Dynamics partners have a huge opportunity to reinvent supply chain scenarios and deliver a data-driven application that uses machine learning, optimization solvers and other advanced analytics to embed intelligence and surround the traditional rule-based automated business process enabled by Dynamics. They could do this by dynamically predicting supply chain input. Instead of asking a supply chain planner to enter the ‘temperature’ of a healthy supply chain and then assume that the supply chain runs on that ‘temperature’ all the time, new solutions and services should derive insights and predictions from ambient data, predict what ‘temperature’ is likely to be and provide alternate execution paths to the supply chain planner. An application like this that embeds Cortana analytics suite components into Dynamics business process and is delivered on Azure platform can be built using existing technology - will provide the ecosystem players with most bang for the buck, short time-to-market, will appeal to existing Dynamics customers, will showcase power of Microsoft platform and will provide unmatched differentiation.

SUPPLY CHAINS 2020

Just like service sector is abuzz with talk of how Big data has potential to revolutionize various industries like financial services, healthcare, tourism, similarly in the supply chain field there is no dearth of “experts” claiming how Big data and related technologies will make and break business models. Just like in late 1990s there was a big wave of investments in e-commerce, today coupled with Big data, digital commerce trends are shaping the thinking of supply chain executives who want to build a supply chain that an listen and learn and sense and shape demand. If “every company is a software company” is true, there is truth in “every trader wants to be an Amazon” and “every manufacturer wants to sell an experience and not just a product”. Just like in 1990s financial services firms particularly traders and brokers moved to a high speed digital system reliant on advanced analytics delivered in the back office by data scientists, today supply chain executives are looking to use similar technologies. Supply chain planning will become much more automated and will be dependent on scientific models rather than being based on manual transaction processing systems. Just like in late 1990s supply chain executives in large global firms bought every software that came with a promise of improving supply chain, today again there is an appetite. However, there are key differences and those who understand the differences can capitalize on this opportunity:

1. Supply chain executives want solutions that help their supply chains become resilient and/or agile, not just reliable. And in some cases for supply chain leaders they are looking for demand-driven approaches.
2. Larger companies are still digesting all the software from 1990s, real appetite is with SMB companies who are looking towards cloud computing vendors to provide them such services at costs they can afford.
3. SMB executives want point solutions so they can quickly try them and they also want end-to-end solutions in the cloud
4. SMB executives are more happy to try such solutions in a subscription model
5. CIO maybe a Chief Integration Officer in larger enterprises but SMB segment is mindful of integration cost and measured about what IT or LOB executives buy

INTELLIGENT BUSINESS PROCESS

It is true that cloud offers more choice to customers to switch between vendors, however it is also true that this new found convenience will become the norm and IT will find the task of moving to another cloud as onerous as in on premise world. Microsoft provides one of the best and easiest to use full lifecycle data management tools in Azure and some will consider this enough incentive for customers to stick to Azure. However, economics will force large platform vendors to move beyond tools. Just like with the advent of power utilities Edison Electricity Company moved to become GE to provide not just electricity grid platforms and components but also industrial and household appliances, Microsoft together with its partners will reinvent and provide higher value adding applications on its’ data platform. More data-driven applications will get more data which will spur demand for more applications. With MS strength in the enterprise, and GTM geared towards enterprise buyers’, business applications are a natural area to showcase such advanced analytics applications. Within business applications - sales, marketing and supply chain applications are hottest from a VC investment point of view and Microsoft Dynamics applications provide the necessary beachhead in the growing SMB market – and therefore this is the time for Dynamics partners to lead the way.

Microsoft partners should reinvent scenarios in their vertical and deliver a data-driven e2e application that uses machine learning, optimization solvers and other advanced analytics to embed intelligence and surround the traditional rule-based automated business process enabled by Dynamics.

Supply chain specific Discussion

Let’s look a level deeper by considering an example of what could be done. Economic order quantity (EOQ) is the number of widgets you order so ordering costs and inventory holing costs are in balance. We should ask ourselves how someone can really know EOQ when purchasing costs are so hard to nail-down and inventory carrying costs vary depending on geography, distribution center, maintenance, insurance, taxation and change with time. Could it be that EOQ is just one of those highly-averaged broad-brush measures that was good enough to give a scientific “feel” to punch-card era accountants of 60s and 70s? Isn’t it obvious that measures like these were invented by statisticians for accountants who both lived in some data dearth decade? We should ask ourselves – is an individual click on internet really more value-adding than an average product stored in a warehouse. My opinion is perhaps not. Then why is it that it is possible to tag, track & monitor clickstream data and use it to offer a very personalized advertisement for each one of us say, valued for hundredth of a penny but we are happy to use one gross overall EOQ average and keep ordering same quantity of product for years to come with complete disregard to actual demand at the time, cost of product being ordered etc. and destroy millions of dollars of value in the process? Is that because of some grand plan somewhere or is it simply because data science hasn’t reached the supply chain domain yet? No one really knows EOQ but it has been the centrepiece of inventory management ever since such solutions were first implemented in 70s. Millions of products are ordered, stocked, sold based on such unscientific static measures and millions of dollars of value is lost in the process. Many best in class companies know this and are reinventing such systems like Amazon.

