difficult to apply in many cases.
• By applying fuzzy logic for control we can utilize the human expertise and experience for designing a controller.
• The fuzzy control rules, basically the IF-THEN rules, can be best utilized in designing a controller.

Fuzzy logic control has several advantages like it is cheaper, robust, customizable, reliability, efficiency and emulates human deductive thinking. However, there are certain disadvantages as it requires lots of data, useful in case of moderate historical data, needs high human expertise and regular updating of rules.

Fuzzy logic in decision making:

Fuzzy logic is a logic trying to be as close as possible to human thinking and perception. It is based on the assumption that people are not thinking in the exact variables (yes / no), but distinguish a range of “blurry” values (rather yes, much yes, maybe no, and yes and no). This means that it operates with cloudy concepts and blurred boundaries.

The procedure of fuzzy processing is realized in the following steps: problem identification, fuzzification, fuzzy interference and rule base, deffuzification, interpretation and verification.

Steps for decision making:

The steps involved in the decision making process −

• Determining the Set of Alternatives − In this step, the alternatives from which the decision has to be taken must be determined.
• Evaluating Alternative − Here, the alternatives must be evaluated so that the decision can be taken about one of the alternatives.
• Comparison between Alternatives − In this step, a comparison between the evaluated alternatives is done.

Fuzzy logic in Neural Network:

Artificial neural network (ANN) is a network of efficient computing systems the central theme of which is borrowed from the analogy of biological neural networks. ANN acquires large collection of units that are interconnected in some pattern to allow communications between units. These units, also referred to as nodes or neurons, are simple processors which operate in parallel.

Following are some reasons to use fuzzy logic in neural networks −

• Fuzzy logic is largely used to define the weights, from fuzzy sets, in neural networks.
• When crisp values are not possible to apply, then fuzzy values are used.
• We have already studied that training and learning help neural networks perform better in unexpected situations. At that time fuzzy values would be more applicable than crisp values.
• When we use fuzzy logic in neural networks then the values must not be crisp and the processing can be done in parallel.

Neural-trained Fuzzy logic:

The reverse relationship between neural network and fuzzy logic, i.e., neural network used to train fuzzy logic is also a good area of study. Following are two major reasons to build neural trained fuzzy logic −

• New patterns of data can be learned easily with the help of neural networks hence, it can be used to pre-process data in fuzzy systems.
• Neural network, because of its capability to learn new relationship with new input data, can be used to refine fuzzy rules to create fuzzy adaptive system.

Examples where Neural-Trained Fuzzy system is applied −

• The Laboratory for International Fuzzy Engineering Research (LIFE) in Yokohama, Japan has a back-propagation neural network that derives fuzzy rules. This system has been successfully applied to foreign-exchange trade system with approximately 5000 fuzzy rules.
• Ford Motor Company has developed trainable fuzzy systems for automobile idle-speed control.
• NeuFuz, software product of National Semiconductor Corporation, supports the generation of fuzzy rules with a neural network for control applications.
• AEG Corporation of Germany uses neural-trained fuzzy control system for its water – and energy conserving machine. It is having total of 157 fuzzy rules.

Applications of Fuzzy logic:

In aerospace, fuzzy logic is used in the following areas −

• Altitude control of spacecraft
• Satellite altitude control
• Flow and mixture regulation in aircraft vehicles

In automotive, fuzzy logic is used in the following areas −

• Trainable fuzzy systems for idle speed control
• Shift scheduling method for automatic transmission
• Intelligent highway systems

In business, fuzzy logic is used in the following areas −

• Decision-making support systems
• Personnel evaluation in a large company

In defense, fuzzy logic is used in the following areas −

• Underwater target recognition
• Automatic target recognition of thermal infrared images
• Naval decision support aids
• Control of a hypervelocity interceptor

In the finance field, fuzzy logic is used in the following areas −

• Banknote transfer control
• Fund management
• Stock market predictions

In industrial, fuzzy logic is used in following areas −

• Cement kiln controls heat exchange control
• Activated sludge wastewater treatment process control
• Water purification plant control

In securities, fuzzy logic is used in following areas −

• Decision systems for securities trading
• Various security appliances

In Pattern Recognition and Classification, fuzzy logic is used in the following areas −

• Fuzzy logic based speech recognition
• Fuzzy image search
• Fuzzy logic based facial characteristic analysis

In Psychology, fuzzy logic is used in following areas −

• Fuzzy logic based analysis of human behavior
• Criminal investigation and prevention based on fuzzy logic reasoning

Warehouse Robotics: The Amazon way!!

‘Sir… Ahh.., har wo cheez jo insaan ka kaam aasan kare ya waqt bacahye  woh machine hai Sir’ – after a long period of time, this not so famous line from a famous movie reverberated in my ears. It was during a recent conversation with one of my friends, when I felt like reiterating the same line as an answer to his really interesting question – ‘What is it that makes it possible for eCommerce giants to deliver the product to their consumers within a day’. Well, you guessed it right, the company under discussion was the one who does complete justice to its name – Amazon – the mightiest of all. The question was interesting enough to ring a bell in the mind of an ops enthusiast searching for a topic to design the content of this latest article in the JIT series.

