Warehouse of the Future: Adopting Automation within Your Supply Chain – Part 1 of 2

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There’s currently a digital supply chain transformation that’s happening faster than the physical supply chain can react, requiring hybrid solutions in semi-automated environments where humans and robots work in tandem. New incentives to modernize operational capabilities should be added that capture efficiencies not previously achieved, while laying the foundation of a digitalized supply chain, continuously re-evaluating plans and evolving for performance.

Automation has been looked at as a solution to operational challenges, but trends in the marketplace signify an unprecedented rate of adoption taking hold in the coming decade. E-commerce is driving service expectations to levels that may not be achieved without the use of highspeed picking alternatives to manual operations. The aging generations in mature economies and challenges securing a loyal millennial workforce for repetitive tasks are creating increased disruption to staffing, forcing employers to look to automation to offset risk of labor shortages. Continued innovation has reduced costs of entry for automated capabilities, delivering improved business case justification for automation of many forms.

With such a strong justification, operations leaders across the globe are seeking ways to capture the potential that automation offers. Large scale transformation of distribution networks is capital intensive, however, and rarely warranted given the pace of change – however rapid – and market uncertainties. Therefore, we’re forced to look within existing environments to identify opportunities to introduce automation into existing facilities, combining automated equipment with manual operations, which requires the added complexity of orchestrating work across semiautomated operations. This scenario introduces the question of how to create an optimal environment allowing warehouse management systems (WMS) to orchestrate work across manual and automated areas to ensure efficient operations and maintain quality and service levels.

Part 1 of this 2-part blog series will take a deeper dive into today’s automation systems landscape and retrofitting today’s supply chain with automation. Part 2 of the series will cover disruptive technologies and digitalization and next generation capabilities.

The Current Landscape of Automation Systems

In automated environments, WMS often work alongside warehouse control systems (WCS) that manage the routing of containers as they traverse the material handling equipment, and warehouse execution systems (WES) which often have basic task management capabilities but not the level of control or optimization of a WMS. Below are a few general groupings of automation that typically leverage these entities in different ways.

  • Conveyors and sortation equipment receive destination / routing information from the WMS and leverage the WCS to divert containers to the appropriate location.
  • Pick execution equipment, including pick-to-light, carousels, or A-frames will receive pick instructions from the WMS and rely on the WCS to control the MHE. At times, these devices will manage task distribution and user interfaces for the performance of picks, though often, the WMS will manage the tasks through prioritization, and provide a common user experience (using consistent equipment where appropriate) for work performed in the pick modules and in bulk storage which feeds it (this work would include putaways, cycle counting, and picks where appropriate). Often, a WES has been sufficient for high volume outbound operations in retail, but with increasing emphasis on service levels, the advanced functionality of a WMS specific to inventory accuracy, pick module replenishment, cross-docking, and exception handling, the WMS brings a strong justification for a two-pronged approach.
  • Automated guided vehicles (AGVs) and automated storage and retrieval systems (ASRS) are well established, though adoption is increasing as more forklift providers offer driverless units. These units can take direction from a WMS (typical when involved in semi-automated environments) or WES (often used in ASRS racking systems where materials are commingled or when the vehicles can follow multiple routes to alleviate congestion). In either scenario, a WMS is often utilized to manage inventory allocation to customer and order.
  • Palletizers use visual determination for pallet building capabilities, but most in use require some level of consistency in product dimensions at the layer level. Advanced pallet building and robotic arm picking capabilities are increasing in use, but require some consistency in dimensions. Improvements in digital sensing will soon be changing the game here.

Retrofitting Today’s Supply Chain with Automation

Automation adoption will continue to accelerate in response to advanced service level expectations and e-commerce, with a focus on scale and speed, whereas a continued migration of margin focused businesses will drive adoption of driverless vehicles and high-density storage modules, especially in cold storage or mega-cities with high volume real estate. The introduction of automation into the existing facilities will bring challenges, such as:

  • Traditional footprints, system capabilities, and business processes will be challenged when faced with the introduction of conveyance, sortation, and pick execution equipment. A natural inclination to delineate businesses, and potentially create channel-specific operations, can result in artificially inflated inventory levels and/or reduced service levels in increasingly sensitive environments to failures in this area. Multi-channel capabilities can be achieved, often driving operational leaders to adopt pick execution capabilities to distribute work without recognizing the backlash to overall service levels of having disparate capabilities with traditional WMS controlled processes. The results, if not thought through, can have repercussions on inventory accuracy, exception management, and operational efficiency.
  • The introduction of driverless vehicles (AGV or ASRS) offer strong advantages in terms of scale and cost, ROI projections in union environments can often deliver break even points less than a year after go-live, even in new projects. However, legacy storage equipment and material flow can introduce limitations. The environments best prepared for the introduction of driverless forklifts are those managing full pallets in bulk locations (where dimensions are predictable and stack requirements are well documented), or those where racking capacity is capable of managing fixed locations that can be tracked in the WMS (if locations can be dedicated to a specific lot), or in the WCS (where multiple pallet locations can be managed by the WCS but the WMS can manage storage/allocation in concert with non-automated areas). In more complex operations, the WCS can take a more active role in determining work and allocation, but this often drives customization and redundancy with WMS functions specific to the needs of the business.
  • More robust, piece level management in advanced pick modules controlled by ASRS such as goods-to-person automation, offer advanced capabilities for high volume distributors and e-tailers. Often, this will require tote storage of product to standardize the storage capabilities, though concessions for non-conveyables must be considered. Integrating pick and pack operations with traditional areas of the same operation also force decisions on how to integrate inventory management with shipping capabilities, adding complexity to projects as WMS and WCS providers offer similar capabilities.

Check back for part-2 of the blog series, where we’ll go into more detail around the technologies driving next generation warehouse automation and digitalization and next generation capabilities.

For more information download the Future Series white paper, “Adopting automation in the digital age.”

About the Author

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Matthew Butler, Industry Strategies Director, JDA Software

More more information contact, 0414 966 232.

Visit www.jda.com

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Big Data – Is Your Supply Chain Ready?

