Posts in Supply Chain Trends

Unearthing Opportunity in the Linear Approach Toward Clothing

Executive Summary

This analysis examines the interconnectivity between human behavior, industrial elements, and natural elements across the linear processes in the textile and clothing system. Using a systems-level perspective, it shows how biological and technical resources are wasted in material extraction and harvesting, the production of goods, and consumption or use by humans. The analysis of waste focuses on the material flows of non-energy related materials.  The impact to natural systems and energy-related materials is not emphasized.

System Introduction

The linear economy adopts an approach of take, make, use, dispose without considerations to comprised systems, each containing an interconnected set of elements organized around some purpose (De Vries, 2013). A broad understanding of system behavior explains the dependencies, influences, and potential consequences of each decision as it relates to the whole (Holling, 2001). Though the textile and clothing system is primarily concerned with the design, production and distribution of yarn, cloth and clothing, the intricacies and system dynamics specific to the fashion industry are far from basic (Amed, Berg, Brantberg, & Hedrich, 2016). The fashion industry fuels a linear economy with waste greater than $460B of value each year through unsustainable disposal of clothing (Ellen MacArthur Foundation, 2017). This analysis will explore the system described as textile to garment through depiction of linear process, flow of materials, and types of waste by each actor across the supply chain (Franco, 2017).

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Human Perspective

With a population projected to reach 1.2B by 2025, consumerism has played an integral role in the evolution of industrialization, mass production, and values that marry progress and prosperity with compulsive acquisition (Goodwin, Nelson, Ackerman, & Weisskopf, 2008). Consumption signals a supply chain, and it responds through the acquisition of materials, manufacturing, and distribution of goods, creating inefficient use of natural resources, pollutants, and waste (Shah, 2014).

Abraham Maslow, renowned professor of psychology and human behavior, classifies all human efforts as an attempt to fulfill one of five needs. Humans require clothing to fill basic physiological and safety needs. However, the dynamics and influences that connect the linear economy and textile and clothing system are complex because economic performance is tied to human behavior, assessed by the growth and level of real output per person (McAlphin, 2014). Reciprocally, employment is a vital means to realize the income necessary for consumption, both individually and in aggregate terms (McAlphin, 2014). Over twenty-seven million people worldwide are employed in the textile and clothing industry (Amed, Berg, Brantberg, & Hedrich, 2016). Moreover, the system represents seven per cent of the world’s exports in terms of sales: a combined two thirds in North America and Western Europe, and one quarter in China (Cardoso, 2013).

Linear Process

The linear economy embodies a linear process because materials flow in one direction, with sub-optimal utilization of materials and energy, and a shortened life cycle (Varney-Wong, 2016). The textile and clothing system is a linear process as depicted in Figure 1, in which 53 metric tons of fibre are extracted annually to produce clothes that are often used for a short time, after which 73% of the materials are mostly sent to landfill or incinerated (Ellen MacArthur Foundation, 2017). Actors in the textile and clothing system supply chain include: supplier in the extraction and harvesting of raw materials, manufacturer in the production of goods, distribution and transportation as a conduit to the customer, and lastly the consumer who uses and disposes the goods, sustainably or unsustainably (De Vries, 2013). Although design and product commercialization are inputs to the linear process, it is not in the scope of this analysis.

In the United States, clothes are worn one quarter of the global average (Ellen MacArthur Foundation, 2017). The global average life cycle of a garment is two years (WRAP, 2017). The system is characterized by a complex supply chain and is considered to be one of the most polluting industries in the world, with more than 1.2B metric tons a year in greenhouse gas emitted during production alone which is greater than the emissions of end-to-end logistics (Ellen MacArthur Foundation, 2017).


Figure 1: Linear Process Model in Textile and Clothing System (Varney-Wong, 2016)

Biological and Technical Resources

The first actor, the supplier, is responsible for raw materials that are categorized as natural and manmade fibres as shown in Figure 2. Raw materials are inputs to processing and manufacturing using the biological resources sun, water, energy, and land to produce non-toxic materials that can biodegrade and safely feed environmental processes. Key biological resources are cotton and wool where yields are more than 26 mega tonnes and 2 mega tonnes respectively (Cardoso, 2013). Manmade or technical resources are not suitable for the biosphere and cover synthetic materials such as nylon, a polyester derived from petrochemicals, or regenerative cellulose materials that are manufactured from wood fibres (Cardoso, 2013). Technical resources include design, equipment and machinery, capital, data, and labor as inputs to create manmade fibers (Maia, Alves, & Leao, 2013). Blends are also used to give the fabric desirable qualities like breathability,


Figure 2: Raw Materials, Biological, and Technical Resources (Cardoso, 2013)

The volume of the world’s fiber production for the textile and clothing system in 2012 was approximately 88.5 mega tonne, from which 56 mega tonne were manmade, of which 40% was polyester, and 32.5 mega tonne natural fibre, of which 80% was cotton (Cardoso, 2013). The use of manmade fibre represents 64% of overall production, primarily by low cost clothing or fast fashion, and propensity to consume in developed countries (Cardoso, 2013). It is predicted that by 2030, the fashion industry will use 115 million hectares, a 35% increase in land for cotton, forest for cellulosic fibers, and grassland for livestock (Snoek, 2017).