Amazon enjoys a cult following. It is a favorite choice for customers due to one crucial reason: quick and efficient supply chain management. Back in 2005, Amazon launched its Amazon Prime service. Customers, paying an annual membership fee, received a guaranteed two-day shipping on hundreds of thousands of products. In fact, the introduction of two-day delivery was the game changer and established the dominance of Amazon in online retail industry. When many other retailers started to catch up with that strategy by offering their own free two-day shipping, Amazon tipped the playing surface by offering a one-hour delivery with its Amazon Prime Now service. The combination of sophisticated information technology, an extensive network of warehouses, multi-tier inventory management and excellent transportation makes Amazon’s supply chain the most efficient among all other companies in the eCommerce world.

Through this article we aim at giving a detailed analysis about one of the game changing techniques that is an integral part of eCommerce circle’s secret magic that make one day delivery possible – Warehouse Robotics. If there is one technological advancement that would certainly make living easy and convenient, robot would be the answer. Robots are human like machines capable of doing task they are programmed to do and thereby decrease human efforts significantly while improving efficiency and productivity.

Back in 2012, Amazon acquired a provider of automated and robotic warehouse solutions called Kiva Systems. And in 2015, that company was re-branded as Amazon Robotics. The robots of Amazon Robotics can pick and pack without needing any human assistance, enabling Amazon to complete warehouse activities super-fast. Over the years, Amazon has significantly increased its army of warehouse robots. Its warehouse robots, in fact, have grown at the rate of 15,000 per year from 2015. As of January 2017, Amazon had more than 45,000 warehouse robots, and the robot invasion continues. It had amounts of 15,000 and 30,000 respectively in 2015 and 2016. To date, Amazon’s robotics have been aimed at bringing goods to people for the picking of orders. The next generation of robots will see them picking the orders on their own to reduce the need for human order pickers.

While Amazon has been increasing its army of robots in its warehouses, other online retailers were initially slow to follow. Now, however, robots are catching on both domestically and abroad, for both large facilities, as well as for smaller islands of automation within existing facilities. Autostore is an example of a robotic automation provider that can accommodate such islands of automation.

The brain of robots where they receive and record set of instructions that make them perform tasks automatically is called Artificial Intelligence or AI. Use of industrial robots has already revolutionized shop floor activities in factories. However, with continued development in the field of AI has led to emergence of new type of robots. Touted as the future of warehouse automation, these rapidly growing robots have reduced the time needed to complete several tasks and have substituted human effort to a considerable extent. Half of the supply chain managers expect to benefit from increasing logistics automation within the decade. Automation is already well established in many distribution centers around the world, but for most, it is limited to workflow automation managed by increasingly advanced warehouse management systems. While system-guided manual processes can make a considerable difference to warehouse efficiencies though, the value of full automation—perhaps the holy grail of distribution center operation—is typically the preserve of corporate giants able to build purpose-designed automated warehouses, or to adapt older real estate for “lights-out” operation.

The situation is changing however, as more and more MHE manufacturers bring warehouse robotics to market. Robotic solutions offer the ability to introduce automation into DC operations without the need for major structural alterations. From unmanned aerial vehicles and driver less forklifts to mobile robots, there are several robot variants in the market that can move about the warehouse without human control. Robots are assisting in loading, picking, packages and moving. With Amazon Robotics leading the way, there are many new entrants in the autonomous mobile robotics (AMR) market that boast improvements in the management, control and automation of warehouse operations.

To date, there are at least four types of driver less vehicles that are bringing new levels of efficiency and automation to the warehouse.

• Goods to person picking robots

Most warehouse managers are looking to apply robotics so as to reduce waste and human movement. According to an analysis of U.S. Census Bureau data, the average warehouse worker wastes nearly seven weeks per year in unnecessary motion, accounting for more than $4.3 billion in labor. While many logistics and manufacturing operations still rely on manual and paper-based picking systems, autonomous mobile robots can now eliminate a lot of unnecessary walking. Improvements in sensors, artificial intelligence and mobility enable these machines to be easily deployed virtually anywhere. These machines typically carry carts and can be programmed to travel flexible routes in the warehouse to move product between workers and stations. A French robotics company has developed a warehouse robot that can actually climb warehouse racks to pick from any level, then transition to surface transportation to carry orders to human workers. Capable of picking up to 400 orders in an hour, the robots are already in operation with one French online retailer.

• Self-driving forklifts

Forklifts are also becoming increasingly complex and intelligent with full autonomy for some applications. They are well-suited for operations whose load-handling processes provide little added value, are repetitive and involve longer distances. These automated trucks operate in fleets from just a few to 30 and are usually used together with manually-operated trucks for certain duties. The platform-based logistics solution enables the forklift to know where goods are and when they are arriving. It can then calculate the loading process, seek the optimal route, assign tasks to itself, collaborate with other forklifts, and send confirmation of placement and movement to the ERP system

• Autonomous inventory robots

Autonomous mobile robots also offer new opportunities for inventory monitoring. When combined with RFID-tagged products and equipment, these machines can now conduct their own inventory sweeps autonomously at schedules determined by the warehouse. It not only reduces the need for manual inventory counts, but also offers real-time mapping to managers can easily visualize product storage. For example, the robot might identify storage and placement that is leading to inefficient movements of machinery or people.  In another case, it may better identify goods that are nearing expiration dates.