You receive a notification on your phone that a critical shipment from your China factory has missed its filing deadline with the customs broker.

Your logistics manager is alerted that there is an 80% chance that the components he’s waiting for are likely to be delayed another 48 hours by excessive port traffic and your GTM software advises diverting the shipment to an alternate port facility.

Your compliance officer is informed that there is a 95% chance that a shipment of parts from Malaysia is likely to be held for up to three days to be subjected to a detailed customs inspection.

If you think this type of information would be of great assistance to your supply chain business planning and operations, you are not alone. It is this type of integrated data and communications that are becoming the backbone of the Big Data led revolution underway in the supply chain.

The human brain can only process and make use of a limited amount of information before it becomes overwhelmed and unable to effectively recognise patterns and trends. But powerful algorithms and the software platforms they drive can take in almost unlimited numbers of data points and process them to generate insights impossible for an individual or even an entire organisation of individuals to identify. And powering this technology-driven transformation of the supply chain is Big Data.

Big Data vs Small Data

To really understand how technology is transforming supply chain, it is important to understand how Big Data differs from any other form of information gathering. Data has always been crucial to efficient supply chain operations so what has actually changed in recent years? How is “Big Data” different from the analysis of “small data” that has always occurred in the industry?

Big Data refers to sets of both structured and unstructured data with so much volume that traditional data processing systems are inadequate to cope with it all. It can be further defined by some of the basic properties that apply to it:

Variety – data being generated from a wide number of varied sources

Volume – while there is no set distinction between where small data stops and Big Data starts, Big Data requires large storage requirements for the data, often measured in many multiples of terabytes

Velocity – the speed at which the data can be acquired, transferred and stored

Complexity – difficulties encountered in forming relevant relationships in data, especially when it is taken from multiple sources

Value – the degree to which querying the data will result in generating beneficial outcomes

The most important property related to Big Data is as the name implies, volume. We normally think of data purely in terms of text or numbers but it can include everything from the billions of emails, images, and tweets generated every day.

In fact, data generation is expanding at a rate that doubles every two years. And human and machine-generated data is growing at 10 times the rate of traditional business data.

IT World Canada projects that by 2020, you would need a stack of iPad Air tablets extending from the earth to the moon to store the world’s digital data.

But the real focus behind a preference for Big Data analysis over small data systems is the ability to uncover hidden trends and relationships in both structured and unstructured data. In most cases, using small data collection and analytics processes simply cannot identify crucial information in a timely manner to allow key decisions to be made or opportunities to be taken advantage of. In other cases, using small data systems is simply a waste of resources and leads to disruptions to supply chain operations.

By contrast, if used correctly Big Data is the key to enhancing supply chain performance by increasing visibility, control, agility, and responsiveness. Making decisions based on high-quality information in context can benefit the full range of supply chain operations – from demand forecasting, inventory and logistics planning, execution, shipping, and warehouse management.

Big Data possibilities

Big Data analytics becomes a vital tool for making sense of the huge volumes of data that are produced every day. This data comes from a whole range of activities undertaken by people associated with supply chain, whether they be customers, suppliers, or your own staff. The range and volume of this data are continuously increasing, with billions of data points generated by sources we see as directly linked to supply chain such as network nodes and transaction and shipping records as well as other areas that more indirectly impact supply chains such as retail channels and social media content.

But it is increasingly becoming necessary to harness this data in order to remain competitive. This is evident from statements such as the below:

“Big Data is certainly enabling better decisions and actions, and facilitating a move away from gut feel decision making.” -Anthony Coops, Asia Pacific Data & Analytics Leader, KPMG Australia

At the same time, he recognises that solutions need to be put in place that allows for people and organisations to have complete faith in the data so that managers can really trust in the analytics and be confident in their decision making.

The need for confidence in the analytics is evident when considering the examples such as where GTM software has the information and capabilities to advise ahead of time to divert shipping stock to an alternate port or that a product is likely to be held up in customs. These types of decisions have potentially large financial consequences but when implemented correctly, it is easy to see how supply chain operational efficiency can be significantly boosted by effective use of Big Data analytics.

Many organisations are also using Big Data solutions to support integrated business planning and to better understand market trends and consumer behaviours. The integration of a range of market, product sales, social media trends, and demographic data from multiple data sources provides the capability to accurately predict and plan numerous supply chain actions.

IoT and AI-based analytics are used to predict asset maintenance requirements and avoid unscheduled downtime. IoT can also provide real-time production and shipping data while GPS driven data combined with traffic and weather information allows for dynamically planned and optimised shipping and delivery routes. These types of examples provide a glimpse into the possibilities and advantages that Big Data can offer in increasing the agility and efficiency of supply chain operations.

Disruptive technologies

What is driving these possibilities is the development of numerous disruptive technologies as well as the integration of both new and existing technologies to create high-quality networks of information. Disruptive technologies impact the way organisations operate by forcing them to deal with new competitive platforms. They also provide them with opportunities to enter new markets or to change the company’s competitive status.

By identifying key disruptive technologies early, supply chain organisations can not only be better placed to adapt to changing market conditions, they can also gain a distinct advantage over others in the industry that are reluctant to embrace change.

In terms of Big Data based disruptive technologies, these are largely driven by the effects of constantly evolving and emergent internet technologies such as the Internet of Things combined with increased computing power, AI and machine learning based analytics platforms, and fast, pervasive digital communications. These technologies then act as drivers that spawn new ways of managing products, assets, and staff as well as generating new ways of thinking about organisational structures and workflows.

IoT

After being talked about for many years, we are now starting to see the Internet of Things really taking shape. There will be a thirty-fold increase in the number of Internet-connected physical devices by 2020 and this will significantly impact the ways that supply chains operate.

IoT allows for numerous solutions to intelligently connect systems, people, processes, data, and devices via a network of connected sensors. Through improved data collection and intelligence, the supply chain will benefit from greater automation of the manufacturing and shipping process becomes possible through enhanced visibility of activities from the warehouse to the customer.