The second actor comprises production and manufacturing, that includes processing to fibre raw materials, spinning, weaving, knitting, coloration, finishing (cutting, sewing, quality, pressing), packaging materials, and staging delivery to the consumer. This step uses technical resources of machinery, labor, capital, technology, and data with biological resources like water, energy, and land, yielding a thirty-six percent loss of material waste as a percent of total production as depicted in Figure 3 (Varney-Wong, 2016). Across the industry, only 13% of the total material input is in some way recycled after clothing use (Ellen MacArthur Foundation, 2017).


Figure 3: Material Waste as a Percent of Total Production (Varney-Wong, 2016)

The third actor, the consumer, is responsible for use and disposal. Use is defined as washing, drying, ironing, and utilizes biological resources of raw materials, water, and energy. The disposal of clothing has the largest impact on the natural environment via landfill or incineration (Rosa, 2016). While natural fibers like cotton and wool produce methane gas, polyester, the most common non-biodegradable synthetic fiber used in the clothing, can remain in landfill soil for several decades (Rosa, 2016).


There are eight types of wastes common to Lean Six Sigma methodologies, a widely adopted best practice in efficient production of goods (Maia, Alves, & Leao, 2013). Across a linear process, T.I.M.W.O.O.D.U. characterizes wastes in transportation, inventory, motion, waiting, over production, over processing, defects and underutilizing knowledge management and people (iSixSigma, n.d.). Additional wastes include untapped human potential, poor design, inappropriate systems, and wasted natural resources (Maia, Alves, & Leao, 2013). All of these wastes are present in the linear process of the textile and clothing system.

The textile and clothing industry relies predominantly on 98 million tonnes per year in non-renewable resources; this includes oil in synthetic fibres production, fertilizers to grow cotton, and chemicals to produce, dye, and finish fibers and textiles (Ellen MacArthur Foundation, 2017). During the growing and extraction of raw materials, 93 billion cubic metres of water annually are consumed, contributing to water scarcity  (Ellen MacArthur Foundation, 2017). During the ‘take’ process step, other wastes include: transportation (energy in picking, extraction to production), inventory (use of land, pesticides, water, energy), motion (inefficient utilization of human labor), waiting (watering takt time, equipment, and human labor), over processing (excessive pesticides, preservatives), defects (waste water pollution, air emissions, poor demand forecast biased by mass-consumption, data quality, poor design), and underutilization (poor bio-design and poor use available technology to aid in harvesting and extraction) (Maia, Alves, & Leao, 2013) (Cardoso, 2013).

Textile and clothing manufacturing demands huge amounts of energy and water, particularly in wet processing. Wastewater must be properly treated from discharge to remove hazardous chemicals, including mutagens, carcinogens, and teratogens, that cause serious environmental damage including exhaust gases, waste water, and the fabrics (Franco, 2017). Also, synthetic fibers, mainly out of petrochemical base, lead to resource use and GHG emissions from processing fossil fuels (Cardoso, 2013). Poor labor standards and conditions pervade global textiles supply chains including ethical issues of child labor, poor safety infrastructure, forced labor, in addition to low wages and extended work days (Franco, 2017).

During the ‘make’ process step, other wastes include: transportation (energy moving products and information, distribution to consumer), inventory (use of land, chemicals, water, energy, fibre, parts, safety stock, working capital, packaging), waiting (between processes, and for human labor execution), over production (water, energy, noise pollution, dust emissions), over processing (water, energy, excessive chemicals, preservatives), defects (waste water pollution, air emissions, poor demand for energy and water, scrap, data quality), and underutilization (poor leadership making unsustainable decisions, unethical treatment of trading partners, poor bio-design, poor use available technology to aid in smart manufacturing) (Kupsala, 2013).

The consumer may send a waste-ridden demand signal that feeds the linear process during use and disposal. Underutilization of information pertaining to sustainable purchasing, sustainable disposal methods, and total costs of ownership in proper care for clothing, can lead to waste in water, energy, and use of chemicals in washing. Less than 1% of material used to produce clothing is recycled into new clothing, denoting a loss of more than $100B worth of materials each year (Ellen MacArthur Foundation, 2017). High costs are associated with disposal and land filling clothing and household textiles; in the UK the annual cost is approximately $108M (Ellen MacArthur Foundation, 2017). Other wastes include: transportation (energy moving products for early disposal and in landfill), inventory (use of land, water, energy, fibre, working capital tied to having too many garments), and defects (waste water pollution, air emissions, synthetic fibre impact in landfill)(Franco, 2017).



