Artificial Intelligence Improving Logistics

Over the last few years there has been a great deal of interest and development in the area of artificial intelligence (AI) and the potential to improve the supply chain and logistics. There is an immense amount of data available across the supply chain from a combination of Internet of Things (IoT) devices connected directly to supply chain partners and then also a great deal information from your partners across the supply chain network. In addition to the data produced internally through process actions and functionality, there is now an immense amount of data available from external customers to the supply chain network via direct interaction within the extended network through partner portals and direct interactions and integration with supply chain external customers. The Internet and online interactions from eCommerce (B2B and B2C) provide a great deal of this data and supply chain partners would be foolish not to use this data to improve their logistics capabilities.

The challenge for the supply chain network has been two-fold;

• How to utilize the data
• What data to collect for both analysis and use

Artificial intelligence starts with the collection of data for analysis and this is an important starting point. There is no way early on in the collection of data to determine what will be important. This is because the analysis must be performed first to understand the potential use and value of the collected data. The intelligence part of the analysis also requires a great deal of information in order to validate the hypothesis and then to determine direction based on this intelligence. Starting with the data you can see why it is so important to collect everything because the intelligence part of the equation will generally require additional data from the same periods to confirm and validate direction.

It is important to collect all data then because you cannot tell what may be important for the analysis when determining direction. The good news here though is that storage is cheap and the technology to analyze vast amounts of data has been improved through game-changing improvements in technology. These two points have probably done more for the growth and development of artificial intelligence than any other developments over the last few years. These improvements in technology allow the supply chain to refine and redirect logistics activities and practices based on facts and data rather than hunches and hopes. Combine these two technologies with the growth of the Internet of Things capabilities and related technologies and the potential for improvements is almost overwhelming.

These increased capabilities bring improvements and opportunities in automation as well, including warehouse location and slotting, along with drones and robotics to improve efficiencies and especially accuracy while reducing costs. These are not the only areas of improvement than can be achieved through artificial intelligence. There will be improvements in volumes, inbound and outbound forecasts for instance, that will bring dramatic change to the supply chain as manufacturers and transportation providers revise their processes to incorporate the analytics into their plans and procedures. The warehouse operation though will also be dramatically impacted as artificial intelligence brings analytics to the equation that allows the operation to understand and immediately adjust labor forecasts to changes in the operation during the date based on near real time events. Artificial intelligence is just another factor bringing transformation to the supply chain and the supply chain must start with a baseline of continuous improvements in the flexibility and continuous change practices in order to meet the transformations.

Tom Brouillette
Contact: tom.brouillette.@gmail.com

Thank you for reading my post on LinkedIn in addition, Here at my blog I regularly write about management and technology trends. To read my future posts on LinkedIn click 'Follow'

Tom Brouillette discusses supply chain trends and provides strategic business & technology advice to his followers.

Artificial Intelligence Improving Logistics

Over the last few years there has been a great deal of interest and development in the area of artificial intelligence (AI) and the potential to improve the supply chain and logistics.  There is an immense amount of data available across the supply chain from a combination of Internet of Things (IoT) devices connected directly to supply chain partners and the partners across the supply chain network.  In addition to the data produced internally in the organization supply chain actions, there is now an immense amount of data available from external customers to the supply chain network partners via direct interaction within the supply chain network through partner portals and direct interactions  integration points with supply chain external customers.  The Internet and online interactions from eCommerce (B2B and B2C) provide this data.  Supply chain partners would be foolish not to use this data to improve their logistics capabilities.

 

The challenge for the supply chain network has been two fold;

-        How to utilize the data

-        What data to collect for both analysis and use 

 

Artificial intelligence starts with the collection of data for analysis and this is an important starting point.  There is no way early on in the collection of data to determine what will be important.  This is because the analysis must be performed first to understand the potential use and value of the collected data.  The intelligence part of the analysis also requires a great deal of information in order to validate the hypothesis and then to determine direction based on this intelligence.  Starting with the data you can see why it is so important to collect everything because the intelligence part of the equation will generally require additional data from the same periods to confirm and validate direction. 

 

It is important to collect all data then because you cannot tell what may be important for the analysis when determining direction.  The good news here though is that storage is cheap and the technology to analyze vast amounts of data has been improved through game-changing improvements in technology.  These two points have probably done more for the growth and development of artificial intelligence than any other developments over the last few years.  These improvements in technology allow the supply chain to refine and redirect logistics activities and practices based on facts and data rather than hunches and hopes.  Combine these two technologies with the growth of the Internet of Things capabilities and technologies and the potential for improvements is almost overwhelming.

 

These capabilities bring improvements and opportunities in automation including warehouse location and slotting, drones and robotics to improve efficiencies and reduce costs. These are not the only areas of improvement to the achieved through artificial intelligence.  There will be improvements in volumes, inbound and outbound forecasts for instance, that will bring dramatic change to the supply chain as manufacturers and transportation providers revise their processes to incorporate the analytics into their plans and procedures.  The warehouse operation though will also be dramatically impacted as artificial intelligence brings analytics to the equation that allows the operation to understand and immediately adjust labor forecasts to changes in the operation during the date based on near real time events.  Artificial intelligence is just another force bringing transformation to the supply chain and the supply chain must start with a baseline of continuous improvements in the flexibility and continuous change practices in order to meet the transformations.

 

Tom Brouillette

Contact: tom.brouillette.@gmail.com

 

Thank you for reading my post on LinkedIn in addition, Here at my blog I regularly write about

management and technology trends. To read my future posts on LinkedIn click 'Follow'

Tom Brouillette discusses supply chain trends and provides strategic business & technology advice to his followers.