Cloud-based GPS and Radio Frequency Identification (RFID) technologies, which provide location, product identification and other tracking information play a key role in the IoT landscape. Sensors can be used to provide a wealth of information targeted to specific niches within supply chain such as fresh produce distribution where temperature or humidity levels can be precisely tracked along the entire journey of a product. Data gathered from GPS and RFID technologies also facilitates automated shipping and delivery processes by precisely predicting the time of arrival.

Big Data analytics

Big Data analytics encompasses the qualitative and quantitative techniques that are used to generate insights to enhance productivity. The more supply chain technologies are reliant on Big Data, either in their business model or as a result of their impact on an organisation, the more organisations have to rely on the effective use of Big Data analytics to help them make sense of the volumes of data being generated. Analytics also helps to make it possible to understand the processes and strategies used by competitors across the industry. Using analytics effectively allows an organisation to make the best decisions to ensure they stay at the forefront of their particular market sector.

As corporations face financial pressures to increase profit margins and customer expectation pressures to shorten delivery times. the importance of Big Data analytics continues to grow. A Gartner, Inc. study put the 2017 business intelligence and analytics market at a value over USD$18 billion, while the sales of prescriptive analytics software is estimated to grow from approximately USD$415 million in 2014 to USD$1.1 billion in 2019.

Over time, the effectiveness and capabilities of analytics software also continue to improve as machine learning-based technologies take forecast data and continually compare it back to real operational and production data. This means that the longer an organisation operates its analytics software, the iterative nature of artificial intelligence powered algorithms means that the performance and value of the software improve over time. This leads to benefits such as more accurate forecasts of shipping times or supplier obstacles and bottlenecks.

Consumer Behaviour Analysis

Although it may not initially seem as vital to supply chain as other disruptive technologies, consumer behaviour analysis can have a huge impact on businesses working in supply chain, especially e-commerce businesses. Known as clickstream analysis, large amounts of company, industry, product, and customer information can be gathered from the web. Various text and web mining tools and techniques are then used to both organise and visualise this information.

By analysing customer clickstream data logs, web analytics tools such as Google Analytics can provide a trail of online customer activities and provide insights on their purchasing patterns. This allows more accurate seasonal forecasts to be generated that can then drive inventory and resourcing plans. This type of data is extremely valuable and is crucial for any organisations operating in the e-commerce space. While retailers and consumer companies have always collected data on buying patterns, the ability to pull together information from potentially thousands of different variables that have traditionally been collected in silos provides enormous economic opportunities.

Potential drawbacks and challenges

Despite the huge opportunities presented by implementing Big Data powered solutions, there can be intimidating barriers to entry when it comes to putting in place Big Data collection and analytics solutions. This can emerge across a range of areas including the complexities around data collection and the difficulties of putting in place the technologies and infrastructure needed to turn that data into useful insights.

Getting complete buy-in

One impediment to adopting a holistic Big Data approach centres around having unified support at all levels of your company to adopt comprehensive Big Data systems. Management commitment and support is crucial and large-scale initiatives of this type usually occur from the top down. However, Big Data analytics type initiatives usually originate at mid-level, from people who actually collect and use data day to day. This means that for this issue, the need for implementation must often be sold upwards. In some cases, upselling the importance of Big Data to management that doesn’t understand why that type of expense is necessary is extremely challenging.

Sourcing clean data

One of the other main challenges is undoubtedly sourcing appropriate and consistent data. There’s no use getting high-quality data if it doesn’t directly apply to your particular market sector. Nor is there much benefit to be gained from obtaining high-quality data but being unable to consistently source it at the same regularity to enable it to build a long-term profile of the company’s operations and market forces. These challenges are often related to technical issues such as integration with previously siloed data or data security concerns.

Richard Sharpe, CEO of Competitive Insights, a supply chain analytics company, believes that the data quality problem is a complex issue that can have many different causes. However, he believes that these challenges can be overcome by management having a clear understanding of what they’re trying to achieve. “You have to show that what you’re ultimately trying to do with supply chain data analytics is to make the enterprise more successful and profitable.” This then leads to support being provided by company leadership who, in tandem with operations managers, can develop the processes required to govern quality data collection. This includes proper consultation with subject matter experts who can help ensure that all data is properly validated.

Managing data volumes

New technologies make it possible for supply chain organisations to collect huge volumes of information from an ever-expanding number of sources. These data points can quickly run into the billions, making it challenging to analyse with any level of accuracy or lead directly to innovation and improvement.

This means that despite many organisations embracing Big Data strategies, many do not actually derive sustainable value from the data they’re accumulating because they begin to drown in the sheer volume of data or don’t have the appropriate software and management tools to make use of it. A common phrase used to summarise this effect is “paralysis by analysis”. Without a thorough understanding of the technologies and systems needed to process and store the data collected, this can be an easy condition for an organisation to become afflicted by.

Building the infrastructure

Companies need to invest in the right technologies to have a true 360-degree view of their business. And in many cases, these technologies can involve large initial capital outlays. Getting the infrastructure in place is key to being able to collect, process, and analyse data that enables you to track inventory, assets and materials in your supply chain.

Putting in place the infrastructure may also require additional training expenses, so that staff are properly trained in how to use new software platforms or to maintain sensors and other new IoT devices. In some cases, this will extend to requiring hiring new talent capable of using and interpreting new analytical tools.

Conclusion

Big Data offers huge opportunities to supply chain organisations, as vital information contained within multiple data sources can now be consolidated and analysed. These new perspectives can reveal the insights necessary to understand and solve problems that were previously considered too complex. New insights can also encourage organisations to scale intelligent systems across all activities in the supply chain, embedding intelligence in every part of the business.

There is also no doubt that implementing comprehensive Big Data solutions can involve new and significant challenges. However, once the new infrastructure and processes are in place, the nature of modern Cloud-based networks allows for data to be accessed easily from anywhere at any time. It also allows for other benefits beyond cost reduction and production gains to be realised over time, such as ongoing rather than just one-off efficiency gains and improved transparency and compliance tracking across the entire organisation.