The large and growing global population has a predisposition to mass-consume with systems in place to inefficiently manufacture clothing, placing an extreme strain on materials, resources, and natural systems (Funkhouser, 2012). Emphasis is needed to highlight the extent that supply chains can contribute to global sustainability improvements and waste reduction in textile and clothing systems. As pressure for natural resources and price volatility intensifies, system focus must shift to recovery of materials and blends, water and energy usage, reducing hazardous chemical use, and ensuring ethical human rights (Franco, 2017). Ultimately, industry must leverage more efficient and sustainable practices through material lifecycle management, including shrinking or decreasing use, slowing, or closing material loops (Ellen MacArthur Foundation, 2015).



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Cardoso, A. (2013). Life Cycle Assessment of Two Textile Products: Wool and Cotton. Universidade Do Porto, Environmental Engineering.

Chan, T., & Wong, C. (2012). The Consumption Side of Sustainable Fashion Supply Chain: Understanding Fashion Consumer Eco‐fashion Consumption Decision. Journal of Fashion Marketing and Management, 16(12), 193-212.

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Franco, M. (2017). Circular Economy at the Micro Level: Dynamic View of Incumbents’ Struggles and Challenges in the Textile Industry. Journal of Cleaner Production, 168, 833-845.

Funkhouser, D. (2012, April). Population, Consumption, and the Future State of the Planet. Retrieved February 15, 2018, from

Goodwin, N., Nelson, J., Ackerman, F., & Weisskopf, T. (2008, January). Consumption and the Consumer Society. Retrieved February 19, 2018, from

Gracey, F., & Moon, D. (2012, October 7). Valuing Our Clothes: The Evidence Base. Retrieved February 19, 2018, from

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Holling, C. (2001, August). Understanding the Complexity of Economic, Ecological, and Social Systems. EcoSystems, 4(5), 390-405. doi:10.1007/s10021-001-0101-5

iSixSigma. (n.d.). 8 Wastes of Lean. Retrieved February 20, 2018, from

Kupsala, H. (2013). Eco-Effective Fashion Theory: How to Implement Cradle-to-Cradle Concept Into Fashion and Clothing Design. University of Lapland.

Liu, J., Dietz, T., Carpenter, S., Alberti, M., Folke, C., & Moran, E. (2007, September 14). Complexity of Coupled Human and Natural Systems. doi:10.1126/science.1144004

Maia, L., Alves, A., & Leao, C. (2013). Sustainable Work Environment with Lean Production in Textile and Clothing Industry. International Journal of Industrial Engineering and Management , 4(3), 183-190.

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McAlphin, D. (2014, September 10). U.S. Population and Its Impact on the Environment: Why Curbing Per Capita Consumption Is Not Enough. Retrieved February 15, 2018, from Progressives for Immigration Reform:

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Rosa, A. (2016). Circular Economy in the Clothing Industry: Challenges and Strategies. KTH Industrial Engineering and Management.

Rydberg, A. (2016). Circular Economy Business Models in the Clothing Industry. Uppsala University, Department of Earth Sciences.

Sandvik, I. (2017). Applying Circular Economy to the Fashion Industry in Scandinavia Through Textile-to-Textile Recycling. Monash University, School of Social Science.

Shah, A. (2014, January). Consumption and Consumerism. Retrieved October 10, 2017, from

Shankar, A., & Pavitt, C. (2002, July). Resource and Public Goods Dilemmas: A New Issue for Communication Research. The Review of Communication, 251-272.

Snoek, S. (2017). Circular Economy in the Textile Industry. Sweden: Environmental Policy Group.

Strahle, J., & Muller, V. (2017, October 30). Key Aspects of Sustainability in Fashion Retail. Retrieved from Springer Link:

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WRAP. (2017, July). Valuing Our Clothes:The Cost of UK Fashion. Retrieved February 19, 2018, from

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Moving Towards a Circular Economy

When you think about accelerating impacts and long-term solutions to current supply chain challenges that impact the 3P’s (people, planet and profit), we need to adopt and develop sustainable frameworks with a holistic life-cycle perspective. There is a ton of innovation happening in the CPG space (Levi’s, Unilever, PepsiCo, etc.)

Shifting from the current ‘take-make-waste’ linear model to the circular economy is critical for businesses to continue to thrive and meet society’s needs. Waste volumes are projected to increase from 1.3 to 2.2 billion tons by 2025, and with nearly 9 billion consumers on the planet including 3 billion new middle class consumers by 2030. The challenges of addressing waste and meeting increasing demand are unprecedented. Therefore it is imperative businesses continue to re-evaluate raw materials, design, manufacturing, consumption, and end of life to keep materials and products continuously flowing through closed loop systems.

How is your company innovating in product life cycle management from design and inception to sustainable product packaging? How are you personally adopting a sustainable mindset in your home, the daily choices you make as a consumer to move toward a circular economy? The bigger question is how are YOU INFLUENCING this change?