COVID-19 Requires Optionized Supply Chain

I have been discussing supply chain disruption for quite a while now as it relates to consumer demands and natural events such as destructive weather. The COVID-19 pandemic now adds a new and even more real, and more serious, disruption that must be addressed. The key underlying challenge is how to address disruption and especially extreme disruption. We have already entered into a time of extreme discontinuous disruption that demands and absolutely requires immediate response to survive and support the demands created from the disruption. We can no longer ignore the disruption cycle and as a result must focus now on an optionized supply chain rather than an optimized supply chain so the response can be supported through pre-planned actions. The critical requirement is to continue the supply chain and minimize disruption through a robust risk sense and respond strategy. This risk management strategy must become a critical aspect to the supply chain management overarching strategic plan.

The global supply chain is a fact of life now and as a result of the length of time this supply chain has been developing, it is now the norm. Frankly, it would require an unreasonable length of time and money to change, and most importantly, the cost would never be recovered. We must look past the angry demands to move manufacturing ‘back to the US’ and instead take a more reasonable approach to accept and plan for disruption and then develop options and practices to quickly and efficiently adjust to disruption. This means adding an enhanced risk analysis and risk management strategy to the supply chain planning and forecasting tool kit. We must now focus on disruption scenario planning and response option development to ensure that we are able to minimize the impact to the supply chain.

The good news is that we already have practices and tools available to enable disruption options risk analysis and determination. The current state of supply chain software with the addition of big data analytics provides the framework for a robust early risk warning capability to allow implementation of options to address the disruption. We must develop the strategy now utilizing the framework of available tools to support the process. The result of this initiative will be a framework of process and procedures that will allow the extended supply chain partners to sense and respond to disruption based on scenarios and options that have been identified previously.

The challenge with implementing the disruption sense and respond risk analysis framework is not the supply chain management tools. These tools are quite robust and provide the capabilities to adjust quickly and efficiently to the disruptions in a truly efficient manner. These tools over the years have progressed to a state of flexibility in configuration capabilities that is truly quite amazing. The flexibility and configurability has been built into the software to allow a super user to support standard and general maintenance requirements without support of an engineer or software developer. These configuration capabilities I believe are quite capable to support the demands of the ‘optionized’ supply chain to support the risk sense and response framework.

The challenge now from a technology perspective is related to sensing and identification of the risk in a timeframe that allows the supply chain to identify and then implement the appropriate configuration options to manage and flourish during and (most importantly) after, the disruption event. Fortunately, big data and analytics has reached a state that is beginning to support the requirements to sense and determine how to respond to the disruption based on a predetermined business continuity plan. The analytics requires an efficient tool in order to bring the data together from disparate sources into a pool where the analytics can be utilized. This is one of the new continuing supply chain management requirements.

This leads to the next challenge which is to define a robust and flexible business continuity plan that provides suggestions for predetermined options based on the type of disruption. This business continuity plan takes on new meaning and also new use in the supply chain. This plan must be extended first to identify any type of business and supply chain disruption that may occur and then identify the potential for the disruption (risk) along with configuration options to react and adjust to the disruption. In my experience, the business continuity plan has been reviewed at a minimum and normally on an annual basis. The review process must be more rigorous and reviewed more frequently in order to provide the flexibility and direction required to address disruption.

I suggest a two-pronged focus on the review of the business continuity plan to extend the types of disruption and the potential impact along with identification and implementation of a data analytics tool that supports efficient and flexible identification of disruption early. It is most important to identify the potential for disruption so that you can react early and adjust to address the disruption prior to serious supply chain impact. The key focus must be the big data analytics in order to sense and respond to the disruption. Disruption is a way of life now, it is critical to manage the disruption in a way that limits the supply chain disruption.

Tom Brouillette

Contact: tbrouillette@ncspartners.com



@ncspartners

Business Continuity In Extreme Disruption

The coronavirus pandemic is another Black Swan event that highlights the importance of a robust and flexible business continuity plan that utilizes big data analytics technology and artificial intelligence to support a reactive and flexible framework rather than a structured process of recovery. We see a discontinuous river of disruptive events that are impacting the supply chain along with businesses and customers and the reactions to these events must be swift in order to limit the impact. The world will not slow down and in fact will only speed up in the types of disruptive events that must be addressed in creative and flexible ways.

The seemingly continuous stream of Black Swan events speaks to the need to sense and respond to these events to limit or even eliminate the impact. Early detection and response to the pending event dramatically reduces the impact of the event and this should be the goal going forward! It makes no sense at all to develop a quick response system that leaps into action when the impact is upon us. This goal of early detection requires a focus on risk analysis and that allows early detection in order to act before the crisis. I suggest that a key to the rise and frequency in Black Swan events is due in large part to a lack of focus on detection.

This is the reason I have been focused on the continuous improvement practice of sense and response. The supply chain is in the position of canary in the coal mine and the continuous improvement practice including and focused on robust identification, or sensing, analytics to sense the coming disruption early in order to respond early. The goal should be in risk identification and then analysis of the pending impact and most importantly the likelihood of the risk manifesting itself. After the 5th ‘500 year flood’ it's time to understand and take action to focus on risk analysis and early detection!

This increased importance of sensing the disruption requires an increased focus on risk management and especially risk identification as a key area of identification in the continuous improvement process. This is a big change in focus from short term cost control to risk and disruption control. Focus on risk and disruption control will increase supply chain costs however will reduce disruption impact. This reduction in impact will in the long term reduce costs of the impact on the supply chain and the economy.