See original blog here

 

About the Author

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Bastian Managing Director, Tony Richter, is a supply chain industry expert with 7+ years executing senior supply chain search across APAC. He works exclusively with a small portfolio of clients and prides himself on the creation of a transparent, credible, and focused approach. This ensures long-term trust can be established with all clients and candidates.

To find out more about the challenges and opportunities offered by Big Data to supply chain, or to gain a deeper understanding of the technology and Big Data trends currently underway in the industry, talk to an industry expert at http://bconsult.io/

The $100 Billion Returns Question

Billion Dollar Return QuestionBy Karin Bursa, Executive Vice President, Logility

Now that the holiday season is behind us, retailers can sleep easy, right? Well, no. Seasons are now both shorter and more frequent which means you quickly move on to the next one. However, a large and rapidly growing issue has emerged: the cost of returns. Unwanted gifts, incorrect sizes, styles and fits that didn’t match expectations, the reasons are countless. To attract and retain customers, many retailers strive to make the return process as frictionless as possible. But at what cost? The ease of returning an online purchase has turned the bedroom into the retail fitting room. Consumers now purchase multiple variations of the same product to “try on” at home and then return the rest.

Following the 2017 holiday season, several industry pundits proclaimed retailers would lose about $90 billion (yes… billion!) in returned merchandise that could not be resold (Good news for FedEx and UPS: People just opened $90 billion in unwanted gifts). Recently, another report published claiming this number reached $107 billion for 2017 ($107 Billion Lost In Returns). Regardless of the final number, we are talking about a lot of money that simply should not be “thrown away to erode margins.”

Returns can be forecasted and much of that inventory can be placed back onto store shelves or made available through ecommerce, discount locations, etc. So, if retailers know the returns are going to happen, why are the losses so high? I sat down with retail industry veteran Jim Brown to learn more.

 

Karin: Are you surprised at the high cost of returns?

Jim: Not at all. When I was in the shoe industry, we experienced a higher-than-average rate of returns that could not be sold. Today, the consumer’s mindset has evolved and applied the same logic we saw in the shoe industry, try on a wide variety before you find the right one, to the rest of their purchasing habits. Consumers today are more comfortable ordering more and returning most of that order. It is too easy to add items to an online shopping basket knowing you have the option to return the merchandise with free shipping. And, since you used a credit card, you’ll never have to pay out-of-pocket.

 

Karin: Based on your experience, what happens to this merchandise?

Jim: Basic items typically do not make up the bulk of the returned merchandise. So, if the items are re-saleable, a lot of them will move into a markdown status by the time they make their way back to the store, or into the available warehouse inventory due to selling seasons. Unfortunately, it’s not as simple as taking it from the initial customer and placing it back on the shelf. Often, there are many hands that will interact with the merchandise—tags may have been removed, packaging may be defective, etc. All of this takes time, resources and investment to re-create and ready the merchandise for resale. Time is money and each ‘touch’ depletes your margin.

 

Karin: Why are retailers not able to place the merchandise back in circulation?

Jim: Most retailers will try, or have a process that should accommodate getting the product back into the inventory. The reality is most retail supply chains are optimized to bulk move allocated or replenished goods to the stores/locations. Handling one item at a time is a very labour-intensive activity. Determining if the item is damaged, repairable, tagged, packaged appropriately, etc. all adds to this cost. If you consider the margin on a single item, the least costly option may be to not handle it all. Of course, this is dependent upon the cost and margin of the item, so you need policies in place that accommodate all types of merchandise sold. This is the same reason why reducing store-to-store transfers is so important for retailers.

 

Karin: Are certain industries more prone to this issue?

Jim: Definitely. Health and beauty is a good example because this industry is heavily regulated. Once a safety seal is broken, that item is off the market for resale. Ready-to-wear is another good example due to its specific sizing which is also prone to returns. Technology becomes obsolete quickly, and the packaging is almost impossible to return to its’ ‘factory fresh’ condition. This forces the majority of these returns to be sold at a markdown, contribute to that staggering number you mentioned earlier and result in ‘open box’ promotions and discounts.

 

Karin: What are some of the ways retailers mitigate this issue?

Jim: This has become increasingly more difficult. In the past, retailers could require return authorizations or a short return window. However, in today’s competitive environment where shoppers have more options, retailers are hesitant to put up any customer service barriers. The prevalence of social media means one bad experience can be amplified across a broad audience and impact future sales. If I know the return process will be a hassle, chances are I will shop elsewhere. The best way to mitigate returns is to get the transaction right with the customer at the point of purchase. By providing as much information about the item to them as possible, easy access to customer reviews, etc. will lessen the chance that an item will come back up front. Some retailers are experimenting with virtual dressing rooms and other innovative technology to help minimize the volume of returns.

 

The cost of returns is truly an astonishing figure; however, as Jim outlines retailers are just not set up to handle the one-off item returns in a cost-effective manner. There are ways to minimize the burden including getting the sale right from the start. Additionally, retailers need to forecast the returns as a part of their planning process and develop more cost-effective measures for handling the merchandise as it comes back. If you are able to better predict the amount of returned merchandise you are likely better equipped to collaborate with your suppliers and partners to mitigate the cost to you while still delighting your customers.

 

About the Author

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Karin Bursa, Executive Vice President, Logility

With more than 25 years of experience in the development, support and marketing of enterprise software solutions, Karin is able to provide The Voyager Blog several provoking perspectives including market-shaping events, end-user perspectives and technical reviews. She is a widely quoted source on the evolution of the supply chain, frequent author to many leading publications, and can be found speaking at many of the industry’s leading conferences.

Distribution Disruption Causes KFC Chicken Shortage

Last week, even the Colonel couldn’t help KFC restaurants in the United Kingdom. Approximately 800 of the 900 KFCs in the U.K. temporarily closed because of a chicken shortage, according to CNN Money.

The supply problem was pinpointed to issues with the restaurant chain’s new distribution partner, DHL. A KFC spokesperson described the issue as “teething problems” related to the transition that happened just a week before. DHL leaders reported that many of its deliveries were incomplete or delayed because of operational issues.