Same applies to reorder level. Every company who uses reorder levels sets them statically once for a fairly long time. If they plan to revise this number they will probably look at ABC classification and do so for select high-value fast-moving A class items. They would never get to do more than that and will never get to long-tail products. It’s not an exaggeration to say that the mathematical ingenuity of the whole supply chain discipline is contained within the bounds of Pareto principle! Most software solutions will only allow possibility to set one value, some more recent solutions do dynamic calculation of this metric. Safety stock as the name applies is used to compensate for forecast error. Again most traditional vendors offer a possibility to setup static value per product-location. Same thing can be said about minimum quantities, maximum quantities and standard order quantities in both distribution and production.

Above are just examples. We can ask same questions for every metric, every process. Every time we make a decision based on space-time bound metric we can ask - will capturing more data make the metric more fluid in space-time, more continuous instead of discrete? Will it improve quality of decisions? Is airline pricing more fluid than concert tickets than the price of that polo you want to buy? Probably, Yes. Does a hotel reservation system know its’ inventory (hence availability) at any time? Probably yes, but does a retailer know? How can we reduce data latency between two nodes in a supply chain?

Delivering point solutions like say predicting reorder level of inventory or predicting willingness to pay for retailers or distributors of consumer goods or simply, showing customer segmentation could be one of the options to show the potential hidden in troves of data. Another option could be to build vertical specific apps focused in a particular domain/problems space that combine many such prediction and optimization services together in a chain of decisions which could answer higher level questions like “which is the best distribution center to service an incoming customer order from?” or “what is the best price for a product given customer and channel”, “how best to price a service contract” or “which are most valuable customers”? Cortana Analytics suite has some apps that illustrate this point. Recommendation service is in instance where the cross-sell and up-sell problem has been rethought and a traditional rule-based system has been upended. Similarly, Dynamics partners could reinvent several other sales, marketing and supply chain problems.

However, it’s important to realize that while advanced analytics provides the most needed deep insight, the number of users it touches is fairly limited in most scenarios. Customers are willing to pay top dollar if such insights are integrated into a business process and create a sort of decision management system – that reduces variability and improves reliability and repeatability of the process that most of the organization is engaged in. In the supply chains of future all benefits will be directly related to the speed of flow of materials and information. Therefore, to derive greatest long-term value and stickiness Dynamics partners should integrate insights deep into a business process so a business user like a (1) customer service rep can automatically get the suggested price from Cortana analytics suite while she is in the process of creating a sales order for a customer order within the context of order management process or (2) so that the system automatically choses the right distribution center to ship from while consumer entered order is being logged into Dynamics.  

Since most companies even in SMB and mid-market space have multiple business solutions, to provide customer with maximum value at the outset, Dynamics partners could use Azure data catalogue (part of Cortana analytics suite) to publish the result set of such advanced analytics apps into the company data catalogue and provide custom visualizations in Power BI so that the data latency is kept to minimum and business can quickly utilize the results where they want and IT can quickly build the required integrations to in-house or other 3^rdparty solutions which may lie outside of Microsoft ecosystem.  

OBSERVATIONS FROM ADVANCED ANALYTICS PoCs

Not all projects mentioned below reach a conclusion, some of those that reach a conclusion are not necessary successful. But it still gives you a flavor of type of projects.

JJ Foods is a food service company that distributes food products in UK. They first came to us because they felt that after moving their shop online they have significantly improved order taking process but they have lost the ability to cross-sell and up-sell. They implemented recommendation engine (Sage) and derived 5% uplift to revenue. They are using it in live environment since Nov 2014. In the beginning such a large lift is expected in environments where there are rich pickings to be had if such an analysis has not been performed earlier. Even
today (Nov 2015) the lift is between 1-2%, which proves long term value of the service. Then they implemented a self-developed Azure ML model that predicts what their B2B customer (a restaurant owner, a school canteen manager) is likely to buy when s/he logs online. This has helped improve order taking efficiency even further. Then they implemented a simple algorithm to classify their customers which their call-center agents now use to determine what sort of offers/discounts they can offer to that customer - classification is automated but not offers - something that can be done in future. Then they integrated their recommendation engine to the call-center application running on Dynamics AX platform. Now call-center agents can complete orders even faster and can cross-sell and up-sell on phone. Being in a true Omni-channel environment, they built an iPhone/iPad app so their customers can use the app to place orders into Dynamics AX backend. All the Azure ML services including recommendations are surfaced up in this app. Now we are experimenting Azure stream analytics to
automatically send alerts if temperature readings go beyond acceptable limits in any of the delivery trucks on the road. We are also experimenting product pricing with JJ Foods for their bi-weekly fixed-price catalogues. Customer is really happy and is a showcase of how automated business process in Dynamics can be made more intelligent by surrounding it with such Azure based services. They use Relex solutions software that provides demand forecasting, inventory optimization and purchase optimization in a browser based application that runs in-memory and is offered through a subscription model. JJ has a large own IT team which is a key reason they have been able to build out quite quickly. CIO recognizes that there are massive efficiencies to be gained in all parts of their business by using more data-driven solutions. Privately held. US$300m. UK.