Blockchain: Revolutionizing the Global Supply Chain by Building Trust and Transparency


The history of Supply Chain Management has evolved since its’ roots in the early 1900s. From improving labor processes of basic material handling and freight transportation, to more sophisticated approaches of balancing cost and efficiency trade-offs, the concept of a supply chain is no longer siloed. It requires integration of supplier-customer relationships, process synchronization, and data harmonization in a complex, dynamic network that is susceptible to vulnerabilities in a global environment. Critical processes to this relationship include real-time communication, collaboration, trust, and transparency that yield mutually beneficial outcomes and competitive advantage. In today’s world, there is a growing prevalence in leading firms advancing toward the adoption, development and implementation of Blockchain technology as a backbone of business operations. This case dives a bit deeper into Blockchain, a novel technology with the strong potential to revolutionize the Global Supply Chain. The goal of this analysis is to discuss: 1) the key technical and economic aspects of Blockchain, 2) the current Blockchain innovators, barriers, and obstacles to Marketplace acceptance, 3) the business case for Blockchain, and 4) future applications and implications of Blockchain technology.

Click here to read the research: Blockchain_Revolutionizing the Global Supply Chain by Building Trust and Transparency

Going Green or Greenwashing?


Sustainable, much like organic, is used loosely as a marketing ploy. More often than not, countless companies use a concept called “green-washing.”Green washing is when a company, government or other group promotes green-based environmental initiatives or images but actually operates in a way that is damaging to the environment or in an opposite manner to the goal of the announced initiatives. This can also include misleading customers about the environmental benefits of a product through misleading advertising and unsubstantiated claims.

I use this greenwashing index There are more robust reporting initiatives, but this site is simple and give tips to the basic consumer on how to spot greenwashing and outlines the methodology behind the index. I highly recommend EcoVadis for larger organizations looking to integrate a desktop, cloud-based sustainable compliance solution. EcoVadis operates the first collaborative platform providing Supplier Sustainability Ratings for global supply chains. With a focus on maintaining quality and integrity, EcoVadis has managed to also grown quickly to meet this increasing need. Since its founding in 2007, EcoVadis has become a trusted partner for procurement organizations in more than 150 leading multinationals worldwide including Verizon, Nestlé, Johnson & Johnson, Heineken, Coca-Cola Enterprises, Nokia, L’Oréal, Bayer, Alcatel-Lucent, ING Bank, Air France-KLM, Centrica/British Gas, BASF, and Merck. Combining People, Process and Platform, EcoVadis has developed the industry-leading team, innovative technology, and a unique CSR assessment methodology that covers 150 purchasing categories, 110 countries, and 21 CSR indicators. More than 30,000 companies use EcoVadis to reduce risk, drive innovation and foster transparency and trust between trading partners. EcoVadis is driven by a diverse team of over 300 talented professionals from 40 nationalities committed to a real impact on the environmental and social practices of companies around the world.

Operations or Supply Chain Excellence?

I’m sharing great insights from Innovation Enterprise via Micha Veen who explains simple tips to master supply chain excellence pinned Operational Innovation.

Source: A Refreshing Innovative Approach To Supply Chain Excellence | Articles | Chief Supply Chain Officer | Innovation Enterprise 

Operational or Supply Chain Excellence has been one of the buzzwords that is often heard around senior Supply Chain Execs. However, is excellence the right terminology, or do we need to rename ‘excellence’? Due to globalization, continuous creation of new small ‘global’ businesses that can compete with established organizations, leading supply chain organizations have started to look beyond ‘operational excellence’, best-in-class, benchmark data and industry metrics, towards using a combination of their own internal and tailored external relevant data to continuously review, assess, and adopt evolving leading-edge processes, technologies and behaviors to stay ahead in this ever increasing competitive business landscape. This new approach, named Operational Innovation, has become an effective methodology to deliver transformational impact through the following elements…

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Innovative solution design

Instead of spending a lot of time and effort in designing the optimal operational and supply chain solutions, successful organizations focus on creating a solid solution foundation, which is constantly reviewed and improved with cross functional teams to deliver cross-divisional, fit-for-purpose solutions.

Close collaboration

Instead of phased hand-offs between subject matter experts, technology specialists, operational teams, sales, finance, etc., leading innovative supply chain solutions should be created through continuous close collaboration with all impacted process participants at every stage of the supply chain journey.

Use of Robotics and Blockchain Technologies

A recent article (How will manufacturing robotics change in 2017) describes how robotics will change the industry as early as 2017. The article describes how by 2019, 35% of leading companies in logistics, health, utilities, and resources will start implementing robotics to automate their operations. Additionally, supply chain blockchain technology has started to be utilized in supply chain organizations to deliver additional benefits. A recent article describes clearly the impact that Blockchain has on Supply Chain.