• Unmanned aerial vehicles

It may still be a while before drones are safely moving large products through the air in distribution centers or to customers’ homes. But in the meantime, lightweight unmanned autonomous vehicles (UAVs) are already being equipped with RFID-scanning technology to offer real-time inventory visibility in the warehouse. Sensors and algorithms enable collision prevention and an intuitive design that enables it to adopt flight patterns to unique layouts and to navigate cluttered environments, according to the company. Amazon has again led the way with its dedicated delivery trucks and research into aerial drone deliveries.

Cost and Efficiency Benefits of Warehouse Robotics

While robots are currently less prolific where carton and piece picking prevails, the experience of Amazon bodes well for companies waiting for the right time to implement robotic picking operations.

According to estimates from Deutsche Bank, Amazon, which now has upwards of 80,000 robots in use, is achieving operational cost reductions of around 20% in the fulfillment centers where they are deployed. These cost savings largely stem from improved efficiency, with cycle times in robot-equipped fulfillment centers slashed from 60-minutes plus, to around 15 minutes.

The most obvious advantages of hiring warehouse robots are cost reduction and time effectiveness. Human labor force requires holidays, sick leaves, paid leaves, lunch breaks, health insurance, and several other benefits. However, all these requirements are eliminated when robots come into the picture. Pros at multi-tasking, robots can lift pallets of merchandise, move entire stacks of shelves to shipping stations, and carry out tedious jobs better than humans. While some warehouses have completely automated pick-and-package systems, other warehouses are experimenting with robots specifically designed for speed-sorting. The multi-robot fulfillment systems are some of the most expensive warehouse automatons that specialize in working alongside humans to transport palettes. Travelling as a group, these robots can navigate automatically, with guidance from a server. Some bots can even pick up racks and drop them to human-operated workstations.

The average cost of warehouse robot is around $35,000 – thus, complete automation is not possible for smaller retailers and a little too difficult for medium enterprises. The limiting factor is the high cost of automation, in comparison with labor costs, which prevents most retailers from completely automating warehouse operations with robots. The scale and type of automation may, therefore, vary from one organization to another. However, with adequate planning and research, any warehouse can find its optimum level of automation, loaded with sophisticated features. It seems that a future of total warehouse automation with increasingly sophisticated features and facilities is at hand.

 

“I ultimately got into robotics because for me, it was the best way to study intelligence”

Sebastian Thrun

 

 

References: https://www.roboticsbusinessreview.com/manufacturing/robots-warehouse-changing-face-modern-logistics/

 

 

 

 

 

 

 

 

Need for High Velocity Supply Chains In INDIA

India is of the largest producer of fruits and vegetables but still there is huge mismatch in demand and supply. This mismatch can be attributed to the lack of proper cold chain infrastructure, climatic changes and improper packaging, etc. According to ASSOCHAM – MRSS India study, about 40 – 50 percent of the production valued about $440 billion gets wasted. The challenge for managing fresh produce is that product value deteriorates significantly over time in the supply chain at rates that are highly temperature and humidity dependent. For many products, a decision about supply chain strategy involves a choice between responsiveness and efficiency. The appropriate choice depends on how the product changes in value over the time interval between production and delivery to the customer.

The perishable supply chain in India typically consists of individuals such as farmers and businesses such as retailers and manufactures which are connected together by Logistics. Logistics is a specialized field of its own comprised of shipping, warehousing, and road/rail transport.

In India perishable supply chain is facing following problems: –

1)Stakeholders working in isolation: -Indian food supply chains are complex and consist of numerous small players working in isolation with each other with very poor infrastructure. Due to failed cold chain transits, poor warehouse conditions and traffic delays India waste 40% of all harvested agricultural produce. Currently there is about 90% shortage of cold storages in India and most of the available cold storages are available in few states. A recently published government press release confirms that in 2013 about 60% of cold storages in India were concentrated only in 4 states – Uttar Pradesh, Gujarat, West Bengal and Punjab (Exhibit 1). Indian Government’s estimates suggest that about 80-90% of cold storages are used to store only potatoes leaving other fresh foods unguarded.  Between 2015-2016, India exported nearly $600 million in fresh fruits – a category that is most severely affected by poor cold chain logistics, losing 18% of all produce after harvest.

2)Lack of demand estimation: -Currently perishable supply chain operates in push-based system where farmers try to push whatever they produce. This creates gap in supply and demand.

3)Lack of technology applications: -Cold chain logistics in India don’t take advantage of data analytics, product tracking, live inventory data across channels and synchronised freight timings to match demand and supply.

4)Lack of centralised supply chain leadership: -An emerging trend in supply chain across globe is integrated supply chain to unify procurement, logistics, forecasting/demand planning which is missing in Indian cold chains.

This helps in facilitating effective decision making with a view spanning the en­tire supply chain.In-store product freshness positively influences customer demand. Estimates suggests that produce spends approximately 50% of its time between suppliers and retailers at different touch points. Handling at these touch points compromises with product quality and freshness due to wait and execution times at dock, malfunctioning of cooling systems, loading and unloading etc.