Company A is an industrial distributor that procures quarter of million different industrial supply SKUs from 16000 suppliers and supplies them to its customers. Company is experimenting with us on demand forecasting and inventory optimization as part of their ongoing Dynamics AX implementation. They would like us to be able to predict demand with high accuracy for non-cyclic, non-seasonal products with sparse demand. Then they want us to build them a system that predicts correct safety stock, re-order level and order quantity for various products. CIO recognizes the lead that his company can get by optimizing and making dynamic decisions to improve efficiencies in a volume spare parts business. Privately held. US$250m. NJ, US. 

Company B, is a typical distributor that provides financial, IT and administration support to hundreds of companies in the group all across US and Europe. They are implementing a new Intershop based e-commerce portal for B2B sales of their products. They were looking for a revenue lifting technology that will help sweeten their e-commerce portal offering internally for their group companies. They are very excited with the models built for them using the recommendations engine. They are evaluating cost of the service, cost of the integrations to their Dynamics AX based call-center and other internal systems before they go-live with the recommendations offering. Company has an own IT team so once after their decision, they could go live quickly with minimal assistance from Microsoft ecosystem. In future, they will like us to advise them on various inventory management aspects. Public. US$5B. NL.

Company C is the largest food company in Israel. This is still a Dynamics AX prospect but was very impressed with recommendations engine in the sales cycle. Sales team built a model to show them the value and now they have started a separate RFP process for recommendations offering. They already own most of the market they can in Israel and see recommendations as the most cost-effective way to increase revenues by cross-selling other products to existing customers. Public. US$1B. IL

Company D is an electrical parts distributor that distributes 400k electrical SKUs to its customers through its many channels including 80+ physical stores all over Italy. Company is experimenting with us on recommendations which they see as a significant way to grow their revenue given their massive product catalogue and complexity. They are keen to work with us on demand forecasting and inventory optimization as part of their ongoing Dynamics AX implementation. Initial analysis of their data shows about 1000 products heavily used and an extremely long tail of products that no one is adding to their carts. A lot of potential to save costs and focus efforts on high value products. Private. US$500m. IT.

Company E is an automotive parts manufacturer that manufacturers and supplies parts to RV (Recreational vehicle) market in US. Company experimented with us on demand forecasting. We built a model for them that had lower forecast errors than ForecastPro an old PC based solution with a battery of algorithms. However they are not willing to adopt the model unless we build a full data pipeline and Dynamics integration for them so they can readily consume the forecasting results in their Dynamics AX and NAV applications with a click of a button. Company is one of these customers where the supply chain professional, procurement department is very old-school and stuck to their EOQ, re-order type calculation and cannot see the light beyond this type of calculation. Although the director, supply chain is very innovative and has been very impressed by in-memory based MRP (manufacturing resource planning) offering from Kinaxis. He is willing to keep experiment with us and get advanced analytics based supply chain solutions as long as we can build a first-class consumption
experience (e2e solution, not point solutions). US$750m. IL, US

Company F is a computer parts and accessories distributor to private individuals and businesses. They engaged with us last year to implement recommendations service together with their Dynamics AX implementation both online and in the call-center. However, the Dynamics AX project is delayed, IT directors were changed a few times so this engagement is stalled. Although what was clear was that with more than 100k products in their catalogue they could easily see how their existing solutions were not sufficient enough to provide them with any
suitable inventory management, distributed order management and trade/promotions management that they desired. Private. US$1B. US.

Company G is a convenience store chain who was looking for a solution to cross-sell and up-sell to its shoppers at gas stations spread through the country. They experimented with recommendations engine but didn’t come back. The results didn’t look very exciting. It seemed that most their shoppers in the morning bought a croissant or a sausage roll and a coffee or a Coca-Cola. Perhaps they could be offered a newspaper too. And all their evening/night shoppers asked for a pizza slice and a drink. These seemed like very set patterns and a basket
of complements for which there didn’t seem much more to do. Whatever there is to be done can be done by the POS agent like offering a chocolate, chewing gum or a bag of chips. Private. US$200m. NO.

