Tag Archives: Master Data
Protected: SPP Capture Historical Demand Setup
Resource Variability Capabilities in SCM
Managing Resource Variability
When the topic of resources comes up on projects invariably the question arises how the resource can be made flexible to account for the constantly changing factors in the supply chain.
One way of dealing with constraints that are constantly changing is with the use of capacity variants. Variants allow SCM to modify resource capacity by time of day, by week, or by month. This can help in making the resources “flex” through time to match the reality of the supply chain. Different capacity variants can be used for different planning runs. A second method is using alternate production process models (PPMs) which means using different capacity constraints. Jorg Dickensbach has the following to say on this topic.
In addition to alternative PPMs and PDSs, SCM has the ability to use alternative resources. This allows the system to select from say one machine that is more expensive, but faster vs. a machine which is less expensive but slower. Depending upon the circumstance, either machine can be selected depending upon how it fits the need. - Jorg Dickensbach
Willingness to Maintain the System Master Data?
A second issue which is not very often discussed is how capable companies are in maintaining these constraints. The answer is not very. Most companies I have consulted with basically want the system to maintain itself, and the trend towards outsourcing system maintenance in faraway locations where the individuals are purely technical, and lack business knowledge is only exacerbating the situation. The best systems are those that are self adaptive. I have a video from the CEO Joseph Shamir on this topic from the SCM Focus Inventory Optimization and Multi Echelon site in which I think he expresses very well that clients want in terms of maintainability.
Title: Joseph Shamir of ToolsGroup on What Clients Are Looking For
So we learned from Joseph that demand shaping is one thing that clients are asking for. Also that there is a recognition that many supply chain planning systems end up being difficult to maintain. As a personal comment I think Joseph is absolutely right, and I hold the consulting firms as accountable as the the vendors as many solutions are designed without consideration to whether they will be truly sustainable. Its good to see a vendor with an eye on this issue.
Unfortunately SAP APO/SCM is not there yet. SCM requires a high degree of maintenance in all of its master data.
MDM to the Rescue? (Not Really)
One might ask the question if whether the expense of implementations is actually sucking money away from the maintenance of systems. This could be the topic of a separate article by itself. So while Master Data Management MDM software is selling very well and MDM consultants are doing great, master data is not improving much if at all, (repeated use of the terms “governance” and “taxonomy” do not seem to be magically improving the master data) and the vast majority of MDM projects are simply consulting boondoggles. MDM is known among partners as an excellent way to extract large amounts of money from clients.
See this article on this topic if you are interested in details as to why.
Secondly, one topic is not discussed with regards to master data maintenance is in many ways getting worse due to outsourcing. Outsourcing of IT is essentially a way to get short term costs reduced, while reducing IT capabilities even more than the cost go down. This means less support for everything and system maintenance is one of them. Its automatic, as soon as outsourcing occurs, the business starts having to perform their own system maintenance. Master data is of course part of this.
Other Methods
Maintenance in SCM can be managed by maintaining Setup Groups and the Setup Matrix in an Excel file and using it for an upload of the Setup Groups to SAP ERP with a CATT via LSMW. Step two is to transfer the Setup Groups to APO with a report, and the third step is to load the Setup Matrix from Excel to APO using the APO loader.
References
http://www.lib.umd.edu/drum/bitstream/1903/7488/4/25813_cov.pdf
Supply Chain Management with APO, Jorg T Dickensbach
http://planettogether.com/capacity-planning.php
Diagnosing DP Statistical Forecasting Problems
Background
It is not unusual for companies to be unsatisfied with their forecasting performance. However, what can be particularly troubling is when even after an expensive SAP DP implementation the client is still unhappy with their forecasts. No doubt, SAP and consulting companies would prefer this not to be known or discussed, however, I have noticed some disturbing signs with DP, that clients who intend to implement DP should be cognizant of.
Data Workbench
I have written on several occasions about my criticisms of the Data Workbench.The Data Workbench is so difficult to use that it serves to limit data flexibility, and serves as a long term cash cow for consultants in both DP and BW.
http://www.scmfocus.com/sapplanning/2009/05/09/the-worst-functionality-in-scm/
http://www.scmfocus.com/sapplanning/2009/11/02/advantages-and-disadvantages-of-dp/
Problems With Statistical Forecasting
However, less discussed is the problem DP has with statistical forecasting. There are a host of problems that really should not exist at this late stage, and should probably result in a shake up at SAP development. Problems extend from the inability of DP to perform best fit forecasting to issues with forecasting some of the basic statistical forecasting methods. One client considers their system so fragile that they only feel comfortable using a single forecasting algorithm, even though their product database is much more varied needs than that.
Case Study: Testing The Client’s Dataset
Before getting into DP, I wanted to run my client’s data through a best of breed forecasting application. I searched for several applications online that were modified Excel Plug-ins, and found myself very disappointed. For any prospective developers out there, there is an unaddressed market for inexpensive forecasting testing software, but also for forecasting interfaces generally. I wrote about the problems with forecasting interface design a while ago.
http://www.scmfocus.com/demandplanning/2010/02/why-are-forecasting-interfaces-so-hard-to-design/
It continues to be a perplexing. When one thinks about how much innovation and creativity goes into the development of video games, the state of forecasting interfaces is the dismalest.
Prototype Environment
Every implementation needs this in order to be able to test the results from the selected application against something else and something more transparent. Myself, I at one time thought that it was sufficient to listen to vendors and check screen shots to match functionality during software selection. However, my experience with troubleshooting DP has lead me to change this view and to think that the forecasting within DP or SAS or any other application should first be tested on client data, by the client, in order to see if the forecast algorithms actually work as advertised.