The challenge for risk management is incorporating the lessons in day-to-day supply chain management and especially the extended supply chain. The correct answer to the global disruption is not eliminating global manufacturing and the global market, the correct answer to global disruption is early detection and risk management plans to address the disruption. This means alternative supply chain capabilities for materials, manufacturing plants and delivery. These all would produce different effects on the success of reaction to the disruption and each of these require focus on risk and cost analysis to identify when it makes sense, financially and production-wise to make the change.

Unfortunately we are in an age of quick and easy answers and this does not work well in a global economy and supply chain. This requires a level of continuous effort to monitor and analyze the risks and likelihood of occurrence. This process of risk mitigation must be incorporated in the regular review and analysis process of the continuous improvement practice. The good news, in my opinion, is that this risk mitigation can be supported more accurately and quickly by embracing data analytics improvements brought about through artificial intelligence technologies. This is a baseline requirement to the effective analysis of the amounts of data available!

Disruptive Forces In The Supply Chain

There are so many factors driving transformation and disruption in the supply chain that we have moved from a model of discontinuous change to a model of discontinuous disruption.

The disruptions in the supply chain now are driven by the developments and incorporation of new capabilities that are more and more identified and developed utilizing the information and capabilities delivered by data analytics and artificial intelligence technologies. The pieces of technologies are being brought together through the immense amount of data and abilities to fully utilize the data that themselves have been made possible by technology. In the same way that technologies have allowed consumers to mold their professional interactions to meet their lifestyle needs, technology is also supporting the supply chain and extended partners to meet the supply chain business interaction and operational needs.

In a very real way consumers are driving the disruption through their embrace of technology combined with increased network capabilities that are blending their interactions across every corner of their professional interactions. Supply chain leaders and partners have embraced growth in data analytics that is made possible by technology improvements in data storage, network improvements to move the data and then most importantly the computing power improvements to drive the data analytics capabilities to analyze the data to understand and define direction and capabilities required to improve the supply chain to meet the demands. The supply chain supports the consumer demands across all industries and the computing power along with the network capabilities available to every individual across the supply chain is driving the discontinuous disruption.

The pieces of discontinuous disruption have been developed and also been growing in their use in the supply chain starting with the growth of the Internet of Things technologies and tools and naturally expanding in the supply chain arena into automation of processes and then add to this the robotic movement that is growing now, especially in the third party logistics industry and you can see that these pieces required a technology and capability to coordinate and connect the dots. All of these technologies are collecting immense amounts of data for the supply chain to utilize to improve the services and increase their capabilities without necessarily increasing the long term costs.

Supply chain leaders are using these technologies and really driven by their own imagination and questioning ‘why not’ to drive this growth and expansion of supply chain processes and operational improvements and most importantly efficiencies. These technologies are disrupting through the implementation and growth of automated operational and process improvements that are driving efficiencies into the supply chain. The true disruption innovation in the supply chain is the imagination that has been released through the implementation of these technologies that are driven in large part by the improvements in analytics and computing power to release and encourage this imaginative approach to disruption.

This is the amazing thing in my opinion, that the consumer is at the center driving the disruption into the supply chain industry based on their demonstrated imagination and use of technologies to put together their own solutions. These strong outside influences are driving the basic need for the supply chain to understand and maintain the velocity of disruption in processes operations and especially functionality in order to meet the consumer and market demands. Most importantly, though is the continuous improvement process and data analytics capabilities that must be implemented to allow the supply chain to sense and respond to the disruption that is now impacting the supply chain marketplace.

Data Analytics And Artificial Intelligence Driving Disruption

One of the most critical capabilities to responding to change and disruption in the marketplace is the ability to sense the change in a manner and, most importantly, timeframe that allows a response to be identified and executed. The frequency and discontinuous nature of change rocking the market requires a robust process that takes into account as many factors as possible to identify the change. This process then must describe factors and relationships to allow them to be analyzed to develop the response. The difficulty lies in the volume of data, both new and old, that must be taken into account to first identify the change and second to guide in determining a response. This is where data analytics practices utilizing artificial intelligence comes into the equation to support the business.

The two challenges; data for analytics and the actual analytics require a thoughtful strategy and approach that will allow you to sense the demands from the market in both a manner and a timeframe that will meet the velocity of change requirements. These challenges go hand-in-hand as two sides of the same coin; you cannot sense the change without a great deal of data to analyze and you cannot analyze the amount of data without the artificial intelligence to process large amounts of data.

From the data perspective it is very fortunate that the big data tools and storage technology has advanced to the stage were the collection and maintenance of the data is no longer an issue. This allows the collection of vast amounts of data available from all points of the supply chain and especially from the eCommerce channels. This data along the supply chain can now easily be captured for detailed analysis and because of the volume of data available the results of the analytics can be more accurate and more informative of trends. The important point here is the collection of data from the viewpoint that the more data the better because you never know where the analysis will take you and in order to come to a conclusion you must have the data to analyze and also prove the concept.

Ten years ago, in the early growth stages of big data, the challenge was the ability to collect and store the amounts of data for analysis and the analytic query tools to quickly perform the analysis. This required careful review of the data available to select the appropriate elements that you believed were necessary to produce the analytic results. Then the data collection required over night collection and the analytics were run to produce large reports in a daily schedule. Everything took time and you had to be careful to analyze the expected outcome. Now though because of the dramatic improvements in the technology the process and results are much more robust and immediate. Now there is no concern about the amount of data and the queries themselves are also much more interactive.

The analytics of the data presents the challenge in this equation and this is where the focus should be placed now. Artificial intelligence tools really come into play from this perspective to provide a value add to the equation. Artificial intelligence and machine learning will be a baseline requirement to allow the market to sense and then determine how to respond to the changing demands. These tools will are necessary to first sense and understand the demands of the market and then these same tools will provide the means to analyze the potential solutions and even forecast the impact to the market of change.