Although the exact details of this disruption management plan were not outlined in the article, KFC’s explanation to its customers was quick. The restaurant chain first alerted consumers via social media with a post that read, “Some chickens have now crossed the road, the rest are waiting at the Pelican Crossing.” KFC also set up a landing page on its website to report store operations.

Then KFC purchased a full-page ad in British newspapers to apologize to its clientele. The ad featured an empty chicken bucket with the chain’s initials scrambled to read “FCK,” a nod to an expletive that, in this case, means oops or whoops, along with an apology note. The company continues to offer humor-filled updates via social media. Although 95 percent of the restaurants have reopened, some are still operating with limited menus because of the distribution disruptions.

Public relations expert Rupert Younger, director of the Oxford University Centre for Corporate Reputation, applauded KFC’s effort to apologize and explain the issue to the public. “It speaks to a business that understand[s] that mistakes were made and they’re prepared to have fun at their own expense,” he said. He also told CNN Money that he was impressed that KFC’s ad did not outright blame DHL for the issue. Younger expects that KFC will soon face an increased demand because of its open, authentic and humorous apology.

I think we can all learn a lesson from KFC here. Disruptions happen, and supply challenges can be a headache for everyone involved. But, while you’re fixing the problem and planning for the future, transparency and a bit of humor can go a long way with your customers. A positive attitude can foster positive relationships going forward.

Risk and recovery

As KFC recovers from this supply chain challenge, no doubt the restaurant chain will update its disruption management strategy. One potential preventive measure could be to add protective capacity, which the APICS Dictionary defines as, “The resource capacity needed to protect system throughput — ensuring that some capacity above the capacity required to exploit the constraint is available to catch up when disruptions inevitably occur.”

When managing disruptions, it helps to have a risk management plan in place. APICS can equip you with the tools to build a risk management plan to anticipate and recover from disruptions. By participating in APICS Risk Management seminars and elective topic presentations at APICS 2018, you will learn about risk and supply chain management, how to assess and control risk, and more. In addition, earning your APICS Risk Management Education Certificate shows your employer that you are ready to tackle risks for your company. Visit apics.org/risk to learn more.

About the author
Abe EshkenaziAPICS CEO

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Turning Reverse Logistics into Profit

While retailers are still celebrating one of the strongest holiday buying seasons in recent years, the secondary retail market is now enjoying its own successful season. Once consumers take their unwanted items back to retailers, resellers and reverse logistics groups acquire those items to turn their own profits, according to an article in The Wall Street Journal.Golden_Passenger_1

The National Retail Federation reports that holiday sales totalled nearly $692 billion in the last two months of 2017. Reverse logistics provider Optoro estimates that about 13 percent, or $90 billion, of that merchandise, will be returned by the end of this month. The most commonly returned items include clothing and apparel, electronics, beauty products, and sports and outdoor gear.

About half of this returned merchandise is restocked on the retailers’ shelves and often resold at a discount. Some 5 billion pounds of merchandise is just thrown away because this option is cheaper than restocking and reselling the items. The remainder is picked up and sold by the secondary retail market. Retailers have actually improved their reverse logistics processes in the past few years, with many being able to process hundreds or thousands of items a day, noted Tony Sciarrotta, executive director of the Reverse Logistics Association. Some retailers even add a second logistics shift to help manage the returned items and move them to the next selling point as quickly as possible, he said.

As a result of these return trends, January and February tend to be the busiest months for resellers and the reverse supply chain, explained Howard Rosenberg, chief executive of B-Stock Solutions. B-Stock Solutions manages liquidation sites for Best Buy and Sears, among other major retailers, and auction sites for retailers such as Costco, Macy’s, JCPenney and Lowe’s. “It’s just mayhem during this period,” Rosenberg said.

Resellers acquire the returned items through liquidation sites at a deep discount, enabling them to turn a profit. For example, last week, Best Buy sold 49 returned washing machines and dryers on one online auction site for $13,300 — a 68 percent discount. Similarly, on the same day, Sears resold four pallets of sportswear, intimate apparel and accessories for only $5,825 — a 93 percent discount. Damaged or bulk items usually have the greatest discounts.

Because of the strong holiday selling and returning seasons in the past few years, the resale market is stronger than ever. Post-retail sales of returned and overstocked items totalled $554.2 billion in 2016 — only $137.8 billion less than 2017’s November and December sales — and have been growing at approximately 7.5 percent a year, reported Zac Rogers, an operations and supply chain professor at Colorado State University. Nearly half of those 2016 sales were collected by salvage dealers and online auction houses, the remaining half collected by smaller vendors like dollar stores, factory outlets, pawn shops and flea markets.

The secondary retail market also received a volume surge this year following the rise in online purchases, which are more likely to be returned than items purchased in a physical store.

In conjunction, online liquidators like B-Stock, Liquidation.com and Optoro’s Bulq.com have grown their businesses. Online auction sales have increased 66 percent in the past 10 years, and combined sales through factory outlets, dollar stores and value retailers have more than doubled.

Be a part of the action

These retail trends are fueling the growth of the reverse supply chain, which the APICS Dictionary defines as, “The planning and controlling of the processes of moving goods from the point of consumption back to the point of origin for repair, reclamation, recycling or disposal,” or, in this case, resale. The growing secondary retail market will need resources to collect, manage and move an increasing amount of inventory to the new end users.

APICS offers resources to help you and your company participate in the growing reverse supply chain. Consider earning your APICS Certified in Logistics, Transportation and Distribution (CLTD) designation. The APICS CLTD program covers reverse logistics as well as capacity planning and demand management, order management, inventory and warehouse management, transportation, global logistics, logistics network design, sustainability and other important topics. Learn more at asci.org.au/cltd.

Author – CEO, APICS
Abe Eshkenazi

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Changing the Status Quo to Stay Ahead of the Amazon Effect

By Henry Canitz

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Planning teams face multiple dilemmas including promoting and supporting top line growth, containing cost, efficiently managing on-going operations and finding new ways to drive innovation. To complicate matters, supply chains are growing more complex as product proliferation and customer service expectations rise driven in part by the “Amazons” of the world.