 

 

Possible Remedies: –

In the perishables sector, providing the freshest products to customers remains a challenge, since doing so is not tied to a single business function. Developing a high-velocity supply chain is one of the most straightforward strategies for ensuring freshness, reducing shrink and realizing top-and bottom-line improvements. A high velocity supply chain can be effective in reducing the shrinkage and hence will increase shelf life of the produce which in turn will help in increasing of likelihood of being picked up from the shelf and reduce the likelihood of it being thrown away.

The transition from Supply Chain 1 to Supply Chain 2 shows how a high-velocity supply chain can improve gross profit.

Possible areas where technology can be employed: –

1)Reducing Replenishment Lead Times: -By reducing the replenishment lead times, managers can decrease the uncertainty of demand during lead time. This is beneficial for seasonal produce which will enable us to place multiple orders hence reducing in-house inventory levels at a particular time. Reducing lead time can help in retaining freshness and shrinkage. This can be done by electronic data interchange(ENI) or Advanced Shipment notifications (ASN) .Cross docking can also be used to reduce lead time associated with moving the produce between stages in supply chain.

2)Demand forecasting at retailers: – Demand forecasting helps us in managing the inventory at optimum level to reduce the inventory carrying costs and ordering cost while ensuring that all the customer orders are fulfilled. Retailers should estimate the monthly demand and share this information across supply chain. Sharing customer demand data across supply chain helps in reducing the Bullwhip effect. Walmart has routinely shared its POS (Point of Sales) Data with its suppliers. Dell shares demand data as well as current inventory positions of components with many of its suppliers via internet thereby helping avoid unnecessary fluctuations in demand and supply hence reducing wastage.

3)Maintaining humidity and temperature for specific products to retain freshness and reduce shrinkage: – Most important factor for maintaining freshness of the fruits and vegetables is the temperature. Generally perishable commodities have an optimal shelf life approximately at Zero Degree Celsius and rate of deterioration of perishables increases two to three folds with every 10 degrees increase in temperature.

4)Multi Storage of produce in a single location: -Currently most of the cold storages are mostly used for single produce which can be extended to multi- produce warehouses by using specialised refrigerators for different crops with humidity and temperature controls. This will help in short term increase in capacity of existing warehouses and money invested will be comparatively lower.

5)Tracking of produce at collection point: -Constant tracking of farm produce at warehouses can help in matching supply and demand and a Pull type system can be established which will help in managing the inventory at optimum level.

6)Cross delivery of produce from warehouse by maintaining live inventory of nearby warehouse: – This will allow the tracking of inventory level of each product present in various warehouses across the region. This will help in scheduling the delivery of product to a retail store from a different warehouse (that is not its master warehouse) if the produce present in another store is older than that of master warehouse therefore helping in reducing the probability of produce getting wasted due to high lead time.

 

ROBANKER: Robot force in Banking and Finance

‘It is not that we use technology, we live technology’ – Godfrey Reggio

Technology in the very basic sense is the strongest pillar of human development which facilitates problem solving. Problems can never be completely eliminated from the world because if we solve one, many more will arise. Our intuitive need to solve the problems has resulted in the advent of technology in almost all aspects of our life. This makes technology that pillar of human development which if removed can cause the entire world to fall. Continued technological advancement has become an essential part of our life; the technology is thriving and almost never-ending.

The significance of such innovation lies in providing unmanned, unbreakable security, without compromising the feature set. One such field that demands uncompromising security is the BFSI (Banking, financial services and Insurance) sector. Over the past couple decades, banks and other financial institutions have had to step up their game. In order to remain competitive in an increasingly saturated market – especially with the widespread adoption of virtual banking these firms have had to find a way to deliver the best possible user experience to their customers.

Being a critical aspect of the economy, technological development in the banking sector becomes a sensitive affair since any loophole may lead to loss of property – intellectual as well as monetary. Such a sensitive sector requires reliable and accurate process design and delivery. Additional features require hardware and software updates or at times, completely new components. Bio-metric scanning is one such component. With considerable improvements in this technology, their usage has increased excessively, be it for mobile phones, laptops, or ATMs.

Therefore, it becomes increasingly imperative to incorporate new technology to sustain and improve operational efficiency and to enhance and maintain customer satisfaction.

Robotics are shaping the future of the industry for financial reasons as well as customer demand. Robots are already an essential component of the banking industry whether you see them or not. They operate on smartphones to create a more efficient mobile banking experience. More obviously, they take customers straight into a sci-fi novel and even provide in-person service at branches.

New and emerging technology, such as robotic process automation, cognitive computing and the Internet of Things (IoT) are profoundly impacting and transforming the workforce of the future across the financial sector and will continue to do so. Robotics Process Automation (RPA) is providing digital speed to market and cost take outs for financial institutions, but the successful financial institutions of tomorrow will be those who embrace the next wave of robotics technology and future technology to drive business outcome.

What is Robotic Process Automation?