End-to-end Solution integration

The key to delivering true Supply Chain Innovation is the manner in which organizations integrate end-to-end processes, technologies, data, and internal vs. external organizational units. Due to the external focus on innovative technologies, many organizations are still only focused on technology integration, but leading businesses have started to explore how different cloud solutions can be integrated across their partners and customers, creating hybrid learning organizational models which go beyond the traditional joint venture organization models.

Continuously generate value

In supply chains it’s crucial to continuously generate value. Through the use of innovative technologies, solution partnerships, operational models, etc. leading supply chain organizations are known to continuously review, adapt and improve their supply chain environment to deliver operational innovation. It allows supply chains to continuously deliver ‘new and improved’ excellence.

In today’s world, Supply Chain Excellence is not enough. There is no ‘end-station’. It’s critical for supply chain organizations to adopt an ongoing innovation journey, which requires people with the right mindset, experience levels, attitude and curiosity to deliver supply chain innovation….


Blockchain: The Best Way to Decentralize Supply Chains


This is a recent article written by a brilliant colleague Harry Goodnight. Great insights and perspectives on blockchain technology and why decentralization is a good thing for modern supply chains.


In business and economics, decentralization often refers to the ability to participate in a market and exchange value between peers without the interference of a third-party intermediary who most likely controls and restricts barriers of entry. As Ethereum co-founder Vitalik Buterin explains in his blog post “The Meaning of Decentralization,” blockchain is politically and architecturally decentralized, meaning no entity one controls it and there’s no central point of failure in its infrastructure. In this way, a decentralized supply chain would allow for a frictionless vehicle of business-to-business value exchange amongst even the smallest players in the industry.

Decentralization is defined as the transfer of power away from a central location or authority. As a concept, it is not new; as a business model, however, it is a powerful idea. Some sociologists claim that decentralization and centralization theories have actually been occurring in cycles for the last 4,000 years, causing the rise and subsequent fall of ruling states and empires. Throughout history, the core theory behind decentralization has remained the same: dispersing power from authorities and empowering smaller, individual entities with the ability to act in their own self-interest.

Why decentralization is necessary for modern supply chains

This is especially necessary in the supply chain industry, which has historically suffered from a number of issues that hinder its efficiency. Its main roadblock is that current supply chains are unable to become agile, which poses a significant problem in a market in which they must be able to change their configurations quickly and continually to meet the constantly-changing dynamics of supply and demand. Another major disadvantage is that methods of communication tend to vary greatly, with some companies still relying on manual paperwork. As a result, data storage becomes locked away in in proprietary systems that don’t allow for collaboration.

Supply chain companies also tend to face cultural and organizational issues, such as executing operating plans due to corporate goals, board restrictions and the competitive nature of the market. Consequently, companies have revoked social contracts, mistreated skilled laborers and underutilized their professional talent assets.

This mismanagement has serious financial consequences: for instance, $4.2 trillion is locked up in net working capital in today’s supply chains. By allowing today’s virtual supply chains to break from the company-centric, server-based environments in which they currently find themselves, they will become less brittle, more scalable and fully leverage the underutilized skills and assets available in modern-day business networks. Even a 1% improvement in Invoice-to-Cash cycle times would immediately return about $42 billion in cash to operations.

How can blockchain remedy the issues of centralization in supply chains?

When looking at its positive implications, blockchain is the most logical next step for supply chain managers and logistics providers. Blockchain was brought to the mainstream through cryptocurrencies like bitcoin and Ethereum. It creates an unchangeable digital ledger that provides a record of financial transactions in chronological order. This technology has been increasingly adapted to address gaping deficiencies in other fields, from education to voting to real estate. Through blockchain, massive networks of decentralized autonomous individuals and organizations can grow and operate seamlessly within a decentralized, distributed operating platform.

Blockchain also provides an efficient and viable solutions to the aforementioned hurdles that are restricting today’s supply chain. Specifically, it offers opportunities to synchronize processes that occur within supply networks, resulting in reduced Cost-of-Goods-Sold (COGS) and more cash freed from working capital.

The solution to many of these recurring issues in supply chain primarily involves people. By creating networks of skilled individuals and decentralized autonomous organizations, immense value can be brought to companies, supply chains, and customers. These networks align economic incentives so that everyone prospers, based on their contributions of time, skill, and intellectual property. These contributions are monitored and administered through outcome-based smart contracts on the blockchain. This new vision of decentralization has the potential to radically transform the supply chain space.

8 Fundamentals for Achieving AI Success in the Supply Chain

Sharing point of view from  Greg Brady is the CEO and founder of One Network Enterprises, a global provider of a secure and scalable multi-party business network. For more information, contact the author at or visit Enjoy!

Source: 8 Fundamentals for Achieving AI Success in the Supply Chain – Supply Chain Management Review

There’s a lot of buzz and hype about artificial intelligence (AI) in supply chain management (SCM). That’s understandable given its potential. AI can offer a huge benefit to supply chain managers, but only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains. More importantly, it needs to consider the availability of the timely and accurate data needed to make smart decisions.