Company H is a food science company that develops flavours, aromas and textures for large food manufacturing and food service fast food chains. They are interested in several scenarios like A/R prediction and data quality services. CIO is very innovative and regards predictive analytics as really ground breaking for his business. They were awarded most innovative manufacturing solution award last year by Microsoft. However currently their attention on this area is patchy. If we build something, they will use and will be very happy to get going quickly. Private. US$1B. IL, US.

Dynamics automates business processes. And does so cheaply and simply and you know that. But what if business processes could be made more intelligent instead of just automated. Automation among other things, brings consistency and improves productivity and you know that too. That’s why you want it. But intelligence could do all of that at a different scale and do it in real-time and could be flexible as business changes perhaps at a fraction of cost. By intelligence I simply mean that instead of a system making rule-based decisions such decisions are made using big data technologies in the cloud in real-time. This opens up the possibility of using various data sources including social data and the possibility of using computing power of the cloud.

Variety, velocity and volume of data is increasing at tremendous pace. Technologies to work with this data are being offered in troves and data is the new oil…. Now we have all been hearing about such possibilities for some time now. But are companies really using and
leveraging such technologies and are these really useful? Jury is still out.

However I will like to share with you our first successful story in Dynamics space.

This post will provide you with a real example of a LIVE customer that benefits from this approach. And perhaps will inspire you to think of scenarios where you can use it in your own business.

CASE STUDY

Let's call our customer Contoso. Contoso is the UK’s leading foodservice delivery and collection provider, supplying full- range of food stuffs across the UK. It is a leading supplier to Restaurants, cars, pubs, café's and schools. It delivers five thousand high-quality, affordable food products to over 45,000 different establishments.

CUSTOMER BACKGROUND

Food service in UK is a highly competitive industry with relatively low barriers to entry. Most players source & distribute low-priced, low-margin simple food products and kitchen supplies from similar set of manufacturers and sell them to similar type of customers in their own regions in UK. The low-to-medium price-range food service business cannot be profitable by differentiating on product quality or by cost cutting alone.

Contoso therefore deploys advanced technology to provide highly differentiated customer service and has managed to grow at higher than market rate consistently for past ten years. Because customers are fickle and there are no real costs to switching suppliers for them, to keep existing customers and to grow the customer base Contoso strives to delight customers by offering new services and reinventing existing ones with laser-sharp attention to detail.

Contoso uses Dynamics AX 2012 as the central source of truth for all its other applications. Contoso has two sales channels - both online portal and call center contribute to sales equally. Call-center is the traditional channel through which all of business was routed once upon a time but Contoso introduced online portal to stay ahead of the curve, reduce call center costs and make it possible for customers to place an order anytime anywhere. Call center agents use Dynamics AX to record call history and create sales orders. Call center agents make guided selling possible which improves customer service experience and can also help with up-sells.

Online portal allows the flexibility for customers to place their orders whenever they like. Product catalogue is pulled from Dynamics AX and shown on the portal, customers complete the order online which creates sales orders in Dynamics AX for warehousing and delivery processes.

Contoso's quest to improve customer experience led them to big data technologies. In the beginning they only had one key requirement - how to predict what a customer is likely to buy today given all their past purchases in the current and past months. This prediction
will allow them to present only those products on the main page. And you may ask what is the benefit of this? Does the benefit justify the cost of building a predictive model? The key benefit is that ordering time is reduced, user does not have to scroll on the page or go to other pages or search through the portal. Same applies in the call center scenario. In a B2B scenario where chefs or restaurant managers have to order food ingredients and kitchen supplies they need online almost daily from many different sources, shaving off a few minutes and making their experience with Contoso stand-out among the competition is a very significant differentiator. What impressed us was the attention to customer experience and how central customer experience is to Contoso's investment decisions.

And if the predictive model could not only be built using simple and inexpensive tools but also could be changed, tested, trained, deployed and consumed without any up-front investment - what more could Contoso ask for?

Awareness that such technologies are already being offered by Microsoft is low currently in the Dynamics world. And more importantly that the real business value of these technologies is only in lighting-up business processes which live inside Dynamics.

So from the first key requirement this project with Contoso is now at a stage where we provide a number of different services.

1. We present next purchase
2. We offer recommendations
3. We analyse customer churn
4. And then we classify customers value to
   the business

We build predictive models in Azure ML, we leverage Azure ML apps available in Azure marketplace and we call Azure ML services real-time from within Dynamics.

Purchasing history and clickstream data are collected and ingested into these models automatically.

There are various predictive models, some are trained weekly others are trained on the fly.

There is still some work remaining to make the data flow seamless and automatic and we are working on improving this.