The key benefit of these tools is the speed of analytical results and then combine that with the delivery speed of a solution. Speed of sensing the demand and then speed of response are the objectives that must be front and center for the market and the participants in the market. All indicators based on technology lead to increased speed of demands and resulting disruption in the market and market participants will not have the time required for any manual analysis of these trends. In addition, only failure will come from participants that wait for the market to deliver a solution that they can adopt.

The participants that embrace artificial intelligence combined with machine learning will be the players that succeed and prosper in the market. The rate of failure that we have seen in the market will only increase as the velocity of changing demands increase. The good news is the building block tools to quickly and efficiently sense and respond to the increased velocity of changing demands are already available. The bad news is that the market participants must embrace the tools to develop their own practices to use the tools.

Artificial Intelligence - The Common Strategic Thread

All networks, social, business, integration and collaboration, news and extended supply chain networks are all related through a common thread, data and more specifically - how is the data used to drive decisions and execution strategies? The glue then that combines the data in a manner that can be used to sense and respond to the waves of disruption in the market.

I believe that the constant running through all aspects of the market place is the discontinuous disruption that is driven by consumer embrace of technology combined with the market reactions to the consumer demands. Marketplace partners must recognize this new reality and work to incorporate methods to obtain the data necessary to feed the decision making process. The value and the accuracy is based upon the amount and types of data that are incorporated and available to the process. This precept itself drives the need and the value of collaborative analysis and data collection across the partners.

This model is especially important to the retail marketplace where the market is continuously buffeted by changing demands from consumers that are themselves driven by changing technical capabilities. These reactions and impact from technology has been a fact of life for quite some time within the extended supply chain and the pressure of reaction to disruption combined with the demands of cost containment and reductions are driving collaborative partnerships in the extended supply chain. These partnerships along with the resulting data collaboration can be used as a model for the marketplace as a whole. This is a key reason for the importance placed on the supply chain by Amazon and others would benefit greatly by embracing this practice.

In addition to the importance of collaborative data, the marketplace partners must also take into account the level of accuracy changes that occur during the life cycle of the analysis. The accuracy of the decision goes through a measurable life cycle:

• Infancy starts with sensing the potential reactions to the demands. This is an unclear time where many options can be taken in reaction to the data and the analysis. This is the beginning of experimentation to test the options.
• Mid-term or decision childhood where the options are narrowed based on additional data analysis based on the results of experimentation from the infancy stage. This is the refinement of decisions and strategy based on the results of experimentation. This is marked by refinement and addition of experiments to support analysis refinement.
• Maturity or decision adulthood where the strategy is fully formed and executed based on the refinement resulting from the data analysis. This is also where the next disrupting concept begins to form and requires the continued analysis of results from the strategy to form the new concept and strategy.
• End of life of old age where the positive results of the strategy decline and the consumer and market are driving the new disruption. This is end of the strategy value and it is equally important to be able to sense the decline of the strategy as it is to identify the beginnings of a new disrupting factor.

You can see that the process I described above follows the PDCA, continuous improvement process, which is a standard practice of the supply chain. This provides a solid framework to sense and respond to the disrupting factors and has been incorporated across a wide range of marketplace practices. The disrupting factor that drives improvements to this process is the collaborative data collection and analysis that allows the process to sense potential changes earlier and then through AI analysis provides the guidance for experimentation and validation to formulate the change to react to the disruption.

Effective Inventory Mangement

Inventory management is one of the pillars of a successful retail business and especially critical in the highly competitive eCommerce marketplace. While this statement would seem to go without saying, in the real world, effective inventory management can be very to achieve because of all of the moving parts. Effective inventory management requires first a focus on the basics; attention to detail, strong process management and determination especially when supporting controls and investigations.

Effective inventory management requires execution of complex global inventory management strategies, along with collaboration across internal and external partners that all come together to support the inventory management processes and procedures that support the highly volatile business demands. A focus on inventory management and control is required to support the extended global supply chain that is now key to the retail marketplace.

Inventory management provides the foundation of tools, process and procedures required to succeed in the global retail marketplace. It may not be a flashy capability but it is a base requirement to support the flashy side of retail . Bad inventory decisions can kill an organization’s profitability and wreak havoc on the supply chain. Developing and implementing inventory management controls as part of a seamless global logistics program doesn't just happen overnight. The successful inventory management program will be based on the following building blocks:


Collaboration program across the entire extended supply chain


Logistics planning, forecasting and analysis accuracy through robust sales and operations planning


Cycle counts to eliminate physical inventory


Product count program based on velocity and product demand to ensure availability for high demand product (A, B, C velocity usage)


How often do you analyze the velocity?


Inventory turn management that includes continuous review:


How do you improve inventory turn?


How do kits and co-pack products impact inventory and inventory turn


Inventory stocking practices in a multi DC network


Advanced Analytics supporting forecasting and planning

Inventory planning and forecasting provides an important feature and support in an effective continuous improvement process guiding inventory management practices in the global market. The accuracy of the planning and forecasting are enhanced by the volume of data available from the extended supply chain partners and requires a robust set of tools to perform the analysis. This begins with a focus on implementing and maintaining the fundamentals of inventory management and controls including robust programs starting with manufacturing planning, through cycle count procedures to ensure accuracy through inventory performance analytics supporting forecasting and planning.