Before addressing what AI can do, it is critical to first understand what it is. In the simplest terms, AI is intelligence exhibited by machines, or when machines mimic or can replace intelligent human behavior, such as problem solving or learning. In essence, AI is machines making decisions whether that is deciding which chess piece to move where, or how to adjust an order forecast based on changing demand.

Despite its benefits, when looked at through the lens of a supply chain executive, AI is relatively useless unless it’s able to add value to support better decision-making.

Why AI Hasn’t Delivered in SCM

In the race to use AI, many companies have made attempts to implement it, but the results have been disappointing. This is because typical SCM systems today:

  • Require armies of expensive planners
  • Run complex engines at each step in the process and at each node in the supply network
  • Are usually in conflict with other functions and/or partners
  • Miss huge opportunities hidden in the network because they are locally sub optimized
  • Work on stale data and thus promote bad decisions
  • Use dumbed-down, over-simplified problem models that do not relate to the real world

These SCM limitations have severely suppressed return on AI investments. For example, typical Retail/CPG supply chains still carry 60-75 days of inventory. The average service level in the store is about 96 percent, with promoted item service levels much lower at the 80 percent range. The Casual Dining segment on the other hand, carries around 12 – 15 days of inventory with relatively high waste and high cost-of-goods-sold. So, unless AI can make a significant impact on these metrics, it’s simply not delivering.

Key Requirements for AI in Supply Chain Management

Having worked with hundreds of supply chain executives, on dozens of software implementations, I’ve studied the AI issue a lot. What I have found is there are eight criteria that are required for a successful AI implementation. Miss one of these and you’ll be lucky to achieve mediocre outcomes, but when you meet them all, you can indeed achieve world class results. For the AI solution to offer optimal value in supply chain, it important to ensure the following:

1. Access to Real-Time Data 
To improve on traditional enterprise systems with older batch planning systems, new AI systems must eliminate the stale data problem. Most supply chains today attempt to execute plans using data that is days old, but this results in poor decision-making that sub-optimizes the supply chain, or requires manual user intervention to address. Without real-time information, an AI tool is just making bad decisions faster.

2. Access to Community (Multi-Party) Data 
The ability to access data outside of the enterprise or, more importantly, receive permission to see the data that is relevant to your trading community, must be made available to any type of AI, Deep Learning or Machine Learning algorithms.

Unless the AI tool can see the forward-most demand and downstream supply, and all relevant constraints and capacities in the supply chain, the results will be no better than that of a traditional planning system. Unfortunately, this lack of visibility and access to real-time, community data is the norm in over 99 percent of all supply chains. Needless to say, this must change for an AI tool to be successful.

3. Support for Network-Wide Objective Functions
The objective function, or primary goal, of the AI engine must be consumer service level at lowest possible cost. This is because the end-consumer is the only consumer of true finished goods products. If we ignore this fact, trading partners will not get the full value that comes from optimizing service levels and cost to serve, which is obviously important as increased consumer sell-through drives value for everyone.
A further enrichment of the decision algorithm should support enterprise level cross-customer allocation to address product scarcity issues and individual enterprise business policies. Thus, AI solutions must support global consumer-driven objectives even when faced with constraints within the supply chain.

4. Decision Process Must Be Incremental and Consider the Cost of Change
Re-planning and changing execution plans across a networked community in real time can create nervousness in the community. Constant change without weighing the cost of the change creates more costs than savings and reduces the ability to effectively execute. An AI tool must consider trade-offs in terms of cost of change against incremental benefits when making decisions.

5. Decision Process Must Be Continuous, Self-Learning and Self-Monitoring
Data in a multi-party, real-time network is always changing. Variability and latency is a recurring problem, and execution efficiency varies constantly. The AI system must be looking at the problem continuously, not just periodically, and should learn as it goes on how to best set its own policies to fine tune its abilities. Part of the learning process is to measure the effectiveness “analytics,” then apply what it has learned.

6. AI Engines Must Be Autonomous Decision-Making Engines
Significant value can only be achieved if the algorithm can not only make intelligent decisions but can also execute them. Furthermore, they need to execute not just within the enterprise but where appropriate, across trading partners. This requires your AI system and the underlying execution system to support multi-party execution workflows.

7. AI Engines Must Be Highly Scalable
For the supply chain to be optimized across an entire networked community of consumers to suppliers, the system must be able to process huge volumes of data very quickly. Large community supply chains can have millions if not hundreds of millions of stocking locations. AI solutions must be able to make smart decisions, fast, and on a massive scale.

8. Must Have a Way for Users to Engage with the System
AI should not operate in a “black box.” The UI must give users visibility to decision criteria, propagation impact, and enable them to understand issues that the AI system cannot solve. The users, regardless of type, must to be able to monitor and provide additional input to override AI decisions when necessary. However, the AI system must drive the system itself and only engage the user on an exception basis, or allow the user to add new information the AI may not know at the request of the user.