Increasing complexity due to larger product portfolios drives demand volatility, more distribution channels and wider-spread supply chain networks. Traditionally, complexity required increasing inventory levels to buffer against the unknown, which in turn brought about more scrutiny from senior management as working capital increased. The answer, while simple to state, seems to allude many organisations: hold the right amount of inventory in the right locations to improve cash flow and provide better responsiveness to dynamic customer demand. Industry research and surveys point to Inventory Optimisation (IO) as a key capability to combat these growing supply chain stresses.

A form of prescriptive analytics, IO determines where and how much inventory to hold to meet a designated service level while complying with specific inventory policies. Through sophisticated algorithms, IO makes stocking recommendations to satisfy demand with the least amount of inventory. Inventory Optimisation can have a huge financial impact by minimizing inventory and freeing up working capital while guaranteeing the right stock is on hand, when and where needed.

Change the Status Quo through Multi-echelon Inventory Optimisation

Multi-echelon Inventory Optimisation (MEIO) goes a step further to simultaneously optimize stock locations and amounts across all inventory types in a supply chain network. Through advanced mathematical algorithms, MEIO models inventory flows through every interdependent stage and location of a supply chain to create an optimal configuration of internal and external inventory buffers to handleScreen Shot 2018-02-13 at 12.18.12 pm demand and supply uncertainty. MEIO can model component/raw, work-in-process (WIP) and finished goods inventory to ensure the right amount of each is stocked in the right locations enabling powerful postponement strategies.

MEIO’s rapid “what-if” scenario analysis to modify stock buffers (lowering some, raising others) and revamped policies and targets around the supply chain has shown in the real world to reduce inventory 10% to 30%, freeing millions (and in some cases, billions) in working capital that was trapped in excess stock and carrying costs.

Multi-echelon Inventory Optimisation, at its simplest form, enables the trade-off between service level and inventory cost modelled across the efficient frontier (see Figure 1). The Inventory Efficient Frontier shows that, for any status quo, it will always cost more to achieve higher service levels. However, through MEIO initiatives we can change the status quo and create a series of new curves that deliver a desired service level at less cost than the formerstate allowed.

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Figure 1: The Inventory Efficient Frontier

 

MEIO’s Impact on the Supply Chain

MEIO modelling compares actual demand to forecast, and actual receipt of goods to the plan for each SKU. MEIO models identifies forecast accuracy and safety stock issues while factoring historical forecast accuracy into the equation enabling predictive service level calculations. This fact-based approach to inventory targets allows you to right-size inventory by SKU and location.

MEIO strategic inventory modelling answers more difficult questions, i.e. where to make or stock products or the impact of distribution or manufacturing facility closures and openings. Strategic inventory modelling can provide quick, side-by-side scenario analysis to help make the right decisions. MEIO enables timely answers to complex “what-if” questions including impacts of channel changes and stocking policies across a complex and volatile omni-channel distribution network.

 

A Proven Three Step Approach to MEIO:

A simple three-step approach has been proven effective to achieving a successful MEIO initiative:

  1. Assess your organization’s capabilities from the perspectives listed below to understand your current state and to lay the foundation for a solid business case that delivers real-world results:
  • Inventory performance
  • Business process and inventory management expertise
  • Technology and organizational readiness
  1. Create a future state MEIO capability—process, technology, organization—that provides your supply chain team with a roadmap to success.
  2. Drive fundamental strategic changes that create greater resiliency and agility throughout the supply chain and establish a cycle of continuous improvement.

 

Time for Change

The benefits of Multi-echelon Inventory Optimization (MEIO) are well established by hundreds of companies of all sizes and in many industries. Leading organisations have shown that right-sizing inventory buffers and restructuring where and in what form inventory is held can drive powerful financial benefits. Inventory Optimisation provides a knowledge platform for better decision-making and enables organizations to use inventory as a lever for balancing supply and demand.

Amazon and other large e-retailers appeal to consumers on product offering, price and speed of delivery. They are able to effectively compete in these areas due to economies of scale that lower operating costs. To compete against the “Amazons” of the world, companies must find ways to simultaneously lower costs and improve customer service. MEIO is a modern weapon that does just that by enabling companies to selectively pick where to engage. MEIO allows companies to optimize inventory for the markets they want the battle to be fought in, for select customers, and select products.

 

About the author

Hank Canitz Picture

Henry Canitz is The Product Marketing & Business Development Director at Logility. To read more of Henry’s insights visit www.logility.com/blog.

BAEP’s Beaumont on understanding the economics of innovation

1515326886990by Julian Beaumont

For all the interest in self-driving vehicles, blockchain, 3D printing and the like, very little time is spent by investors in understanding the economics of new innovation and who might actually benefit.

Most investors in these hot industries can’t fathom anything other than a bright future. With the benefit of hindsight, however, investing in the latest hi-tech industry isn’t necessarily the easy path to riches most might presume.

Looking out from the 1920s when air travel was just taking off and the commercial airline industry was attracting much excitement, few would have been disappointed by its subsequent growth or importance to society. Investors in airlines, however, have been losing money ever since. Indeed, many airlines have gone bankrupt, including Ansett and Compass, and most have at some time required bailouts.

Consumers, however, have benefited, especially through lower flight prices over time. And to prove innovation isn’t the key to success, the supersonic Concorde stopped its super-fast flights in 2003.

Similarly, automobiles, plastics, personal computers and dot-coms were all once new-age industries that have caused carnage for investors. Picking the few winners that will emerge from the hype is often difficult.

From the dot-com bubble, Amazon is obviously one. Other big tech winners, such as Facebook, Google and Netflix, weren’t even listed at the time.

To date at least, Amazon has won with profitless prosperity, with arguably little profits to show for its success. Online retailing has been a tough place to invest.

Here, the value of the innovation accrues to customers rather than shareholders, as those in Surfstitch and Temple & Webster can attest.

Improved range, searchability, price transparency and convenience all clearly benefit the customer, but come at a cost to retailers – particularly due to increased price competition and expensive delivery costs.

