#Supply Chain Management Software

[lifecarelogistic]

What New Features Should A Logistics Software Have?

The Right Platform for Your Organization

Most customer service providers has a single platform that they use for all their products. For example, if you’re an online fashion retailer, you might have your website, blog, mobile app, and social media platforms. If you’re a manufacturer, you may have your website, blog, and online store. However, there are advantages to each of these platforms individually, as well as in collaboration. These platforms allow you to create content, distribute news and updates, manage your blog, track your sales, and much more. Top Logistic Companies in India always prefer newly invented features and logistic software to use in a company that is profitable for progress and update.

Track and Trace

Traceability is a crucial aspect of any business’s operations. If someone were to hack into your systems, they would be able to see everything from where you build your products to the products you sell. If you don’t have a traceability strategy in place, it could make it harder for law enforcement and financial institutions to steal your data. For example, if a customer’s card was stolen at a store, the police and financial institutions would be Questioned by the thief.

Expertise

You can’t run a business without having expertise in some aspect. For example, you can’t call the shots in the financial planning department. You have to have an understanding of investment advice, taxes, insurance, and other financial products. However, when you have expertise in one area, it helps to place other aspects in a broader context. For example, your customer service team can provide expert advice in customer service, but if they were to handle every situation expertly, they would be holding back on other aspects of the company.

Add Value

Add Value is the key to any business’s success. When you have expertise in one area and a scalability problem leads to the need to add another, you have an opportunity to make a difference. For example, let’s say your company manufactures an app that helps people track their landmass. If someone were to steal your app and make a copy, they could easily take over your business. You have an opportunity to make a big difference by implementing traceability within your app.  

Conclusion

A logistics system should represent a single source of truth for all your stakeholders. This single source of truth enables you to offer a high level of assurance that your goods and services will reach their destination efficiently and effectively. Whether your organization is a start-up or a large enterprise, you need a strategy that encompasses the new capabilities that the blockchain promises.  

[valuechainplanning-aop-budget]

Value Chain Planning-The Business Plan or the annual Budget often follows the following outline: 

 Capacity and other bottlenecks 

Organizational Consensus 

Revisions to Spend and volumes 

Management buy-in 

Internal Factors. 

Development of a Sales and Marketing Plan 

Calendar Monthly forecast 

Strategic analysis of external factors: 

- Economic 

- Political 

- Competitive 

Operating Budget 

- Manufacturing Costs 

- Administrative costs 

- Sales and Marketing Overheads  

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[valuechainplanning-collabor-proc]

Value Chain Planning - Exception Management

Budget Shortfall Review- Depending on the pain points of the current organizational process, we design this meeting to reconcile top-down financial and marketing forecasts with the operational demand plan. The GAP identification and resolution is a major part of the Sales and Operations Planning Process

Exception Management- A well-defined process will thrive on exception management. All Component meetings will start with a follow-up of issues from the previous meeting and deal with exception issues highlighted by the meeting templates. A concise design of meeting templates will help you achieve brief, sharply focused, effective meetings

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[valuechainplanning-consulting-b]

Value Chain Planning -The Business Plan

Valtitude has developed a unique methodology to develop long-term forecasting and analyzing the forecast sensitivity.  

Often the annual Marketing Plan is the driver for the demand information in the annual budget. The Marketing Plan is developed with a volume forecast for the year along with the spend levels necessary to create and sustain the expected demand for the products. The process for Market share forecasting is explained here. Marketing-mix modeling is a key component of developing the Marketing Plan.  The Business Plan or the annual Budget often follows the following outline: 

 Capacity and other bottlenecks 

Organizational Consensus 

Revisions to Spend and volumes 

Management buy-in 

Internal Factors. 

Development of a Sales and Marketing Plan 

Calendar Monthly forecast 

Strategic analysis of external factors: 

- Economic 

- Political 

- Competitive 

Operating Budget 

- Manufacturing Costs 

- Administrative costs 

- Sales and Marketing Overheads  

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[demandplanning-servicemetrics]

Demand Planning -Incentive Alignment using Metrics

How to measure the Demand Chain?

Financial Forecast versus Demand Plan

How to measure the Supply Chain?

Alternative control metrics

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[valuechainplanning-forecast]

Value Chain Planning -Top-Down forecasting                              

In Top-Down forecasting, the forecasts are developed at the brand, category or division level, and then allocated down to the lower levels (to SKU, then to SKU/Warehouse).

There are different schools of thought, arguing that either method is superior. One approach says that forecasting at the detail level results in a much more accurate forecast because information at the detail level is more precise. Aggregating this detailed forecast with a time series of prices produces a realistic view of the financial plan.

The alternate view, which is equally plausible and fair, states that forecasting at the SKU level produces too much noise. At the SKU level, volumes could be very light; some of

the SKUs could be shipped very infrequently. So the lower the level, the more difficult it becomes to create any useful statistical model because of sketchy and intermittent demand data. If that is the case, forecasting at the SKU/customer level will magnify the impact of noise and data infrequency. Forecasting at the customer SKU level also means aggregating not only at the customer SKU to the SKU level, but also aggregating all the way to the top, to the division level.

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[valuechainplanning-sas]

Value Chain Planning -Converting Date formats in SAS

Converting Date formats in SAS

SAS is very helpful but can be complex as well. Because of the flexibility it offers, it has to be necessarily complex. The manuals from the Institute are not very comprehensive to cover the length and breadth of the complexity.