Technology adds value to the continuous improvement process by providing a framework that integrates the data available from the from planning and forecasting with the process execution data to improve the ability to sense and respond in a continuous manner. Technology has become an integral partner in these activities to provide the connectivity required for the extended supply chain partners create knowledge from the vast amounts of data that are available. A robust continuous improvement program allows the supply chain partners to sense and respond to the changing inventory control demands that are increasing in both velocity and impact on the supply chain success,





Technology and collaboration has reached the point now where inventory management and control can fully utilize and take advantage of the volume of data to increase the effectiveness of the continuous improvement procedures and data volume support should not be considered a limiting factor. Big data capabilities provide the opportunity for each of the partners in the extended supply chain to both share and utilize data that would not have been imagined even three years ago and by the same token the capabilities of three years from now will be light years ahead. The focus of the supply chain partners must be on starting the continuous inventory management process and to develop the collaborative processes and procedures that will encourage experimentation and improvements.

Age Of Disruption

We have entered into an age of disruption in the market and this disruption is fueled by the consumer embrace of technology and especially the explosion of wireless technology. The technology has allowed consumers to reshape their interactions with retail to support their lifestyles and the velocity of this change has been increasing past the point of market disruption. The market partners must realize now and accept this as the new reality. The velocity and volume of change will not slow and will continue to disrupt the market for a long time. This disruption, though, can be addressed through a robust continuous improvement program, or more appropriately, a continuous disruption program. It is more critical than ever to implement a strong process that can sense the direction of change, or disruption, and also identify the most flexible and robust method to address the disruption.




As change velocity and magnitude increases the market disruption also increases to the point of reaching a continuous disruption model that remakes the market on a regular basis. The tensions of this model have been building for a while now and it seems that the technology advances and especially the wireless technology advances have provided the foundation to deliver the disruption that as been in the wings of the market. Market participants can no longer wait for the change to settle an then implement the new standard because the new standard is changing at a breakneck pace. This continuous disruption will require that market participants increase their rate of change to meet the demands or they will be left behind on the periphery if they are lucky. You can clearly see the results of the disruption in the retail marketplace by the increase in the number of bankruptcies.




This requires the market participants to step up their game, especially in the ways that they are collaborating and developing partnerships to meet the demands. The supply chain is leading this integration and partnership drive and has been developing the tools for a while now. The extended supply chain has developed a strong model for implementing the type of network that will allow the entire marketplace to sense and respond to the demands and even help to create the disruptions in the marketplace. The supply chain extended network supports speedy and efficient integration of new partners and encourages specialized service providers to bring new point solutions to support the supply chain demands.




The way to succeed in the age of disruption is not through harder work but through AI technologies to sense change and then a strong collaborative network to react to the change. No single organization can hope to bring the resources and the flexibility of capabilities to address the disruption (maybe Amazon, but that’s another story). This is where the flexibility and capabilities of the extended supply chain network can be viewed as a model for future direction.

Analytics Key to Disruption

The retail marketplace has developed a robust set of tools to support consumer purchase and delivery over the years providing a solid framework of tools and capabilities that can be combined in many ways to support consumer demands. Now it is important to focus the same kind of attention and effort to develop and support the tools that will allow the marketplace partners to sense the change and respond to the demands of the marketplace. In my opinion, analytics tools and data collection is the key to the ability to sense change and demands and as such provide the key to navigating and responding to the disruption that is rocking and will continue to rock the marketplace. Analytics is truly the next frontier and provides the key to addressing and reacting to the demands for change and interactions.




The disruption will continue and increase in velocity and the change will be focused on collaboration and integration of marketplace services with the consumer. A contributing factor to this integration and collaboration will be services and internet tools that are growing in numbers and capabilities to provide new consumer services and tools that will naturally require integration with omni market shopping and purchasing. These tools and services themselves while driving change into the market are also producing a huge amount of data that can be harvested for analytics by the smart marketplace partners. This is where the smart partners will focus because this data provides them with a goldmine of information and capabilities to sense and respond to the marketplace. This is why assistant software is so important to companies like Amazon, Google and Apple not only does the assistant engage and lock the consumer to the assistant, the assistant is also collecting data specific to the consumer for analytics.




We are now entering an age of analytics as a tool to navigate the velocity and force of disruption in the market. I think this is a bit of a chicken and egg question because I believe that as much as analytics enhances the understanding and the how to address the change, I also believe that the analytics is also driving change to new depths and capabilities because of the types of analytics and the data available and utilized in the analytics. In other words the analytics is actually creating new change and disruption because of the types and breadth of the analytics performed.




Analytics are not only the key to navigating and surviving disruption, the analytics practice and framework is also driving the disruption in ways that were not available just a few years ago. In addition the analytics of the future will also be dramatically different in a few years as well. Analytics is a practice that people will improve with use and addition of data, however, analytics is also much more than just a practice. Analytics is truly the key to disruption; not only sensing and responding to disrupting change but more importantly driving disrupting change into the market. This is truly a practice that must be started and invested in for the future and partners must start immediately so they are not left behind. The velocity of change and disruption will not allow time for waiting to settle, the settling of change is really the introduction of the next change and as a result there can be no waiting to follow the lead.

Big Data And Collaboration

Retailer collaboration practices with consumers has usually been focused at the personal, and in person, level through the employee interaction with their customers.  This must change with the changes introduced by the omni market and the explosion of the social network aspect and capabilities and especially the embrace of this technology from by consumers.  These demands can be especially difficult if the retailer attempts these efforts without a means to analyze the reactions and the interactions with the consumer. This is where the big data practices and capabilities come into play and these capabilities can be utilized to provide the consumer reactions that were captured in the past from the personal and direct one on one interactions with consumers.  Big data can be utilized to produce the analytics and provide the means to explore consumer reactions through their direct reaction and interactions with the omni market technologies.