Interestingly, it has been bricks-and-mortar retailers like Zara and H&M whose fast fashion and express supply chains have been among the most profitable innovations in retail in recent years.

Right now, investors are enthusiastic about lithium stocks, disruptive tech names, pre-profit concept stocks and bitcoin. Of course, that which is new and lacks much historical track record allows this optimism, with little in the way of disproof.

The key for investors is not to focus exclusively on the importance, societal value or seemingly exponential growth of the innovation, but to understand the economics behind it.

For example, if lithium is ultimately plentiful, it won’t be lithium miners that will prosper from the electric vehicle revolution. Nor will it necessarily be Tesla, as incumbent auto manufacturers can just as easily go electric.

Ultimately, whether any one company truly benefits from innovation comes down to whether they have something unique – a competitive advantage – that limits the extent to which the value of the innovation is competed away or otherwise passed on to the customer.

A common example is where the innovation makes for a unique product or service. Often forgotten as innovators are a number of world class Australian-based healthcare companies that include Cochlear, Resmed, Sirtex and CSL.

They spend big on researching and developing new and better medicines and medical devices.

Their products are protected by intellectual property rights such as product registrations and patents, allowing them to reap the profits of their innovation. Interestingly, investors don’t seem to attribute much value to R&D spend, perhaps because it usually represents an expense and subtracts for profits.

For example, CSL’s pre-tax profits would be almost 40 percent higher but for its R&D investment, which is rarely raised by those focused on its apparently lofty earnings multiple.

Other examples on the ASX include Aristocrat, which is spending more than $300 million annually on developing new market-leading slot machines and online social games; Reliance Worldwide with its Sharkbite push-to-connect plumbing fittings that offer ease and time saving in installation, and which are taking share by disrupting the market; and Costa Group, with its intellectual property in blueberries that improves quality and all-year-round availability.

As these cases attest, seemingly boring innovation can produce exciting profits.

Another less risky way to play innovation is by understanding where it can augment a company’s competitive advantage.

For example, the stock exchange ASX Limited is soon to replace its CHESS settlement system with blockchain technology that is expected to reduce costs and provide added functionality.

Another good example is Domino’s Pizza Enterprises, which operates a franchise of pizza stores. The company has very profitably leveraged new innovation to improve the efficiency of its operations and the cost, convenience and appeal of its customer offer.

For example, new ovens cook pizzas in less than four minutes, its GPS tracker helps speed up deliveries and grows the appeal of using its online ordering app, and DRU (Domino’s Robotics Unit) delivery robots save on costs and are fun for customers.

Of course, it is hard to get ahead using innovation that is readily available – all supermarkets now get the labour savings of self-service checkouts, for example – but Domino’s has been ahead of the curve in integrating new technologies into its customer proposition and thereby advancing its competitive advantages.

There are two takeaways. Firstly, to profitably invest in innovation often means looking beyond the latest sexy sector, including to second derivative beneficiaries. And two, looked at this way, the Australian market is full of innovative companies that are worthy of investment. After all, miners like Rio Tinto have already started using driverless trucks and trains, well ahead of Silicon Valley.

Julian Beaumont is the investment director at Bennelong Australian Equity Partners.

Source: Australian Financial Review


Read more:
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Machine Learning in Supply Chain Planning

Machine Learning in Supply Chain Planning - square

Machine Learning in Supply Chain Planning

Are we ready? And if so what’s involved?

The evolution to using artificial intelligence and machines that learn in supply chain planning is inevitable. In fact, there are early examples of the potential of AI to improve both supply chain planner efficiencies and provide better or optimized supply chain decisions. The question is, are we, as a profession, ready to embrace Machine Learning? If so, what does that mean and how do we get there?

Machine learning is a type of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. Machine learning computer programs teach themselves to grow and change when exposed to new data.

The latest Gartner Hype Cycle, published in July 2016, shows Machine Learning approaching the Peak of Inflated Expectations. Gartner predicts that mainstream adoption of Machine Learning is at least five years away, potentially ten. Still typing “Machine Learning AND Supply Chain Planning” into Google delivers more 16,000 results in less than half a second. This is a topic that supply chain planning people are thinking, talking, and writing about. In reality, the supply chain planning mindshare spent on Machine Learning is miniscule compared to that spent on reducing costs, improving customer service, and driving new revenue. Type “Cost Savings AND Supply Chain Planning” into Google and you get 2.5 million results.  One could argue that Machine Learning could contribute to meeting cost, revenue and customer service goals, but clearly there’s more focus today on basic supply chain planning capabilities like Demand Planning, Inventory Optimization, Sales & Operations Planning, and Supply Planning and Optimization.

One way to get started with Machine Learning is to look at your Demand Planning capabilities. For example, a “Best-Fit” forecasting algorithm automatically switches to the most appropriate forecasting method based on the latest demand information, ensuring you create the best forecast for every product at every stage of its life cycle. The algorithm evaluates forecast error each forecasting cycle and recommends or automatically selects the forecasting method that will produce the best forecast. “Best-Fit” forecasting is a basic form of Machine Learning.

Another example of today’s Machine Learning capabilities is found in software solutions that use algorithms to continually analyze the state of your supply chain and recommend or automatically execute plans to meet customer requirements. Optimization driven by algorithmic planning is an early form of machine learning that relies on a set of provided information (supply chain facilities and capacities, transportation lanes and capacities, customer service requirements, profit requirements, etc.) to automatically make optimal decisions.

One powerful example is the use of Multi-Echelon Inventory Optimization (MEIO) to automatically adjust inventory positions. The current inventory-to-sales ratio in the United States sits at 1.41, higher than any time since 2009 (right after the 2008 downturn). One cause of this glut of inventory is the emergence of omni-channel retailing. The “Amazon effect” of free and fast shipping, easy returns, and everyday low prices has changed customer expectations and the way products get into the hands of the consumer. It’s a struggle to keep up with the mind-bending rate of change that prevents companies from having the right inventory positioned at the right location to service customers. Companies that respond by increasing buffer inventory based on outdated information fail to attack the underlying issue of stock-outs, a fundamental shift in fulfillment. MEIO automatically seeks the optimal balance of inventory at the right locations, and provides optimal inventory parameters and positions by stocking location to establish optimal buffer locations and quantities. Embracing MEIO can reduce total inventories by upwards of 30% while maintaining or improving customer fill rates. MEIO is an example of basic Machine Learning that is available today.