Let's see each of the Azure ML services created in a bit more detail.

CUSTOMER CLASSIFICATION

Customers are classified into different levels based on a mining model built in Azure ML studio. This classification helps call-center agents in decision making. Going forward, we will base promotion offers on this dynamic customer classification.

CUSTOMER'S NEXT PURCHASE

Based on purchase history, customer’s next purchase is predicted and presented products on the portal and in call-center are ordered by likeliness to buy today. This prediction is based on a model built in Azure ML studio. Customer’s next purchase is expected to delight the customer by presenting what she needs on a given day. It also increases order taking efficiency.

RECOMMENDATIONS AS A SERVICE

Customer has been live for a month and we have seen consistently that 20% of all the provided recommendations are clicked on by the users which is a very high number given this is a B2B space and customers/users are busy people whose main focus is to get done with the
order quickly. Almost 5% of the items in the final shopping cart are coming from the recommendations provided. If this number continues to hold this would lead to about 5% sales uplift which is also very high. Customer's expectation is 1-2% lift in the long-term. For Contoso this translates to £1-2m lift. Contoso consumes about half-a-million prediction events per month.

Three types of recommendations are offered in real-time

Market basket analysis produces frequently bought together recommendation when customer choses to put an item into the cart.When user clicks on a product, a real time call is made to Azure ML. The Azure ML service returns the items that are frequently bought together with this item. Azure ML models do all the math on more than billions of possible combinations to show the most relevant items. The most significant benefit here is to help customer place the order quickly to make sure s/he is not forgetting something most people buy together with this item. 

Item-to-item recommendations are provided to the customer on the item landing page in the portal. When user clicks on an item to go to the item landing page, a real-time call is again made to Azure ML. The service returns other recommended items. These items are predictions based on what other users buy when they buy this item. Azure ML service not only recommends relevant items but makes sure that there is some novelty and diversity in its recommendations - nudging & subtly urging the customer to click. This helps in exposing the darker parts of the catalogue which the user may not be aware of. This not only can lead to upsells but also helps in bringing back the concept of guided-selling which Contoso lost to some extent when they moved half of their sales from call-center to the portal.

User-to-Item recommendations are provided to the customer just before she checks out both in the call-center and on the portal. Just before check-out another Azure ML call is made in real-time. This time user is recommended items based on the total basket of items user already has. Azure ML service here does what is referred to as "training on the fly", real-time personalization. This is unique to Azure ML.

CUSTOMER CHURN ANALYSIS

Contoso analyses monthly data to predict which customers are likely to churn in the current month. Sales department uses this information to call customers-at-risk and take necessary action.  

DISCUSSION

As you can see the sales order business process has been surrounded by a number of machine learning services at critical decision points - what product is this customer likely missing in the cart?, what product is this customer likely to buy today? What product is this customer likely to want if revealed to the customer? Should I offer this promotion to all my customers or to the ones who are likely to churn or to the ones who are rock solid customers? Customer wants a delivery slot that is reserved, should I offer that slot to him given that he is a  diamond customer for three straight months? This type of decision management capability is what transforms sales order process from merely being an automated process to an intelligent process. With the simplicity and affordability of Azure ML Studio and services in Azure marketplace Contoso can chose to stay engaged and continuously develop newer models to answer deeper questions about their business. For instance, say a particular product is recommended several times but gets rarely clicked by users, is it time to discontinue this product and save all costs related to the product. Or if certain recommended products consistently end up in the cart, is there some type of latent demand and perhaps more such products should be included in the catalogue? Or perhaps work in a different area for instance do text analytics on case history logs stored in Dynamics AX to identify customers who have complained often in the past or delivery drivers against whom most complaints have been registered or products against which most pre-orders have been placed.  

What has been achieved at Contoso with Azure ML and Dynamics working together cannot have been achieved if Contoso relied on Dynamics alone. For instance clickstream data being analysed to monitor consumer behaviour would not fit in a SQL database. The recommendations offered are predictions that need heavy computing capability and would be too expensive if were deployed on premise. The business changes quickly, new products, new markets, new customer requirements, new competitor offerings all require
Contoso to respond quickly - Flexibility and ease of modifying a model and redeploying a service using Azure ML studio makes this possible to do in few hours.

This is a case study of a successful Dynamics customer. We are closely monitoring Contoso and at the same time working simultaneously on a number of other projects. If you have a scenario you would like to discuss please reach out aksheyg@microsoft.com.

Like I said initially, the jury is still out. Success depends on identifying right scenarios, having the requisite skillsets etc. but clearly the opportunity here is endless - surround the business processes with various Azure ML services, perhaps at each decision point and transform your automated business processes into intelligent business processes. Manage your decisions consciously and not by accident.