The desire to achieve optimal results with available resources and the need to go increasingly digital has caused the companies to strengthen the autonomy of the workers through technology based advances. Similar to many other industries, the financial field is heavily reliant upon documents and the many legacy systems that have been employed to help manage them most effectively. There are a great deal of records involved in the life cycle of a banking customer, from the initial application to account management documents to deposits, withdrawals, loan documents and a whole myriad of other day-to-day transactions that inevitably generate documents. The volume of documentation required for financial transactions can also lead to slower processing times. In many instances, a process could be stuck in limbo for days, weeks or even longer as it awaits approval. And with humans at the helm, errors are inevitable – some of which could prove incredibly costly to the institution, both financially as well as with respect to reputation. By automating these back office functions, these delays and errors can be all but eliminated, creating a more productive, efficient and accurate process. Robotic Process Automation (RPA) is fast emerging as a highly efficient way to help financial institutions support their digital transformation initiatives. RPA is at the forefront of human-computer technology and provides players in the financial services industry with a virtual workforce that is ruled based and is set up to connect with your company’s systems in the same way as your existing users.

If you think of artificial intelligence (AI) as a robot’s brain, then you could call Robotic Process Automation (RPA) the eyes and hands. RPA allows for efficient, repeated processes and data collection while AI can interpret that data and change behaviors as a result. In banking, these systems can help with reviewing financial documents and cut down on human error. With robotics, you automate and build an automation platform for you front office, back office and support functions. Best of all, you don’t need to wait months or even years to see the results. In an industry that is constantly looking to improve the consumer banking experience, mitigate risk and comply with regulations, and increase great efficiencies around core customer oriented processes, the use of intelligent software robots is already being put to use across many banks.                              For e.g.,

• Bank of NY Mellon Corp. is investing heavily in this technology, rolling out more than 200 bots to handle tasks such as transferring funds. As a result, BNY Mellon reported an 88 percentage improvement in processing time and its funds transfer bot saved the company $300,000 alone.
• Bank of America coming up with its own chat-bot, Erica. A user can interact with Erica both through text and through voice; one of the primary differences between this and other chat-bots we might have seen is that the bot interacts with you first.

Until RPA was introduced as a solution for the financial industry, banking professionals struggled to connect the many legacy systems being used in order to manage and retrieve the information needed to do their jobs most effectively. And given the massive number of mergers and acquisitions in the financial world, this problem wasn’t going away. One of the greatest benefits of RPA, however, has been the ability this technology provides to integrate with and bridge these legacy systems, creating a much more uniform approach to data management without having to start from scratch. It’s been nothing short of revolutionary.

Beyond this, robotic process automation has also dramatically streamlined a wide variety of back office processes that once bogged down bank workers. By shifting a majority of these tedious, manual tasks from human to machine, banks have been able to significantly reduce the need for human involvement, which has had a direct impact on everything from performance and efficiency levels to staffing issues and expenses.

Robotic process automation checklist

To identify processes that are suitable for robotic process automation you can use this check list:

• The process should be rule based and not depend on human judgement
• The process should be initiated by a digital trigger and be supported by digital data
• The process should be functioning and stable
• The bigger the volume of executions of the process the better
• For a proof of Concept project it is key that the process leverages the key systems of the company

Processes relevant for Robotic Process Automation: Banking institutes can automate the following process with robotics:

• Risk and compliance reporting:

This process needs number of different applications to be accessed so as to provide the required data for reporting. Use of RPA has automated 90% of these processes, saving significant costs and time

• Anti-Money laundering (AML) and know your customer (KYC):

Both the processes are rule based data intensive processes and thus forms good candidate for application of RPA

• Accounting:

Several accounting and reporting processes involve repetitive daily activity that requires capturing data from multiple systems; these are well-suited for rule-based RPA

• Mortgages:

Given the number of third-party entities in the mortgage value chain, the significant use of paper (any proud owner of a mortgage can relate to this!) and the fragmented nature of the systems means RPA can play a key role in providing efficiencies while the industry undertakes a wider transformation

• Reconciliation:

RPA with predictive algorithms to reduce exceptions and automate the resolution process

• Front office:

Front-office and contact centre staff often need to access multiple applications to work with customers. RPA can be used to bring all relevant information from multiple systems to one screen for support staff to provide effective service

• Other areas:

RPA can be used in areas like cards & payments and asset & wealth management

The Benefits of robotics are:

• Cost Saver: Automating the processes using robotics can lead up to 80% cost reduction
• Higher Quality: Increased quality by reducing human errors
• Time Saver: Automation basically aims at reducing time and effort
• Integration: Runs on top of existing IT infrastructure and requires no IT transformation projects
• Scalable solutions: Scalable solutions that fits into your current set up

In conclusion, today’s banking firms are facing increasing demands to maintain as lean an operation as possible while also delivering exceptional client experience at the lowest possible cost. Robotic process automation is making it possible for financial institutions to achieve these goals and remain competitive in a sometimes                                                                turbulent, ever-changing environment.

‘You are either the one that creates the automation or you are getting automated’-

                                                                                                                   Tom Preston Werner

 

 

References:

https://www.accenture.com/in-en/insight-financial-services-robotic-process-automation

https://www.thecognizant.com/blog/rpa-in-banking-here-is-where-its-most-beneficial/

Intelligent Poka Yoke: When technology meets tradition

Think of the way Opsession went about composing this article for this month’s Just-In-Times and compare it to the manufacturing process. The process of manufacturing this post (the product) began with the assembling of components (relevant articles, concepts etc.). Then a refined part of the work-in-progress unit (draft) was checked by members of the editorial team for a quality check to ensure that the polished final piece reached the customers (i.e. you, the readers). There was a squiggly red line that would’ve highlighted the spell errors and grammatical mistakes while the post was a work-in-progress. In this context, it is a poka yoke tool. It flags an error at the source, so that the operator (the writer) can eliminate the defect before the piece leaves the station.

