MUFI Rating & Risk – JDA Demand Management
MUFI: Maintainability, Usability, Functionality, Implement ability
Vendor: JDA (Select For Vendor Profile)
JDA owns some of the most previously prominent supply chain planning companies in Manugistics and i2 Technologies. Both were such essential leaders in the supply chain planning space that it is a fantastic turn of events that both companies were united – and at low purchase priced – under JDA. This changed JDA as their heritage is in retail software. At some point, they decided to switch to being a software conglomerate. The purchase of two software vendors with identical footprints (although strong in different areas) always seemed to indicate that JDA was following the Computer Associates/now Oracle model of buying software for its customers and their support contracts. Experience in analyzing JDA supports the fact that this is the company’s strategy.
JDA Demand Management (DM), their forecasting application was acquired from the Manugistics acquisition. Manugistics forecasting application was for years one of the best applications that Manugistics had. When companies that switched way from Manugistics demand planning to trendier applications like SAP DP end up wishing they had not. Therefore, even though it has not been developed much by JDA since the acquisition, it is still a competitive application.
JDA has an excellent classification tool that tells the user what percentage of the products would optimally go out on which type of model.
In JDA’s DM Classification Manager, it is clear that with this data, the majority of the product database goes out on the continuous non-seasonal method. What is shown in the graphic above are not models because within each technique are multiple models which contain specific configuration details? For instance, a moving average is a method; however, a three-period moving average is a particular model.
Above, the time weighting factor can be seen. Time weighing can reduce the influence of older demand history. While this is a very convenient way of essentially performing historical removal, one still has to determine whether and how much history should be removed. Once known, this setting in JDA DM can help implement the historical removal or historical time weighing.
Lifecycle Planning in JDA Demand Management
Lifecycle planning is managed very individually concerning dates in JDA Demand Management. The dates include:
- History Start Date: The earliest demand history date that the system uses in producing a forecast.
- Demand Post Date: Date up to which demand has been posted (DFU: DmdPostDate). The period into which the demand post date falls is the first forecast period. (see footnote for explanation)
- Effective Date: The date when the system begins producing forecasts
- Discontinue Date: The date after which the systems stops producing forecasts
The user can specify all of these dates very quickly in the JDA interface. These dates, shown in the screenshot below, are then applied at the product level.
Using these dates, JDA Demand Management can easily control the essential lifecycle dates for a product. The dates can be applied to the user interface by a planner or can be adjusted for a large number of products by performing a upload, with the dates adjusted as desired, to the JDA Demand Management application database.
Copying Demand History Between Like Products
One way of performing lifecycle planning is by copying one product’s demand history over to another product. This is particularly useful during a new product introduction, where a company believes there to be a high degree of similarity between the new product and an existing product. Copying demand is an alternative to providing a product with uplift commensurate with how another product performed. While a company can use a variety of approaches to predict the demand of a new product, one cannot do much better than to find a like product that has already been introduced and has a demand history.
JDA Demand Management provides a comfortable and detailed way to copy the demand history as well as other factors such as the tuning parameters, forecasting method, etc.
The ability to mix and match different features of a product in the forecasting system, as seen above, makes JDA’s functionality quite flexible. Notice also that the demand history can be copied for specific portions of the overall demand history. Secondly, there is a great deal of flexibility regarding what can be reproduced as the lower settings demonstrate. I consider this type of flexibility concerning lifecycle planning a best practice design.
JDA DM is a good choice for companies that are looking for very detailed control over the application of changes to the forecast and demanding history. Although little developed since the JDA acquisition it is still a competitive application. DM has been a leading forecasting application since before the JDA acquisition. It has a very good word of mouth among people that have used it, and many of its approaches have stood the test of time. Some of the ways that DM works are unique in the marketplace and many of the features do not become apparent until one has extended use. DM often seems to have precisely the type of functionality that many experience forecasting people would have put into an application if they had a chance to develop their forecasting application. And an indication that DM was designed by individuals with a great deal of practical forecasting experience.