You are welcome to join us for EMEA Convergence in Barcelona on Tue, Nov 4, 2014 to watch this in action at the keynote or later in afternoon session on Wed, 5th Nov 2014.

Contributors: Royi Ronen, Akshey Gupta

• 124 processes for Retail Industry
• 39 processes for Professional services library in following process groups

We will continue to add to these libraries in monthly releases.

Why are we doing this?

Earlier this year we conducted a survey to understand whether people like BPM tools, what they do with these and what more will they like from MS. We got 60 responses with overwhelming majority saying they love the tool, they will like us to ship more standard processes out of the box and that most people use these tools across various implementation project activities - analysis, design, test & training. The survey results are here.

Brief background on how we develop these will help you use them better, so here it is.

We have a cross-industry library that is based on APQC PCF. APQC has done work in the past to combine industry processes into a cross-industry process framework so we leverage this instead of building something new. APQC frameworks are widely used across industries in US & Europe. Then we have industry libraries for Manufacturing, Retail & professional services. Some of the processes here have already shipped while others will follow. These processes are inspired from APQC framework but most often they go deeper than four levels that APQC publishes. In industry libraries, as opposed to cross-industry library, we have chosen to depict AX processes in more detail than what APQC process will allow us to do, therefore think of these processes as extensions and variations to APQC PCFs.

APQC level definitions 

• A process category is a category containing related process groups.
• A process group is a group of related processes
• A process is something that converts inputs into outputs 
• A task is something that you do not want to decompose further. This is the lowest level. 
• An activity is something that lies between process and task. So, it's usually a combination on
  tasks.

Default BPM levels correspond to APQC levels. But you are free to add/modify or create completely new levels in your own libraries.

APQC PCFs exist for several industries and one for cross-industry. APQC collects industry wide data and generalizes processes and creates its industry libraries and assigns unique IDs to these processes. It then regularly conducts a review and finds processes common across industries and promotes these to cross-industry library and assigns a unique ID to it.

We did some work to identify following

• Unique process categories across all industries and for each industry
• Unique process groups across all industries and for each industry
• Unique processes across all industries and for each industry

If you would like to have this data and if your organization is an APQC member, send me the APQC member ID and I can send you this information.

Primary use cases

These are primary use cases for BPMs that we ship

• Presales: show off a working product to a prospect at very initial deal stage 
• POC: An AX consultant/team builds a POC  
• Analysis: An AX consultant or implementation team is exploring AX capabilities
• Design: Implementation team knows as-is processes and has chosen to build to-be processes in LCS/BPM
• Test: Create test scenarios as part of a business process flow or create scenarios to record how a certain capability should always perform despite changes during development
• Training: Keep a recorded library of to-be processes and use it to train users

If the BPMs we have published help you with any of the above, we think we have made a contribution. Ideally we would like the BPMs to be used to understand standard AX capabilities, take the guesswork out of what is possible & what is not in AX. We are aware that there are many variations and it is only in time that we will be able to show a decent subset of these out of the box. If you modify/extend the processes that we ship or decide to create your own processes that's even better. Modifying processes has been covered in other posts/documentation, it's basically a matter of choosing a control and dropping it in the diagram. If you want to create your own processes some of which use the ones that we publish you can copy these into your own model and add other processes in front or in back as separate nodes. In this manner you can think of what we provide as building blocks or pearls which you join to make a string of your own.

Some changes coming in the future will make working with these processes easier. We will implement tags on each node and capability to search through the tags and names of nodes. We will also put all processes into one big model so you can search instead of browsing as you do today. Once you find relevant nodes you can copy to your own model for further modifications.

Building your own libraries

If you decide to build your own libraries our experience has shown that it takes a bit of time to get used to the tools so please follow this iterative process

• Import your model using TR template into Task recorder
• See how your hierarchy looks in TR
• Do a couple of recordings
• See if you are happy with how auto generated artifacts turn out
• Make the BPM package
• Upload the BPM package
• See if you are satisfied with how it looks in BPM tool

Once you have done a few trials like above, only then do the full exercise

Build an excel template like the one shown below with your own defined levels

Then build some formulae to convert this excel file into TR template shown below

Import the template into TR and start recording

Note:

• Any nodes that you want to shift up or down or add new into hierarchy once hierarchy is in TR, only way to do that is manually inside TR which will take longer, so make sure you are pretty complete with your model before starting recording
• Any nodes that have been recorded inside TR cannot be moved up or down without deleting associated artifacts
• If you imported a .axbpm file into LCS, then modified an already recorded node in TR, and uploaded another .axbpm, no changes will be imported for this node into LCS
• If you modified a process flow in LCS BPM tool, and imported a new .axbpm, the import/upload will ignore the node you manually changed
• If you would like to add voice to your video, you can do this with third party tools, if you need more information please contact me

Known issues

• When you start Dynamics AX, make sure you right click on the exe and select “Run as Admin”, otherwise sometimes there are issues with process flow diagram
• Do the recording with Dynamics AX in EN-US only, foreign characters cause upload issues
• Recordings don't show non AX metadata driven forms (EP, WPF controls, mobile warehouse, etc.)
• More than 2 GB .axbpms cannot be uploaded, so do the uploads in much smaller chunks

If you are working on a customer project and will like to emulate as-is or to-be processes in LCS BPM, inquire if customer has a capability model or something similar. If they have, use it so you can speak customer language and so every "item" you create goes back into a customer capability, see my original post on this here. You can see how to upload custom business processes here.