What if human operators on the assembly line, like Just-In-Times writers at their word processor, had some equivalent of a red squiggly line that highlighted errors as they were made?

Poka yoke, or error proofing, ensures that it is nearly impossible for workers to make a mistake. More specifically, it ensures that mistakes within the process are immediately visible, so they can be eliminated at the source. The effort and time required to implement, program and maintain, as well as eliminating the flaws of existing methods of data collection and observation are the major challenges of poka yoke. Manufacturing technology on the horizon promises to transform poka yoke by disrupting many of the elements that make the value it delivers so costly to achieve.

In this post, we lay out four benefits of the coming age of new factory technology that could be applied to simplify and accelerate poka yoke. But before that, lets take a look at what it is that makes traditional poka yoke so challenging to perform at scale.

The Costs and Rewards of Poka Yoke

The magnitude of the effort it takes to implement poka yoke speaks to its enormous value: a factory can benefit significantly from even the smallest gains in productivity and quality. But poka yoke is also self-limiting in the sense that the overhead required by some of the activities listed below makes the undertaking impossible—and makes the status quo more financially attractive than optimization.

First, it takes a lot of effort. Think of all of the overhead required by any one poka yoke solution:

• To start, you must quantify whether a problem exists.
• Then, you must prove that the problem is worth solving given all the other competing challenges you’re faced with.
• Then, you must spend hours or days on the line, painstakingly observing and measuring the status quo.
• Then, there’s the effort of crafting the solution—which could be as simple as a new fixture in a workstation, or as complicated as a redesign of a component part to prevent an error-prone state from being physically possible.
• And, finally, programming the specific logic of the solution while interfacing with existing fixtures and devices, including light curtains, interlock switches, safety mats and power guards. Programming this Rube Goldberg-like system is often done using PLCs (Programmable Logic Controller) and ladder logic—neither of which are easy to master, and both of which predate 3rd and 4th generation programming languages that have dramatically sped up the software development process.

 

Whichever path is chosen, all of this often requires the re-alignment of people and materials throughout the organization. Then, there is a need to do more observation to confirm the solution creates value, and further refine the solution to perfect it. And then the cycle starts over.

Complicating this is what is called the observer effect (famously articulated by Werner Heisenberg in quantum physics as the uncertainty principle): the act of observation perturbs the system.

In the factory, the act of observing an operator at their workstation changes the way they perform their work. An operator under observation may subconsciously or deliberately change their style to impress or game the observer. Put plainly, you could spend days on the line and not even see the behavior you’re trying to fix.

Coaching Networks

Companies hope that AI would help operators increase their precision and effectiveness on the assembly line—by observing their activities and flagging issues that an operator should address. A phrase for such a system has already been coined: a “coaching network”.

A coaching network is a system that guides workers to doing their jobs more effectively, even as they’re doing it.

Imagine if you had a digital assistant sitting and working with you during a next sales call, offering feedback and advice, calling your attention to the next script you should use, or helping you keep track of your customer’s objections.

This is one kind of coaching network. Intelligent poka yoke is another.

Despite the constant negative headlines suggesting that AI is making humans obsolete, AI should be seen as a tool for guiding human attention to the points where their mental cognition and physical agility add the most value to the process.

When lean manufacturing meets deep learning: Intelligent Poka Yoke

In an era of mass customization and ever-shortening times-to-market, the industry needs to accelerate poka yoke. Not by narrowing the focus, compromising the methods, or lowering expectations, but by turning to technology to amplify these principles and accelerate the outcomes.

In particular, there are four areas where newly available technology which automatically gathers and analyzes data from human activities will help.

1. Identifying the most important steps for optimization.Every poka yoke exercise currently begins with a question: Is this opportunity going to show return on investment (ROI) for the effort required to improve it? Intelligent poka yoke will arm the process engineer with the information that they need for every process element to more quickly identify the most important opportunities. Or, better yet, it will automatically reveal the best candidates for poka yoke—ones that align with key manufacturing KPIs like productivity and quality—before the engineer even thinks to search for them.

 

2. Shortening observation times. What if the process engineer didn’t have to painstakingly perform days’ worth of observations? What if, instead, they could complement a much shorter observation period with data and video that was already waiting for their analysis? A hypothesis could be proven simply by analyzing data that’s already been captured.

 

3. Enabling rapid experimentation. The success of a poka yokeproject is unknown until you’ve measured the changed state—something that you can’t measure until you’ve implemented the change. Which means that no poka yoke project is complete until a post facto cycle of observation and measurement has been performed. Intelligent poka yoke, if it fulfills requirement #2, by definition allows you to perform more experiments in less time. Instead of pausing your second project to perform measurements on your first, you can let your tools gather data while you focus on the next improvement.

 

4. Eliminating the observer effect. If data collection is not a one-off event but instead a persistent practice, then operators won’t get nervous with the presence of an engineer on the line. In fact, as we have seen, it becomes a part of daily life and is ignored. This means that the data gathered will be more valuable not just because there’s a far larger sample size, but also because there’s less bias in the data collection methods. The result is more accurate data, which leads to better outcomes.