AI in the Real World Today

Sounds good in theory, but how does it work out in practice? Now that we have addressed the key fundamentals, let’s look at how some actual companies have achieved applying these criteria.

For instance, one of the major problems in Casual Dining is anticipating and meeting demand for the restaurants, corporate owned or franchised. This is especially important during Limited Time Offers (LTOs). Using the eight criteria outlined above, a global, casual dining company connected to a real-time, multi-party network, and was able to rapidly achieve their objective function – excellent customer service at the lowest cost.

The company constantly monitors Point-of-Sale (POS) data, and is using AI agents to recognize and predict consumption patterns of consumers. In addition, intelligent AI agents create the demand forecast and then compare it to the actual demand in real-time. When there is significant deviation, the agents make the decision to adjust the forecast, and additional agents adjust replenishments. They then propagate those adjustments across the supply chain to trading partners in real time at all times considering the cost of change and the propagation impact.

This drove a remarkable improvement in forecast accuracy. During promotions, the company achieved over 85 percent forecast accuracy at the store level and even higher at the DC level. This represents at least a 25 percent improvement over traditional approaches.

Intelligent agents also optimize restaurant orders autonomously by recognizing the impact of projected restaurant traffic trends and impact on LTOs and therefore the orders. The system runs on an exception basis but allows the managers to review the decision criteria and override orders where the managers may have local information such as inventory issues or local store traffic issues. This has resulted in much faster order placement and order accuracy of over 82 percent, which reduces both inventory and waste dramatically while increasing service levels to the consumer. This is a significant improvement to all other known implementations in the marketplace.

Because the algorithms are highly scalable, they are processing over 15 million stocking locations continuously throughout the day.

Prior to the AI-based, multi-party execution system, restaurant managers had to interact with nine different ordering systems and manually create their own orders based on general guidelines, rules of thumb, and spreadsheet-based or manual calculations.

With AI implemented on a sound foundation, this company can now anticipate, manage, and serve demand at the lowest possible cost. During LTO’s, when demand fluctuations would overwhelm a restaurant manager, intelligent agents monitor demand in real time, and autonomously orchestrate the supply chain to align supply with demand. Thus, the company can meet its goal and maintain high service levels while reducing cost to serve.

These are not isolated results. Also in the food marketplace, another CPG-Retail implementation achieved 99 percent in-stock, in-store, with 25 days of supply (DOS) across the supply chain.  The inventory results are less than half the standard DOS in this marketplace and 3 percent points higher in in-store in-stocks
AI-based solutions are being deployed at two large automotive tier one suppliers with results ranging from 16 – 40 percent reductions in inventory as well as significant reductions in expedited freight costs.

AI Delivers Value in SCM Today

As you can see, laying the proper groundwork for AI pays huge dividends. There’s no doubt that AI offers even greater promise in the future, but, as these results show, there are significant benefits and dramatic results waiting for companies that focus on the fundamentals and put AI to use today.

The beauty of AI-based solutions is that they learn and drive continuous improvement over time. They get more precise and sophisticated as they gather more data and more experience. The sooner you start, the better the results you’ll see in future, and the further ahead you will be. With the right AI solution in place, you can outpace your competitors today, and be well positioned for reaping even bigger rewards of AI’s promise tomorrow. ~Greg Brady



2017 Trends in Corporate Sustainability 

I’m sharing insights from Amy Augustine. She elaborates on trends in sustainability, particularly clean-energy policy. Enjoy!

Source: 7 Trends That Will Drive Corporate Sustainability in 2017

As we confront a new political climate that is inspiring both uncertainty and rising citizen action, I am more convinced than ever that business must play a critical role in achieving a sustainable, equitable and clean-energy future. Bold leadership, as well as individual and collective action from influential companies and investors, is critical to ensure continued progress in achieving the ambitions of the historic Paris Climate Agreement and the U.N. Sustainable Development Goals.

Fortunately, companies we engage with here at Ceres continue to demonstrate that sustainability is not just good for the bottom line; it is the bottom line. Despite backward steps in Washington, there is unprecedented clarity in the business community – especially from the Fortune 100s – that building a healthy, low-carbon economy is irresistible and irreversible. Examples of this are popping up everywhere, although still not at the pace and scale we need.

These seven key corporate trends are ready for primetime and will be critically important in advancing our sustainability goals, no matter the political winds in Washington.

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1. Corporate support for clean-energy policy is accelerating

Corporate energy buyers want renewable energy – and not just to help them meet their own greenhouse gas reduction goals. Renewable energy prices are increasingly cost-competitive in many parts of the country, and they remove the long-term risks associated with fossil fuel energy price volatility.

More than 900 companies and investors are calling on President Trump and Congress to keep the U.S. in the Paris Climate Agreement and to support low-carbon policies in the U.S. And nearly 100 global companies have signed on to to the RE100 initiative, a commitment to source all of their energy from renewables.