A note on numbering of nodes

Prefixes

Numbers like 1.1, 3.2.1 etc. are prefixes that you see in node descriptions, these come from Task recorder templates when recordings (.axbpm) files are imported into LCS. The only purpose they serve is readability.

Suffixes

5 digit codes in the end on node descriptions are unique identifiers of the "item" on that node. APQC specifies these unique IDs. In cross-industry library all nodes are APQC identifiers. In industry libraries, since we have made extensions and variations to APQC processes, we have introduced our own unique identifiers for new "items" that don’t exist in APQC PCFs. If you create your own processes you could also choose to uniquely identify them, if you do so, remember these need to be on Task recorder.

Processes are recorded in Task recorder, download it here. You can find other LCS resources here.

Special thanks to many folks who have helped us come this far. Please keep the feedback coming.

Thanks.

We have several ideas on how to get you more content but will like your feedback.

Please take this 10-minute survey.

http://www.instant.ly/s/iyy25

Thanks.

First, start with a business capability model that customer has even if it is old and inaccurate. Only very large companies have done decent work on such a model to get you the detailed business processes you would need under each capability. Mostly you will find customer business capability models are very high level like manufacture and design and so on. Some will break this down further and will have schedule manufacturing and release design and so on. If you find yourself staring at a capability model like that, don’t be dissuaded, use one of the industry frameworks or capability models. I prefer to use APQC process classification frameworks or SCOR model. In this post, I refer to APQC.

Second, use APQC PCF for customer’s industry to augment customer’s capability model. Find relevant nodes on customer’s capability model and add to these various levels from PCF like for instance under manufacturing/schedule manufacturing, add further levels like schedule production and schedule and sequence jobs. Usually this activity will lead to healthy debate about terminology or lack thereof. For instance in this example, don’t be concerned about production being added as a subset to manufacturing when this should be other way round. Reason is that customer thinks their capability is manufacturing and not production, so be it. Using customer specific language is more important than using terminology from a standard framework – remember this is simple & fast approach, so take some shortcuts to get ahead, come back and iterate if you encounter too much resistance.

By this time you have four levels – process category (manufacturing), process group (schedule manufacturing), process (schedule production) and activity (schedule and sequence jobs). By doing this, you are breaking down capabilities in customers’ business capability model to individual business processes and activities. At this stage, if you find yourself going down the path of mapping each and every business process and activity in this model – stop yourself. Remember that this is part of an ERP implementation project where the objective is to address business challenges by implementing Dynamics AX. So, focus on the processes and activities that are more likely to address the business challenge and leave the rest out.

Hopefully you have been capturing the unstructured information that you have been collecting in your interactions with business managers and users. If you want you can use an O365 account and store all such data in a sharepoint repository.

Third, now that you have four levels of your model already defined, add a fifth level called scenarios – for instance under activity - schedule and sequence jobs, use that unstructured information or customer provided requirements list and add scenarios like describe the production route to schedule and sequence jobs, run operations scheduling, run jobs scheduling. Strictly speaking these don’t always end up like the ideal scenarios that have a persona and action taken by the persona. Sometimes they might and at other times they may be at a higher level. Granularity that you can achieve will depend on detail in the business process that you want captured in the model. Scenarios at this level should not go across departments, they should be small and within span of control of one manager. If you focus them keeping customer organization in mind, you will find that you get a very attentive audience and you can churn through this activity at a faster pace without getting impeded by departmental politics.

Fourth, add a further level to scenarios – call these user stories. These must be the requirements that you want to fulfil using Dynamics AX. User stories are very targeted at one user, usually an action that one user takes in one flow through Dynamics AX like edit the resource loading for works order on Gantt chart and view the result.

Let’s recap what has been achieved by now. Instead of working of a requirements laundry list which itself has individual list items on varying levels of detail, you have (1) brought the requirements at same level (2) you have defined them in a way that they are targeted at individual users (3) you have defined them at a level at which you would normally configure Dynamics AX (4) placed the requirements in the right hierarchy of business processes they belong and as a result (5) you have created a model which explicitly states fulfilling requirement A will mean changing business process X which will mean improving business capability Z. You have identified specific users in customers’ organization who will champion a requirement to fulfilment. You can go further and add derived business value at the scenario level. Then you could explicitly state that completing this scenario will say, reduce manufacturing cost. For more quant oriented projects, if you can find, you can add an estimate of derived value (for instance, estimated cost saving) at scenario level and then add them up to show the total derived value.