 

Hello, Intelligent Poka Yoke

Poka yoke has remained stubbornly manual because Industry 4.0 technology has been focused on machines and machine-driven datasets.

Industry 4.0 is effectively blind to the activities of humans, even though humans remain manufacturing’s biggest contributor to value and to variability.

Lean manufacturing replaced mass manufacturing over the previous four decades. Perhaps intelligent lean manufacturing is the next?

The next generation of Industry 4.0 technologies will finally usher in true digital transformation in the factory by helping the human workforce increase the value they deliver—and accelerating the efforts of the engineers to reduce the variability they create.

The practice of poka yoke is about to get even more important because, with the next generation of tools, the overhead of poka yoke is poised to drop. When poka yoke can be built, applied and iterated at scale, minor improvements can cascade into major financial gains that may be felt as high as earnings per share. Far from accelerating automation, intelligent poka yoke—which brings computational assistance to the assembly line—might be a step towards true human/machine collaboration, and a driver for greater factory employment.

RPA: A revolution in business process automation

More CIOs are turning to an emerging technology practice called robotic process automation (RPA) to streamline enterprise operations and reduce costs. With RPA, businesses can automate mundane rules-based business processes, enabling business users to devote more time to serving customers or other higher-value work.

Others see RPA as a stopgap en route to intelligent automation (IA) via machine learning (ML) and artificial intelligence (AI) tools, which can be trained to make judgments about future outputs.

Below we take a look at what robotic process automation really is, and how CIOs can make the most of RPA in alignment with business goals.

What is robotic process automation?

RPA is an application of technology, governed by business logic and structured inputs, aimed at automating business processes. Using RPA tools, a company can configure software, or a “robot,” to capture and interpret applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. RPA scenarios range from something as simple as generating an automatic response to an email to deploying thousands of bots, each programmed to automate jobs in an ERP system.

COOs working for financial services firms were at the vanguard of RPA adoption, figuring out ways to use software to facilitate business processes without increasing headcount or costs, says Regina Viadro, vice president at EPAM Systems and adviser of the company’s IA practice. Viadro has worked on RPA engagements for clients in financial services, healthcare, retail and human resources, showing the breadth of RPA use today.

What are the benefits of RPA?

RPA provides organizations with the ability to reduce staffing costs and human error. David Schatsky, a managing director at Deloitte LP, points to a bank’s experience with implementing RPA, in which the bank redesigned its claims process by deploying 85 bots to run 13 processes, handling 1.5 million requests per year. The bank added capacity equivalent to more than 200 full-time employees at approximately 30 percent of the cost of recruiting more staff, Schatsky says.

Bots are typically low-cost and easy to implement, requiring no custom software or deep systems integration. Schatsky says such characteristics are crucial as organizations pursue growth without adding significant expenditures or friction among workers. “Companies are trying to get some breathing room so they can serve their business better by automating the low-value tasks,” Schatsky says.

Enterprises can also supercharge their automation efforts by injecting RPA with cognitive technologies such as ML, speech recognition, and natural language processing, automating higher-order tasks that in the past required the perceptual and judgment capabilities of humans.

Such RPA implementations, in which upwards of 15 to 20 steps may be automated, are part of a value chain known as intelligent automation (IA), Viadro says. “If we were to segment all of the major enterprises and ask them what’s on their agenda for 2018, close to 100 percent would say intelligent automation,” Viadro says.

By 2020, automation and artificial intelligence will reduce employee requirements in business shared-service centers by 65 percent, according to Gartner, which says the RPA market will top $1 billion by 2020. By that time, 40 percent of large enterprises will have adopted an RPA software tool, up from less than 10 percent today.

What are the pitfalls of RPA?

RPA isn’t for every enterprise. As with any automation technology, RPA has the potential to eliminate jobs, which presents CIOs with challenges managing talent. While enterprises embracing RPA are attempting to transition many workers to new jobs, Forrester Research estimates that RPA software will threaten the livelihood of 230 million or more knowledge workers, or approximately 9 percent of the global workforce.

Installing thousands of bots has taken a lot longer and is more complex and costly than most organizations have hoped it would be, Edlich and Sohoni say. The platforms on which bots interact often change, and the necessary flexibility isn’t always configured into the bot. Moreover, a new regulation requiring minor changes to an application form could throw off months of work in the back office on a bot that’s nearing completion.

A recent Deloitte UK study came to a similar conclusion. “Only three percent of organizations have managed to scale RPA to a level of 50 or more robots,” say Deloitte UK authors Justin Watson, David Wright and Marina Gordeeva.

Moreover, the economic outcomes of RPA implementations are far from assured. While it may be possible to automate 30 percent of tasks for the majority of occupations, it doesn’t neatly translate into a 30 percent cost reduction, Edlich and Sohoni say.

What companies are using RPA?

Walmart, Deutsche Bank, AT&T, Vanguard, Ernst & Young, Walgreens, Anthem and American Express Global Business Travel are among the many enterprises adopting RPA.

Walmart CIO Clay Johnson says the retail giant has deployed about 500 bots to automate anything from answering employee questions to retrieving useful information from audit documents. “A lot of those came from people who are tired of the work,” Johnson says.