Lacking a national carbon mitigation strategy, states and cities will continue to be the platforms on which we’ll see meaningful clean-energy progress. In Michigan, Ohio and Virginia, among other states, companies are helping to shape policies that strengthen and increase access to renewable energy, leading to more clean-energy investment and jobs in those states.

Stay tuned for our upcoming Power Forward report this spring documenting these trends among Fortune 500 companies.

2. More investors expect companies to disclose climate-related risks and opportunities

The Task Force on Climate-Related Disclosures (TCFD) – whose leadership includes Ceres member companies such as Bloomberg LP and JPMorgan Chase – recently published a specific guidance on how companies should evaluate and disclose climate risks in financial filings.

Investors and global stock exchanges are taking notice, especially in regard to how carbon-intensive companies are analyzing business impacts under scenarios where carbon pollution is reduced at levels that would limit global warming to 2-degrees Celsius or less – the goal of the Paris Climate Agreement.

More than ever, investors are aiming these questions at energy-intensive companies like ExxonMobil and Chevron, which are already struggling financially as global oil demand is waning.

3. Companies are advancing human rights reporting and performance

Companies are facing unprecedented scrutiny on their human rights performance and reporting. In 2015, the nonprofit group Shift that helps organizations to implement the U.N. Guiding Principles on Business and Human Rights (UNGPs), developed the UNGP reporting framework, which companies such as Ericsson, Nestle, and Unilever are already utilizing to strengthen human rights reporting and performance.

Ceres is now collaborating with Shift to advance corporate adoption and implementation of the framework to drive improved human rights performance across direct operations and global supply chains.

4. Water risks are rising on the investor agenda

Water crises such as prolonged droughts and extreme precipitation events – been in California, lately? – were again among the top five global impact risks in an annual report from the World Economic Forum.

Increasingly, companies operating in water-stressed regions are proactively taking action to conserve and protect water sources. General Mills, Gap and PepsiCo, are among a growing cadre of companies engaging with California policymakers on the urgency for stronger water management policies in this water-starved state.

5. Competence on sustainability is becoming a measure of board effectiveness

Corporate boards have a key authority and responsibility to boost corporate attention on long-term sustainability risks like climate change. Large investors are increasingly focused on the role board members can play on sustainability. U.S. pension funds CalPERSand CalSTRS, for example, both recently updated their governance principles to explicitly request that company boards have stronger experience and expertise on climate risk management.

In the coming months, investors and other stakeholders will be looking to engage with key governance experts within companies on this topic, including corporate secretaries and general counsel.

6. SDGs will be a bigger driver of strategy and action

In 2015, more than 190 world leaders committed to 17 Sustainable Development Goals (SDGs) aimed at ending extreme poverty, eliminating longstanding inequalities and fighting climate change.

Worldwide momentum behind these internationally supported goals continues to gain strength, and at the upcoming Ceres Conference we will hear from Novozymes, BASF and Intel about how they are aligning their commitments and business strategies with this global vision.

7. Sustainable sourcing is becoming the new norm

Access to reliable, affordable supplies of key inputs is threatened by climate change, water scarcity risks, and the use of unethical practices like deforestation and forced labor. Agricultural supply chains are feeling some of the biggest pressures, leading to stronger action by investors and companies themselves to push for strategies to assess and manage these risks.

This spring, Ceres will release an interactive tool called Engage the Chain to help investors and companies better understand wide-ranging agricultural commodity risks.

No doubt, company actions on all of these fronts will continue to evolve – and, hopefully, accelerate. Such leadership is more essential than ever.

The Amazon vs. Retail Battle


Source: The Amazon vs. retail battle: Explained 

It’s no secret that Amazon is revolutionizing the retail industry. But what does that actually mean?

Which retailer is Amazon targeting now? Amazon newest target isn’t a retail chain at all — it’s your local convenience store.The company rolled out a new service today called Amazon Instant Pickup, which lets customers order basics like chips, soda and toothpaste. You can then pick them up from an Amazon locker in just two minutes.

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Isn’t mimicry the sincerest form of flattery? Not if you’re a retailer that wants to stay in business. Just ask Dick’s Sporting Goods (DKS). Dick’s earnings report disappointed Wall Street on Tuesday. The retailer lowered its full-year profit forecast today because of “a challenging retail environment.” Its stock fell more than 20%. Sound familiar? Last month, Amazon filed a patent to launch a competing meal-kit delivery service. Blue Apron’s shares plunged 11% following the news. And grocery stocks got clobbered after Amazon announced plans to buy Whole Foods (WFM) for $13.7 billion back in June.

Is Amazon a death sentence for traditional retailers? Not necessarily. Retailers like Home Depot (HD) are surviving by selling things you can’t buy on Amazon. Today, Home Depot reported record sales last quarter and bolstered its outlook for 2017. Home owners and professional builders alike still prefer to go to stores to test out home products, especially big ticket items like flooring and appliances.