Getting back to the applications, I had very little success with the Excel plug-in vendors. I looked at Lokad, EZForecasting among others, but did not find what I needed. Furthermore, I found the Excel plug-ins to be a bitawkwardcompared to the dedicated forecasting user interface systems. One vendor which has a nice trial is SPSS. Although I was hesitant to use it, because they were recently purchased by IBM (when a software company is purchased by Big Evil, the software stagnates, the price charged goes up for the software and the service, developers become frustrated with thebureaucracy, and the service goes down.) Finally the software begins turning up in places it should not be because it is pushed there by IBM’s corrupt software recommendation service. However, even though SPSS’s long term prospects are bleak, I still though it was useful to use SPSS to at least compare forecasts externally to what was being produced by DP. I also wanted to test drive SAS’s forecasting application, however, I was not able to find a downloadable trial of the SAS software.
Using SPSS
SPSS has a decent interface which allows the fast accessibility to many different forecast methodologies. However, I was not able to find a distinctly seasonal method,it was necessary to build it. As for best fit functionality, the links on the SPSS website related to this are broken, and their help within the application did not lead me to the answer. So I had to search the web where there are several papers. Overall my trial with SPSS did not leave me with the idea that it would provide me with a fast forecast prototyping environment.
Making My Own
Eventually I was very close to making my own forecast best fit formula in Excel. It picks from a seasonal, seasonal trend, Croston’s method. When the error gets sufficiently high, no forecast method is used and this is where an instead the periodic order up to formula is substituted over the the forecast. I essence this would be like turning off demand management in SAP DP and ERP, and only activating the periodic order up to logic in the material master.
These are simply consumptive planning items. It is important to segment the product database into those that can be and are worthwhile forecasting, and those that cannot beforetasted. The large implementation partner that my client used put everything in APO and put everything towards a forecasting model. It is interesting to arrive on accounts with so much money spent, and with so little thought put into the design.
In Steps Demand Works Smoothie
However, then I found DemandWorks Smoothie. I ran into Bill Tonetti, who gave me a great over the phone presentation of what DemandWorks Smoothie could do. As soon as I found out it could perform an auto select or best fit, export the data for each line item with the coding for each forecast model selected, and that it had the ability to compare a moving average against theforecastmodel selected to arrive at an R squared, I knew I had to recommend to my client to purchase this application. After using the applications for months I have become extremely impressed with it, and find that it easily exceeds SAP DP in most areas. The pricing for Smoothie allows it to be used as a pure simulation environment even if the company is using a different production system. The pricing on Demand Works can be found here.
http://www.demandworks.com/try-or-buy/smoothie-pricing.html
After a few days they approved the purchase of the introductory version, and I began using it to prototype the forecasting that we eventually want to move into production, with or without DP.
I discuss the use of DemandWorks Smoothie at these posts below.
http://www.scmfocus.com/demandplanning/tag/demand-works-smoothie/
Protected: SPP IMG Setup: DRP
SPP IMG Setup: Deployment
Introduction
This post will describe the different settings for deployment. All of these settings are underneath the Deployment folder in the IMG under SPP. They are in the same sequence as is found in the IMG.
Define Service Profile for Deployment
The planning mode is probably the most important part of this configuration screen. Options include PULL, PULL, PULL_SIM, PUSH, PUSH_SIM, PUSH_SUPPL.
It is necessary to select a DRP service profile, which you can see more about here.
http://www.scmfocus.com/sapplanning/2010/09/05/spp-img-setup-drp/
You can also declare whether you want STOs confirmed, and whether EOQ should be used in determining TSL parameters. (TSL in SAP stands for Time Supply Limit – and is the maximum amount of supply that can be delivered to a location.
http://help.sap.com/saphelp_scm70/helpdata/en/47/9e11dca31e1596e10000000a42189d/content.htm
It is not the same as the Target Stocking Level, which is a term used by niche service parts planning software vendor called MCA Solutions.) You can read about that TSL here. This is one of the most important concepts in service parts planning.
http://www.scmfocus.com/servicepartsplanning/2009/05/18/target-stocking-level/
Make General Settings for Deployment
Very basic settings for the deployment related to things like minimum package size, storage time for log entries. However, does have whether orders should be scheduled both forwards and backwards (only for STOs first forwards, then backwards). SPP Prio Before OLD Prio determines “whether the system prioritizes open customer demands / backorders according to order due lists (ODLs) or according to Service Parts Planning (SPP) sales order prioritization during fair share distribution.” – SAP Help
The Pull Deployment: Only Locs with Triggers, registers demand at the parent location and sets a trigger. Setting this makes the system only plan goods with direct child locations of the parent locations that have registered demand and have set the trigger.
Here are some of the options with respect to Priority Tiers. The Sort Sequence is applied per Priority Tier.
Define Fixed Demands that Push Deployment Does Not Consider
Here you define which fixed demands you do not want to be considered during push deployment at the parent location. All that is entered in this configuration screen is a Reason Code.
Exclude Means of Transport from Express Shipment of Dangerous
“When the system makes an express shipment decision, it checks whether the product in question is dangerous goods. You can specify and see whether the product is dangerous goods in the product master data on the Stor. Data tab page in the Haz. Sub. Strg-Rel. field.” – SAP Help
This is also just an entry of a single means of transport.
Select Order Due Lists
“When dealing with open customer demand or backorders during fair share distribution in the deployment process, you can work using order due lists.In this IMG activity you select the order due lists that you want to work with in deployment.” – SAP Help
For more information, see the Implementation Guide (IMG) for Advanced Planning and Optimization under Global Available-to-Promise (Global ATP) -> Event-Driven Quantity Assignment (EDQA)-> Quantity Assignment to Order Due Lists (QODL) -> Configure Order Due Lists (Obtain Confirmation)