David Thompson, CIO of American Express Global Business Travel, uses RPA to automate the process for canceling an airline ticket and issuing refunds. Thompson is also looking to use RPA to facilitate automatic rebook recommendations in the event of an airport shutdown, and to automate certain expense management tasks.

“We’ve taken RPA and trained it on how employees do those tasks,” says Thompson, who implemented a similar solution in his prior role as CIO at Western Union. “The list of things we could automate is getting longer and longer.”

But with many more CIOs mulling RPA, CIO.com asked some consultants for advice on how IT leaders can tackle the technology.

 

 

10 tips for effective robotic process automation

1. Set and manage expectations

Quick wins are possible with RPA, but propelling RPA to run at scale is a different animal. Dave Kuder, a principal with Deloitte Consulting LLP, says that many RPA hiccups stem from poor expectations management. Bold claims about RPA from vendors and implementation consultants haven’t helped. That’s why it’s crucial for CIOs to go in with a cautiously optimistic mindset. “If you go in with open eyes you’ll be a lot happier with the result,” Kuder says.

2. Consider business impact

RPA is often propped up as a mechanism to bolster return on investment or reduce costs. But Kris Fitzgerald, CTO of NTT Data Services, says more CIOs should use it to improve customer experience. For example, enterprises such as airlines employ thousands of customer service agents, yet customers are still waiting in the queue to have their call fielded. A chatbot, could help alleviate some of that wait. “You put that virtual agent in there and there is no downtime, no out sick and no bad attitude,” Fitzgerald says. “The client experience is the flag to hit.”

3. Involve IT early and often

COOs initially bought RPA and hit a wall during implementation, prompting them to ask IT’s help (and forgiveness), Viadro says. Now “citizen developers” without technical expertise are using cloud software to implement RPA right in their business units, Kuder says. Often, the CIO tends to step in and block them. Kuder and Viadro say that business heads must involve IT from the outset to ensure they get the resources they require.

4. Poor design, change management can wreak havoc

Many implementations fail because design and change are poorly managed, says Sanjay Srivastava, chief digital officer of Genpact. In the rush to get something deployed, some companies overlook communication exchanges, between the various bots, which can break a business process. “Before you implement, you must think about the operating model design,” Srivastava says. “You need to map out how you expect the various bots to work together.” Alternatively, some CIOs will neglect to negotiate the changes new operations will have on an organization’s business processes. CIOs must plan for this well in advance to avoid business disruption.

5. Don’t fall down the data rabbit hole

A bank deploying thousands of bots to automate manual data entry or to monitor software operations generates a ton of data. This can lure CIOs and their business peers into an unfortunate scenario where they are looking to leverage the data. Srivastava says it’s not uncommon for companies to run ML on the data their bots generate, then throw a chatbot on the front to enable users to more easily query the data. Suddenly, the RPA project has become an ML project that hasn’t been properly scoped as an ML project. “The puck keeps moving,” and CIOs struggle to catch up to it, Srivastava says. He recommends CIOs consider RPA as a long-term arc, rather than as piecemeal projects that evolve into something unwieldy.

6. Project governance is paramount

Another problem that pops up in RPA is the failure to plan for certain roadblocks, Srivastava says. An employee at a Genpact client changed the company’s password policy but no one programmed the bots to adjust, resulting in lost data. CIOs must constantly check for chokepoints where their RPA solution can bog down, or at least, install a monitoring and alert system to watch for hiccups impacting performance. “You can’t just set them free and let them run around; you need command and control,” Srivastava says.

7. Control maintains compliance

There are lot of governance challenges related to instantiating a single bot in environment let alone thousands. One Deloitte client spent several meetings trying to determine whether their bot was male or female, a valid gender question but one that must take into account human resources, ethics and other areas of compliance for the business, Kuder says.

8. Build an RPA center of excellence

The most successful RPA implementations include a center of excellence staffed by people who are responsible for making efficiency programs a success within the organization, Viadro says. Not every enterprise, however, has the budget for this. The RPA center of excellence develops business cases, calculating potential cost optimization and ROI, and measures progress against those goals. “That group is typically fairly small and nimble and it scales with the technology staff that are focused on the actual implementation of automation,” Viadro says. “I’d encourage all IT leaders across different industries to look for opportunities and understand whether [RPA] will be transformative for their businesses.”

9. Don’t forget the impact on people

Wooed by shiny new solutions, some organizations are so focused on implementation that they neglect to loop in HR, which can create some nightmare scenarios for employees who find their daily processes and workflows disrupted. “We forget that it’s people first,” Fitzgerald says.

10. Put RPA into your whole development lifecycle

CIOs must automate the entire development lifecycle or they may kill their bots during a big launch. “It sounds easy to remember but people don’t make it a part of their process.”

Ultimately, there is no magic bullet for implementing RPA, but Srivastava says that it requires an intelligent automation ethos that must be part of the long-term journey for enterprises. “Automation needs to get to an answer — all of the ifs, thens and whats — to complete business processes faster, with better quality and at scale,” Srivastava says.

For further reading refer to : https://www.computerworld.com.au/article/641674/what-rpa-revolution-business-process-automation/