This is a coming together of technologies and interactions between consumers and the retail marketplace.   These technologies have exploded in the marketplace with new capabilities and interactions on a daily basis, it seems, and the retail marketplace has been incorporating much of these technologies as a result of consumer direction and demands.  The difficulty for retailers lies in their abilities to incorporate these capabilities into their procedures and practices which requires a robust and flexible framework to support the required integrations and interactions. However the benefits can be priceless for these consumers because of the data these technologies and consumer interactions can produce.

Technology improvements has produced the tools and the foundational hardware to support the collection of huge amounts of data to quickly and efficiently analyze the data to answer the questions that will support direct consumer collaboration.  As a result of the advances hardware technologies the big data capabilities can now support the amounts of data collection required to analyze consumer shopping and purchasing practices in the retail omni marketplace. The challenge now for retailers is to understand and embrace the capabilities, while at the same time incorporating new technologies and capabilities to support consumer shopping demands in the retail omni market.  

This is the single greatest challenge for retailers now and probably the single greatest opportunity to support their collaboration efforts with consumers.  The retail omni market produces huge amounts of data based on the consumer direct interaction with the technology and the omni market practices and capabilities of wireless technologies provide the means to support seamless interaction across all channels, also producing huge amounts of data.  All of this data availability will do no good for the retailer without big data technologies and this technology requires expertise and imagination to analyze the data.

These are challenging times for many of the large legacy retailers because of the velocity of change demanded by consumers and the retail omni market.  Layer on top of this the requirement to understand and then plan for changes and the challenge can become almost overwhelming. Big data technologies and analytics will add to this complexity and challenge on the one hand and then on the other hand these technologies will provide the knowledge based on factual data collections to help the retailer succeed and navigate the demands from the market.

Retail Supply Chain Analytics

The retail omni market provides a basis and opportunity to collect vast amounts of data from a range of data points to support the types of analytics required to understand and adapt to the changing marketplace.  The omni market really provides methods to collect data points from all aspects of the customer experience that can then be used to develop customer relationship and also programs, product and services to meet the consumer demands in the marketplace. The omni market really provides a remarkable opportunity for the retailer and all of their partners in the market, including the end customer to increase collaboration opportunities and most importantly build the relationships that will help the retailer and their partners to succeed.

All of the pieces and parts have been coming together over the years now to provide the framework and infrastructure to collect and store along with the horsepower to process the vast amounts of data required to perform the analytics to understand and make decisions to drive the business.  The technology is really coming into a maturity that can provide tremendous value, if the retailer in this case is imaginative and also skilled in analytics. As the technology advances there is more and more opportunity to grow and expand expertise based on your desire and imagination.

The challenge in the retail omni market now is not the technology, it is what should be collected and how should it be analyzed.  There is now more than ever before a level of data available to the retailer that can support analytics of the shopping and purchasing process that could never have been achieved in the past.  The omni market provides the opportunity and the collection points that would allow the retailer, and their partners, to achieve a level of understanding of the consumer habits that would allow the retailer to develop the methods to truly customize the shopping and purchasing experience based on the demands of the consumer.

Retailers have focused on the operational processes and capabilities to support consumer purchasing demands based on the marketplace, in other words the offerings to consumers have been based on the offerings of competitors and focused on sales and operational efficiencies.  The analytics now available to the retailer will allow them to develop relationships with consumers and extend the relationships of their partners into the customer experience area to focus on the reasons why consumers shop and purchase from the retailer. Retailers cannot afford to follow the leaders the market because the market is changing to quickly and the analytics allows the retailers to identify trends and demands earlier than in the past.

The challenge now for retailers is to embrace the tools and capabilities that will allow them to detect the trends and direction early.  This means a framework that collects the data for analysis and even more importantly provides a feedback framework to support the change implementation.  This is another tool or practice in the continuous change framework that can easily be utilized to support and deliver the success of the consumer. Now is the time though to start because this framework requires investment in both technology and skills to support the retail market demands.

Retail Analysis Objectives

The surface objective of retailers for a predictive analysis program is the identification, understanding and early reaction to market and consumer trends.  I see the secondary and probably more important objective for this predictive analysis is the development of collaborative relationships with consumers and the extended market partners.  This collaboration objective will support the retailers’ success and longevity in the omni market in a sustainable manner that eliminates the ‘me too’ practices that are currently prevalent in the market.  These ‘me too’ practices are practiced in place of the creative and collaborative practices and capabilities that are required for retailers to succeed in the future.  It is important though for sustained success to develop a personal relationship with consumers and retail partners that is built on collaboration.

Data is only valuable when it is used as a basis for analysis and confirmation of the analysis.  To this point data values volume in order to improve the analysis and the confirmation of hypothesis.  The challenge of the collection and analysis is that you really do not know where the analysis and the confirmation is going to take you and so you must collect a great deal of data. This is the basis of big data analysis and this is the types of activities at which big data collection and analytics excels and these are the activities that big data encourages.  The value produced from the analysis increases with the volume of data and also the analysis skills, in other words, there is a level of experimentation in this analysis that continuously drives the investigation for validation.  

Fortunately for retailers the omni market retail environment provides a treasure trove of shopping and purchasing data that was not previously available to retailers.  It is important for retailers to collect this information as a matter of course during the operational activities related to completing sales and filling delivery to consumers that can be added to other operational data related to receiving, transportation and forecast data that can be utilized in support of the retail operations.  There is a tremendous amount of additional data available to retailers as a result of the online shopping and purchasing operation which also should be added to the collection of data to provide additional data points for analysis.  This collection during the operational activities would best be supported via a control tower framework that would allow the data to efficiently flow to and from the appropriate containers and processes.