And no, by no means you are doing this in MS Excel. You are doing this in Task Recorder that ships with Dynamics AX 2012.

Fifth, as you go through this process, you will identify gaps that cannot be filled by standard Dynamics AX 2012. To make sure that these are addressed add or modify scenarios and/or user stories to account for these gaps. Once the model is there in Task Recorder, get your team to start doing recordings. Use these recordings to show your prototype to the users. You should not aim to get the complete model in place at the first instance. Instead you should use the approach described briefly by Nalin in this post, keep iterating, and changing recordings until the user is satisfied. Then use the recording for training purposes later on in the project. Once scenarios and user stories are in usable shape, if a project plan already exist, place the user stories under relevant nodes in project plan or use these stories to create a project plan. If you use TFS for project management and if you use the Agile 6.0 template, it will look something like this.  

Sixth, add additional deliverables to user stories as required for instance add development and configuration tasks to each user story.

Seventh, add test cases to the project plan to ensure that user stories and associated deliverables do what they are meant to do.

Once you have the recordings in Task recorder, upload the .axbpm package file to business process modeller in LCS (Lifecycle services) for AX2012. BPM in LCS is a great tool for all your users to visualize the process and view the recordings. For instance in the picture below, there are four recordings for process group 3.4 and six recordings for process group 3.6. You can access other LCS resources on this page.

With such a structure in place, you are in control of the project. As deliverables complete, user stories will get completed. As user stories get completed, you can see which scenarios are getting fulfilled and find out what processes and being changed, what capabilities are being improved, what business challenges are being met and eventually what value is being added.

We have put together most of the capabilities tied to inventory batches on this one list page. In previous versions, this capability did not exist and users had to go through different places in the menus to perform different actions. This consumed extra time and clicks, was difficult to learn and operate.

Below you will find a quick summary of what inventory batch related information is available and what actions can be performed from this list page.

In the grid

All inventory batches will display in this grid. You can use standard filter capabilities to narrow down the list to specific products, sites, warehouses as you desire.

On the ribbon

• On Batch tracking button group, you can

1. Trace the batch
2. Change the batch attributes
3. Change the vendor batch details
4. Look up and change the shelf life information

• On Inspect button group, you can

1. Check the connected quality orders
2. Check the connected non conformances
3. See where and how the batch is blocked or create a blocking transaction
4. Change the disposition status to block/unblock the batch

• On View button group, you can

1. Check the multi dimension on-hand if containers have been setup
2. Check the consolidated on-hand if containerization has been setup
3. Check the intercompany on-hand if the batch participates in intercompany transaction
4. Check all inventory transactions related to a particular batch

In the fact boxes

There are five fact boxes

1. In the first box, you can quickly see totals of on-hand inventory data that is not shown by default in the grid
2. In the second box, you can quickly see totals of on-hand inventory data for a catch weight product
3. In the third box, you can quickly see totals of physical inventory data that is not shown by default in the grid
4. In the fourth box, you can quickly see totals of cost of the batch
5. In the third box, you can quickly see totals of physical inventory data of the catch weight product

Availability of this data in fact boxes saves the user from opening up multiple forms to preview this information. The calculation results shown in the fact box are dynamic - so these are summarized or detailed depending on the inventory dimensions chosen on the list page.

In the preview pane

Here again main goal for us has been to save the user from opening multiple forms. So as user browses through inventory batch records, disposition history, shelf life information and vendor batch detail information is made easily available for preview in this pane.

If you have suggestions to further improve this capability, please email me.

So for the non-linear consumption of malt, you will create two lines in the formula for malt product. On one line you will setup linear consumption, 5 kg for 12 litre, 10 kg for 24 litres and so on. On the second line you can change the formula to "STEP" on the setup tab. This will make the step consumption grid available. Here you can specify that an extra 0.1 kg of malt is needed when finished beer quantity is between 12 and 24 litres and an extra 0.15 kg of malt is needed when finished beer quantity is more than 24 litres and so on. So, this gives you flexibility to setup non-linear consumption in the same formula.

During production estimation system will look at the finished quantity of beer you want to manufacture and will automatically calculate the correct quantity of malt needed. In case of carbon example above, it can be achieved by using just one line on the formula lines for carbon where you can setup step consumption of carbon of 1 kg up to 5 KL and then of 2 kg between 5 - 8 KL and so on.

Hopefully this will be useful for your scenarios. In case you have suggestions for improvement please email me.