How to converts dates and times into the combination format date time? Extracting dates and times out of a date time format is easy. We can use the function date part() and time part() on the date time value and write them as specific formats in the output dataset.

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[valuechainplanning-certified-ana]

Value Chain Planning -Training Requirement

Training Requirement

A total of twenty hours of blended learning including (classroom and online e-learning modules) as follows:

Successful completion of the two-day workshop on Demand Planning and Sales Forecasting currently offered as an in-person workshop in various locations

Successful completion of four online modules.

The above two requirements qualify a candidate to sit for the final certification exam. The exam is four hours long and administered in two parts on the same day. Each part will count towards a 50% weight.

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[demandplanning-modeling-diag]

Demand Planning - Forecast Performance Metrics

Mean Absolute Percent Error

Forecast Bias

What do we learn from forecast errors?

Introduction to Exceptions Management

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[demandplanning-planningprocess]

Demand Planning -Modeling special cases of Demand Product Life Cycle & Long-term Planning 

Product Lifecycle and trend • Launch Forecasting • Volume effect on line extension

Event Modeling Baseline vs. Incremental • Illustration of Event Models

Planning for Intermittent Demand What is Intermittent Demand & what causes it? Strategies to handle intermittent demand & Statistical Models for Intermittent Demand

Higher-order Models

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[valuechainplanning-aop-budget]

Value Chain Planning-Long-term forecasting and analyzing the forecast sensitivity.  

Valtitude has developed a unique methodology to develop long-term forecasting and analyzing the forecast sensitivity.  

Often the annual Marketing Plan is the driver for the demand information in the annual budget. The Marketing Plan is developed with a volume forecast for the year along with the spend levels necessary to create and sustain the expected demand for the products.

The process for Market share forecasting is explained here. Marketing-mix modeling is a key component of developing the Marketing Plan. 

To know More, Visit Us:

[valuechainplanning-collabor-proc]

Value Chain Planning -Supply Collaboration Process- 

Once a consensus demand forecast is finalized, Supply planners will refresh their planning systems to arrive at their new schedule with constraints. The new demand may point to imbalances in their supply process including issues in raw materials, finished goods inventory, manufacturing schedule, and capacity constraints.

The collaboration process should consider these issues to problem solve and decide a set of supply constraints to be acted on in the Operations Review meeting.

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[valuechainplanning-consulting-b]

Value Chain Planning -Business Planning and Budgeting

Business planning is critical for every company. Often conducted as an annual exercise the business plan is the volume, cost and profit plan for the upcoming year. This is also referred to as the annual budgeting process.   

Some companies conduct this as part of the long-range forecasting process, which cover anywhere from three to seven years. The long-term forecasting is indispensable for companies with longer product development cycles. It provides the inputs for capacity planning and other long-term expansion initiatives.   

It is common knowledge that longer-term forecasts are more erroneous than short-term forecasts. There are more dynamic variables and error processes at play in determining the conditions expected to prevail in the future. So it is necessary to subject your forecast to a sensitivity analysis to understand the robustness of the forecast.  

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[demandplanning-servicemetrics]

Demand Planning -Inventory Metrics

Inventory Metrics

Inventory Turns – Productivity

Dead and Obsolete – The Slobs

Effects of Demand Volatility

Is MAPE a questionable measure?

Incentivizing Demand Planners

Identifying forecast Bias when there is persistent volatility

Demand Management

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[valuechainplanning-forecast]

Value Chain Planning -Bottom-up/Top-Down forecasting                              

Adding more excitement to this menu of terminology, different software tools have different methodologies to perform forecast reconciliation between the different levels of forecast. Typically, forecast reconciliation is sought after in cases where you need to forecast multiple levels of the product hierarchy, so in some sense it’s aligned with product grouping and product hierarchy. If you are also forecasting at the customer level, then the levels of aggregation multiply by the customer dimension. Customers may roll up into a sales territory, sales district, and sales region to the national level. In this article, we are going to limit ourselves to illustrating forecast aggregation and disaggregation just using the product hierarchy. We would abstract away from further disaggregation at the customer SKU level.

When you develop your forecasting process, you have a choice to make – at what level do you develop the forecast? You could forecast at SKU level or slightly higher (at brand or sub-brand level) or, given a simpler supply chain you could get away with forecasting at the category or division level.

When forecasting for the supply chain, as a general rule, you forecast at the SKU level. When you have a process focused on this type of detailed forecasting, you could aggregate an SKU level forecast to a higher level forecast, dollarize it, and use it for financial planning, which is the typical methodology for Bottom-up forecasting.

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[valuechainplanning-sas]

Value Chain Planning -Creating Weighted Statistics in SAS

Creating Weighted Statistics in SAS

I wanted to Proc means to calculate weighted statistics but the options seem too involved. In the specific example, you have hundred days that are zero and the remaining days have positive values where some of the zeros are not coded as zeros but simply the date is omitted. What is the average daily value?

If you computed an unweighted simple average, it will be misleading. It will overstate the mean since it will not weight the zeros. So the observed average needs to be multiplied by a proportion of number of non-zero days over total number of days. However the variance cannot be easily adjusted.

So in SAS, I created a row of zero and weighted it with 365-number of non zero rows. I created a weight of 1 for every non-zero row. If you use proc means and apply weighting option you are done. The mean is easy......... but the variance needs more work. You need to specify a variance deflator option to indicate you really want to use the total of weights to arrive at the variance.

This option is VARDEF=WEIGHT.

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