This change necessary now is to recognize both the availability of data and the value of this data in extending and growing collaborative relationships across the entire chain.  There is a treasure trove of data generated by online activities now that has grown in volume and potential value and considering the growth of the omni market in retail this data is continuing to dramatically increase in both volume and potential value.  This requires though that legacy retailers begin to collect the data at a minimum to support the collaborative opportunities that will result from the omni market.  Do not wait while focused on the operational impact of the omni market this will only delay the potential value.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?

Inventory Forecasting Strategy

As a result of the changing consumer demands driving dramatic changes in the retail marketplace to implement a true multichannel experience retailers must overhaul their inventory forecasting strategy.  Consumer demands are driving purchases across channels which in turn is driving changing into the inventory planning and forecasting requirements to increase flexibility in the inventory placement.  This requires retailers to expand big data and analysis requirements in order to take into account the many new variables that are impacting the forecast and the forecast strategy.  One of the greatest challenges for retailers is the rate and impact of changes that are buffeting retailers now based on new and changing consumer demands.

The combination of the consumer changing demands and the velocity of these changing demands is disrupting large legacy retailers’ abilities to react to the changes and meet the demands.  The physical and virtual channels are blending into a cohesive consumer shopping experience and this requires retailers to adjust their inventory forecasting strategy.  These adjustments focus on an increased velocity of forecast and replenishment cycles, along with the fluid movement of inventory across channels based on changing consumer shopping and purchasing demands.  

These changes require the forecasting strategy changing to incorporate and analyze a great deal of information to be processed in much shorter cycles.  Internet time takes on more importance now for retailers and especially large legacy retailers and Internet time dramatically increases the velocity of changes to inventory demands.  These changes in velocity of inventory demands and especially demand across channels requires much shorter inventory forecasting cycles in order to support the need to quickly adjust to the changes in demand.  The demand for a product can explode in an extremely short period of time and by the same token it can also drop in short period of time and these discontinuous fluctuations in inventory demand wreak havoc on forecasting.

Large national retailers have additional complexity in their inventory forecast requirements based on fluctuations in regional demand for products.  These demands can actually provide benefits for both early reads on product demand and also, on the flip side, an outlet for overstock liquidation.   It is completely unrealistic to believe that retailers can meet all of the changing demands and this means that retailers must account for inventory fluctuations in demand in their forecast strategy.  This is where large national have an advantage of an early warning system for potential demand and then a natural outlet for overstock at the tail end of the cycle.  This provide some leeway to the forecast accuracy and should also be taken into account in revisions to the retailer's inventory forecasting strategy.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?

Collaborative Supply Chain

There are new and expanded demands in today’s retail supply chain to increase and improve the collaboration across all partners both internal and external.  Consumers are included in this increased and improved collaborative supply chain in a manner and depth that is creating new norm in the retail supply chain.  Most retailers have been interacting with consumers via a handful of tools, including product reviews and surveys covering shopping and purchasing experiences.  The more advanced retailers are engaging consumers directly in automated focus groups to collect opinions regarding product and lifestyle advertising.  While these are all productive and healthy steps in the right direction they must continue to expand collaboration with the consumers to engage in relationship shopping and sales to maintain their position in the marketplace.

As the collaborative relationship with customers grows and expands, the retailers will also need to expand their collaborative relationship with their other supply chain partners in order to react to the demands of the consumer.  As an example, expanding the relationship with customers will improve the demand forecasts and I expect this will then change the manner and cycle in which retailers purchase from their suppliers.  Then changes in the supply purchasing cycle will flow into changing requirements in transportation and shipping of products.  Shorter demand purchasing cycles that result from the refinement of demand planning will ripple across the extended supply chain and change the relationships and methods to support the demands.  

These changes can be a very positive thing and result in improved inventory management and placement along with a reduction in overstock inventory.  Improvements and the expansion of the consumer relationship and collaboration will allow retailers to gain insight into their customer demands at an earlier time in the cycle.  This will be especially supported by the increase in data available from the improved relationship along with the increase in eCommerce sales.  In the past retailers were basing their plans on the fashion developers’ tastes and new product development along with the actual sales of the products and similar products.  This was inaccurate and allowed for fluctuations in demand along with missed sales that were not realized.

eCommerce and online sites allow the retailer to capture all of this lost data for action because the provide the ability to capture the consumer shopping patterns when they are shopping on the site.  Retailers can determine the sequence of shopping and selection and these sequence patterns can then be turned into more accurate demand forecasts. So you can see how important it is for retailers to utilize every communication channel available across the supply chain to improve communications and collaboration.  All retailers now need to focus on relationship selling that is supported by the collaboration across all channels and partners.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?

Multi-Channel Inventory Management

Inventory management in the multi-channel retail marketplace can be difficult due to the changing demand from different channels and different geographic locations.  It requires dramatic changes from retailers to their purchasing procedures to support the consumer demands for purchasing.  The consumer shopping demands are blending channels and the consumer lifestyle is fueling these changes that are also driving changes to the consumer purchasing patterns and methods.  All of these changes are then fueled by mobile technologies and improvements in wireless networks to support the consumer shopping and purchasing demands.  All of these changes in consumer practices then drive changes in retail requirements to support these consumer practices.

These changes in consumer practices require that retailers blend their capabilities across channels to support direct to consumer shipments from all channels.  This simplifies the inventory planning to some degree because it blends the total inventory demand forecasting into a combination of inventory demands from all retail channels. It makes it more difficult in the manner in which the inventory is then consumed and the forecast to determine the channel.  From a simplistic approach though, the inventory planning and resulting consumption in this new retail multi-channel marketplace should result in lower overstock inventories and increased sales.  