Attaining the full benefits of Machine Learning will be an evolutionary process. We must learn to crawl, then walk, then run. The introduction of Machine Learning into most supply chain organizations will take years, but that shouldn’t stop supply chain professionals from planning for the future or taking advantage of some of the Machine Learning solutions available today. Implementing algorithmic planning and optimization technologies today builds the kind of expertise and experience that will ease the adoption of advanced Machine Learning solutions in the future.

Are you considering Machine Learning solutions?  If so, what steps are you taking to get there?

 

About the author 

Hank Canitz PictureHenry Canitz is The Product Marketing & Business Development Director at Logility. To read more of Henry’s insights visit www.logility.com/blog.

Removing the Two Barriers to Optimising Inventory

Removing the Two Barriers to Optimizing Inventory

Removing the Two Barriers to Optimising Inventory
Caught Between a Rock and a Hard Place

Henry Canitz – Product Marketing & Business Development Director, Logility

Supply chain leaders often find themselves in a difficult situation when it comes to the conflicting goals of improving customer service and minimising inventory. The omni-channel world we live in has driven customer service to new heights. Companies that don’t prioritise providing what the customer wants when they want it will soon find themselves losing market-share. On the other hand, product lifecycles continue to accelerate and the penalty for carrying too much of the wrong items leads to high levels of obsolescence. This isn’t a new dilemma, the balancing act between inventory and service has been going on since the earliest days of commerce. However, the penalties for bad service and/or high inventory are growing more severe and the space “between a rock and a hard place” is continuing to shrink.

Because of variability in demand and supply, increasing customer service levels can lead to higher levels of safety stock. Improving cash flow by indiscriminately reducing working capital dollars can result in slashing the wrong inventory, resulting in lower customer service levels.

While some supply chain teams have conducted inventory optimisation (IO) initiatives to raise service levels while lowering inventory cost, others worry that they won’t be successful in the effort.

There are two common barriers that can prevent an organisation from reaping the benefits of inventory optimisation efforts:

  • IO success can be undermined by reliance on:
    • Limited tools (such as modules built into, or bolted onto, existing ERP systems)
    • Inadequate solutions (e.g. error-prone, hard-to maintain spreadsheets)
    • Black-box systems (where calculations are difficult or impossible to validate)
  • An internal perception that understanding and implementing proven mathematical tools and business processes in order to streamline the creation of optimal inventory policies and targets is too difficult for the team to take on.

Overcoming these barriers is easier than you think and the benefits are too good to ignore. Companies that embrace Multi-echelon Inventory Optimisation (MEIO) achieve, on average, a 28% increase in inventory turns.

A simple three-step approach can remove barriers to achieving a successful MEIO initiative.

  1. Understand your current state and lay the foundation for a solid business case. Assess your organisation’s capabilities from the perspectives of:
    • Inventory performance
    • Business process and inventory management expertise
    • Technology and organisational readiness.
  2. Create a future state inventory optimisation capability—process, technology, organisation—that provides your supply chain team with a roadmap to success.
  3. Continue to drive fundamental strategic changes that create greater resiliency and agility throughout the supply chain and establish a cycle of continuous improvement.

Can you overcome the two common barriers to implementing inventory optimisation capabilities and get out of being Between a Rock and a Hard Place? Of course. We work with companies around the world who are driving significant value from their MEIO process.

Learn More:

 

About the author

Hank Canitz Picture

Henry Canitz is The Product Marketing & Business Development Director at Logility. To read more of Henry’s insights visit www.logility.com/blog.

ASCI2018 Advisory Panel

Our journey continues on the path to ASCI2018. Our main announcement has been made and the implementation is well under way. Our next step is to announce our advisory panel, and here it is.

·       Pieter Nagel, CEO, ASCI – Dr Nagel has spent his whole working career of more than 30-years, in the Supply Chain domain. He has achieved a dynamic balance between corporate, consulting and academic positions and has always endeavoured to advance the logistics profession. He developed an international reputation as a leader in Supply Chain Strategy.

·       Penny Bell, ASCI Director and Supply Chain Director, Medical Devices, ANZ, Johnson and Johnson – Penny Bell is a highly effective strategic supply chain executive, with well-developed general management competencies who focuses organisations on their strategic direction, challenges the status quo through continuous improvement initiatives, guides transformational change programs and identifies and develops high performing talent.

·       Henry Brunekreef, ASCI Director and Director Advisory Services, Supply Chain and Operations Management, KPMG – Henry Brunekreef is a Senior Manager with nearly 20 years of industry and consultancy expertise in leading organisations to operations excellence, with extensive domestic and international experience in all aspects of Supply Chain, Customer Service, Logistics and Project / Change Management. First-class strategic thinking, networking and interpersonal skills allow him to create high performing teams and drive necessary change. Henry is result driven whilst constantly focusing on customer requirements.

·      Laynie Kelly, ASCI Director and Marketing Manager ASIA Pacific, IPTOR – Laynie is an accomplished marketing and communications executive and advisor with more than 20 years corporate development experience in the technology, food & beverage, automotive and media sectors, managing sales and creative project teams. Laynie specialises in applying her expertise and market knowledge to consistently exceed the marketing performance of her clients.

Our advisory panel will be able to provide the strategic advice and relevant industry knowledge to take ASCI2018 to the next level. The panel includes an array of experienced professionals from across the supply chain, as you can see above.

With such a strong advisory panel, ASCI2018 is sure to be a unique opportunity. Each panellist comes from varying sectors within the industry, meaning your organisation will be able to engage everyone, from logistics to procurement and overall, your entire organisation can benefit from the latest industry advances.

You are also invited to take part in our survey and let us know what you want to see and hear at the conference – HERE

 

Regards,

Pieter Nagel
CEO
Australasian Supply Chain Institute