All scores out of a possible 10.
Vendor and Application Risk
JDA DM has a long history of implementations behind it. The application is dated, but its original design was so good that it continues to be a good choice. The main risk issue with JDA DM is related to getting users to access the deep functionality of the application. DM provides precise control over manual changes, substitution, and many other functions. The functionality is robust, but there is a lot of it, and this means the application has a relatively high training load.
Likelihood of Implementation Success
This accounts for both the application and the vendor-specific risk. In our formula, the total implementation risk is application + vendor + buyer risk. The buyer specific risk could increase or decrease this overall likelihood and adjust the values that you see below.
Finished With Your Analysis?
Brightwork Forecast Explorer for Error Calculation
Improving Your Forecast Error Management
Did you know that most companies don’t know what their forecast error is? If a company knows an error percentage but not the interval or the aggregation level measured, that means they don’t know. Most forecasting applications make getting a weighted forecast error extremely difficult. That is why we developed a SaaS application that allows anyone to find out their forecast error with a simple file upload.
The Brightwork Forecast Explorer is free to use in the beginning. See by clicking the image below:
Software Selection Book
Enterprise Software Selection: How to Pinpoint the Perfect Software Solution Using Multiple Sources of Information
What the Book Covers
Essential reading for success in your next software selection and implementation.
Software selection is the most important task in a software implementation project, as it is your best (if not only) opportunity to make sure that the right software—the software that matches the business requirements—is being implemented. Choosing the software that is the best fit clears the way for a successful implementation, yet software selection is often fraught with issues and many companies do not end up with the best software for their needs. However, the process can be greatly simplified by addressing the information sources that influence software selection. This book can be used for any enterprise software selection, including ERP software selection.
This book is a how-to guide for improving the software selection process and is formulated around the idea that—much like purchasing decisions for consumer products—the end user and those with the domain expertise must be included. In addition to providing hints for refining the software selection process, this book delves into the often-overlooked topic of how consulting and IT analyst firms influence the purchasing decision, and gives the reader an insider’s understanding of the enterprise software market.
By reading this book you will:
- Learn how to apply a scientific approach to the software selection process.
- Interpret vendor-supplied information to your best advantage. This is generally left out of books on software selection. However, consulting companies and IT analysts like Gartner have very specific biases. Gartner is paid directly by software vendors — a fact they make every attempt not to disclose while consulting companies only recommend software for vendors that give them the consulting business. Consulting companies all have an enormous financial bias that prevents them from offering honest advice — and this is part of their business model.
- Understand what motivates a software vendor.
- Learn how the institutional structure and biases of consulting firms affect the advice they give you, and understand how to properly interpret information from consulting companies.
- Make vendor demos work to your benefit.
- Know the right questions to ask on topics such as integration with existing software, cloud versus on-premise vendors, and client references.
- Differentiate what is important to know about software for improved “implement-ability” versus what the vendor thinks is important for improved “sell-ability.”
- Better manage your software selection projects to ensure smoother implementations.
- Chapter 1: Introduction to Software Selection
- Chapter 2: Understanding the Enterprise Software Market
- Chapter 3: Software Sell-ability versus Implement-ability
- Chapter 4: How to Use Consulting Advice on Software Selection
- Chapter 5: How to Use the Reports of Analyst Firms Like Gartner
- Chapter 6: How to Use Information Provided by Vendors
- Chapter 7: How to Manage the Software Selection Process
Demand Post Date refers to the last date that demand was posted to the system. It separates out the “history” from the “forecast” and serves as the basis for defining the first forecast period. For example, if I am forecasting in weekly buckets on a Sunday start, and my demand post date is set to Feb 5, 2012, this date represents that the last posted history was on Feb. 5, 2012, encapsulating the sales from the prior week. Therefore, the first bucket of the future forecast is for the week of February 5, 2012. – Paula Natoli, JDA