There seems to be a disconnect though between the current reaity what I describe as an expected inventory forecasting and changes to the inventory management that would result in improvements to inventory management outcomes.  We are seeing instead what appears to be no change in the levels of overstock inventory, especially in the large legacy retailers and in fact the overstock inventory seems to be increasing in the case of one retailer.  This is where big data analysis comes into the picture to help retailers understand the changes to the flow of inventory by channel and by region to allow them to improve the inventory management to meet the consumer demands.  

A considerable challenge for retailers to enacting these changes is presented from both the business process, including culture, revisions requirements and the cost of technology upgrades required to implement forecasting and inventory management improvements along with the business process changes.  Blending the support of purchasing across all channels requires that all channels efficiently execute the particular processes across channels.  Bottom line the retail store must have the ability to ship customer orders using similar processes as a distribution center configured to ship orders to the consumer.  These changes to forecasting and inventory management require significant change to business process in addition to technology.  This means that the retailer must increase efficiencies and capabilities to fill customer orders shipped from the local store, along with improvements in inventory movements between stores and regions to support these demands.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?

Collaborative Inventory Planning

Inventory planning practices are driven to a much more fluid process due to the changes in consumer shopping and purchasing practices.  Planning and forecasting must take into account now the changing consumer shopping as well as purchasing practices to provide a model that can support the opportunistic purchasing practices that are increasing in volume and importance.  The integration of technology into the consumer practices is the single greatest challenge for retailers to overcome and these consumer changes are rippling through the marketplace and all practices supporting the marketplace.  Consumers are now practicing more fluid shopping and purchasing based on the technology and I feel that one of the greatest impacts is presented by mobile technology and wireless technologies.

The good news for retailers and the extended supply chain is that this increase in technology utilization provides a great opportunity for retailers to interact and collaborate directly with consumers.  The bad news for retailers is that this increase in technology utilization allows consumers to shop across multiple channels at the same time, allowing consumers to purchase any time from any channel.  This flexibility then increases the complexity of inventory planning and forecasting and this is driving additional disruption in the marketplace.  These consumer practices disrupt the forecasting and planning accuracy and this disruption of accuracy leads to increases in markdowns and on the flipside this also leads to lost sales when inventory is not available for purchase.

More than ever, everything is connected in the extended supply chain and as a result, inventory planning and forecasting must also take into account these connections and data available from these connections to improve the accuracy of the forecast.  This means that retailers must extend their collaborative relationships and include the data collected from these relationships to improve their planning and forecasting.  Technology is driving the opportunities for both collaboration and extensions to the sales channels and these opportunities must be taken into account when developing inventory plans and forecasts.  

The good news in this story for retailers is that technology is also available for retailers to increase their collaboration with consumers and then to maintain the information collected.  Big data capabilities provide the means for retailers to make this data collected actionable to improve their forecasts.  Consumers are investing in technology at greater levels and then using this technology to support their lifestyle changes and requirements.  Retailers must now also invest in technology and business process improvements to support the consumer changes.  This includes using technology and social networking tools to collaborate with consumers to improve their inventory planning and forecasting accuracy.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?

Social Commerce Data Challenge

Social commerce and purchasing can and should be the most data intensive practice in the retail industry.  The data availability and variety available in the retail industry can be overwhelming causing delays and analysis paralysis in trying to determine what to collect and how to use the data.  This is real a very old problem going back to the beginning of the big data practices and the struggle will continue into the future.  The challenge really is not in the data collection aspect of the equation, the challenge is in how to use the data and what data to use.  Social commerce brings an additional variable to the challenge because the data is spread across partners and even industries that can bring value to the analysis.  

This turns into a bit of a detective challenge to follow the data through the social commerce process from social networks to shopping to the purchase and finally the consumer delivery.  Every step along the way there is a great deal of activity related data that can help the partners better understand and promote their relationship with consumers. The large legacy retailers must replace the data they have been capturing related to consumer shopping habit in the brick and mortar store with the data from the virtual marketplace in order to understand patterns and develop consumer collaboration techniques and practices.  As an example, Wal Mart is a master at product placement in their brick and mortar stores as a result of their consumer shopping pattern data collection and analysis, they are able to determine the best placement for products to drive sales and shoppers through the store.  Now Wal Mart has access to their web site shopping and purchasing habits which can be very helpful however they have a more difficult time determining what shoppers are doing prior to stopping at their web site in the virtual world.  

This requirement for data and especially consumer collaboration increases based on the type of products and retail outlets.  For instance a retailer such as Neiman Marcus would have a much greater demand for consumer touch point ability and consumer collaboration while retailers such as Sears or Wal Mart do not have the same requirements,especially when commodity type products are involved.  As the requirements for data increases the requirement for collaboration and partnerships also increases.  This is because the value of the data increases as the span and range of data increases.  The Internet provides a great opportunity to understand consumer shopping patterns based on consumer touch points and this value can only be unlocked by collaboration and partnership.

There is a question of consumer identify security involved in this discussion as well.  Consumer identify must be protected at all costs and this must be taken into account when tracking activities.  I would be a catastrophic problem for retailers if there was a consumer identity data breach that could be tracked back to the big data collection and analysis.

And now for the audience participation portion of the show…

ECommerce will have wide ranging impacts on both the retail and manufacturing sectors.  How can you focus these abilities to improve the consumer's experience?  Improving the consumer’s experience will require a re-evaluation of the sales channels, the manufacturing channels and practices and the supply chain channels and practices from the raw materials to the consumers’ homes.  In order to ensure and maintain success in this new reality you must harness the tools and capabilities in many new areas.  How can you support these continuously changing requirements?